Cluster of Excellence on

Multimodal Computing and Interaction

Mission

The past three decades have brought dramatic changes in the way we live and work. This phenomenon is widely characterized as the advent of the Information Society. It is fueled by the power of information technology to acquire, store, process and transmit data compactly, inexpensively, and at greater speeds than ever before. Two decades ago most digital content was textual. Today, graphics and audiovisual I/O devices are in widespread use and modern devices have multimedia capabilities. As a result, current digital content additionally comprises speech, audio, video and graphics. Ubiquitous sensing devices further increase the global volume of digital data. The availability of digital content in different modalities and the increasingly pervasive access to the Internet combine to make a host of information available to anyone, at any time. A deluge of multimodal information is openly available across a surprising variety of Internet platforms. New devices such as smartphones, time-of-flight cameras, and motion sensors are abundant. Users can easily create rich and novel forms of multimodal content, and they can interact with virtual characters and other forms of augmented reality.

Given these trends, the Cluster of Excellence on Multimodal Computing and Interaction (MMCI) has addressed the challenge to organize, understand, and search multimodal information in a robust, efficient, intelligent and privacy preserving manner, and to create dependable systems that support natural and intuitive multi- modal interaction. We have successfully made major inroads towards the Deep Integration of Language and Knowledge, Augmented Reality, Multimodal Dialog with the Environment, and Information Privacy and Accountability, and our results provide a major step forward towards a unified understanding of multimodal computing and multimodal systems.

Overview

The Cluster of Excellence on Multimodal Computing and Interaction (MMCI) was established by the German Research Foundation (DFG) within the framework of the German Excellence Initiative in 2007 and successfully renewed in 2012.

MMCI originally comprised the Computer Science and (UdS-CS) and Language Science and Technology (UdS-LST) departments of Saarland University, the Max Planck Institute for Informatics (MPI-INF), the German Research Center for Artificial Intelligence (DFKI), and the Max Planck Institute for Software Systems (MPI-SWS). Together these institutions form what is now known as “Saarland Informatics Campus (SIC)”. In 2011, the Center for IT-Security, Privacy and Accountability (CISPA) that was established in 2011 as a national BMBF-funded competence center for IT security and Privacy at Saarland University and meanwhile became the CISPA Helmholtz Center for Information Security in 2018 was added as a cooperation partner.

During the reporting period we have seen significant growth of the research base on Saarland Informatics Campus on all levels. A particular emphasis of MMCI has been on the promotion of young researchers, and as such, we have committed the majority of allocated funds to our independent research group (IRG) program: We attracted a pool of highly talented young researchers to MMCI and successfully hired 43 independent research group leaders during the reporting period. Our IRG leaders have achieved outstanding results, and we have seen an unusual amount of collaboration within the Cluster. At the time of writing, one group is still ongoing, all other IRG leaders received offers for faculty positions following their stay with MMCI. Many former IRG leaders continue to maintain close ties to the Cluster, and a multitude of joint publications attest to the quality of this sustained collaboration.

In our research we have significantly advanced towards our overall goal, to organize, understand, search and interface the wealth of multimodal information in a robust, efficient and intelligent way, and to create dependable systems that support natural and intuitive multimodal interaction, and researchers affiliated with MMCI have left their mark in the international research community.

Partners

Participating institutions of the host university

  • Computer Science Department (UdS-CS), Saarbrücken
  • Department of Language Science and Technology (UdS-LST), Saarbrücken
  • Center of Bioinformatics (UdS-CBI), Saarbrücken

Participating non-university institutions

  • Max Planck Institute for Informatics (MPI-INF), Saarbrücken
  • German Research Center for Artificial Intelligence (DFKI), Saarbrücken
  • Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken

Most important cooperation partners

  • Intel Visual Computing Institute (IVCI), Saarbrücken
  • Globus Innovative Retail Laboratory (IRL), St. Wendel
  • CISPA Helmholtz Center for Information Security, Saarbrücken

Research Areas (RA)

RA 1 – Text and Speech Processing

Language is the most natural and expressive medium for human communication and interaction. The richness of language derives from its grounding in our knowledge of the world and the immediate linguistic and non-linguistic context. In Research Area 1, we focused on structurally informed models of distributional semantics, using unsupervised and minimally supervised approaches that combine linguistic knowledge with linguistic information. We substantially extended our work by going beyond intra-sentential context in two directions. On the one hand, we created text-level models of discourse relations and script knowledge, and applied these models to improve text-level comprehension. On the other hand, we have advanced cross-modal methods that deeply integrate language processing with both extra-linguistic knowledge and visual information, and lead to more naturalistic interaction with dialog systems and avatars. Furthermore, our methodological contributions regarding neurophysiological and pupillometric measures have enabled us to investigate situated human-computer interaction in greater detail. Our results have significantly contributed to improvements in several types of natural language processing tasks: the offline extraction of complex knowledge from text to feed knowledge bases; the online semantic interpretation (disambiguation and composition) required for deep text understanding, enrichment of text documents, and deep question answering; the understanding of pictures and visual scenes; and the integration of speech and vision modalities to enrich spoken-language understanding and generation technologies in virtual interactive environments.

RA 2 – Visual Computing

Visual Computing is a cross-disciplinary research area integrating and advancing computer graphics, computer vision and machine learning methods. Visual Computing includes in particular the areas of image analysis – such as image processing, computer vision, pattern recognition – and image synthesis – such as geometric modeling, computer graphics, scientific visualization. At the same time acquisition, transmission, and efficient representation of visual data also play a role. While textual information processing by computers is a well-established and successful area, the quality, speed and robustness of many current visual computing algorithms is still behind human capabilities and requires more research. We have made contributions towards a coherent and robust bottom-up framework for key problems in visual computing, and we have extended our focus on the integration of different, complementary approaches and modalities within visual computing. A particular focus of our work is understanding of visual information. Scientific challenges cover the entire pipeline from single-sensor processing, over spatial and temporal fusion to the complete description of large-scale sensor streams. At the same time we observe a tremendous increase in both the quantity as well as the diversity of visual sensors embedded in a wide variety of digital devices and environments as well as due to the increasing storage of sensor data – such as surveillance data, personal storage of visual data, or simply the Internet. While storing and indexing large amounts of visual data has made tremendous progress, understanding visual data still lacks far behind. Therefore our long-term goal is to make progress on how to process, structure, access, and truly understand visual data both for online use as well as for large-scale databases. Machine learning has been a core enabler for progress in the area of visual computing. As a result it becomes more and more important to understand both privacy implications of sharing and using visual data and security questions related to the use of machine learning techniques. Over the last few years we have investigated various aspects at the intersection of privacy and security on the one hand and visual computing and machine learning on the other hand. Also, the interaction between modern algorithms and modern multimedia hardware such as smartphones, multicore processors, GPUs, time-of-flight cameras, and Kinect cameras has become an important issue in our research. We have also contributed to computational photography, a rapidly emerging field where imaging hardware and algorithms from image processing and computer graphics are combined in a fruitful way. Last but not least, our visual computing approaches have gained robustness from incorporating adaptivity, prior knowledge, and concepts from machine learning.

RA 3 – Algorithmic Foundations

Multimodal computing and interaction require smart algorithmics. Ever larger data sets need to be processed and analyzed; networks of interacting and sometimes competing agents evolve dynamically and to very large sizes; the fact that more and more tasks are handled by computerized systems increases the importance of reliability and correctness; and NP-complete optimization problems arise in almost all research areas. RA3 provided foundations at all levels of algorithmic research: the development of new algorithms and data structures (foundational level), the investigation of algorithm engineering issues, experimental work, and the provision of reliable and efficient implementations through the software libraries LEDA and CGAL (technological level), and the incorporation of our results into mature software libraries (systems level). We continued our successful work on geometric computing, data organization, and certifying algorithms and put increased emphasis on machine learning, algorithmic game theory and analysis of networks, and algorithms for NP-hard problems. 

RA 4 – Security, Privacy, and Accountability

Over the course of the past two decades, IT security has been struggling to meet the increasing demands posed by the advent of the information society. Security, accountability and the protection of privacy were not among the objectives of the original Internet design. Although successful steps on these key properties have been taken gradually, new requirements arise because of several ongoing trends: Large parts of industrialized nations’ critical infrastructure (e.g. energy, water, communications, transport, and financial networks) rely on information technology and have thus become potential targets for cyber espionage and sabotage. Internet users reveal, knowingly or unknowingly, more and more personal data; the collection and analysis of these data pose a substantial threat to privacy, the extent of which is difficult for lay users to grasp. In addition, the global and open character of the digital world makes it difficult to enforce existing national laws, rules and regulations, or to hold individuals and organizations accountable for unlawful behavior. Within this RA, we have been developing methods, technologies and prototypical systems along three main areas: we have devised foundational methods and tools for analyzing and designing reliable, secure systems; we have devised novel techniques for analyzing and controlling privacy in the digital world; and we have investigated novel techniques for analyzing and overcoming the inherent tension between private, but also accountable, interaction.

RA 5 – Knowledge Management

The vision and mission of RA5 is the automatic construction of comprehensive knowledge bases from web sources, text, and social media, and harnessing this digital knowledge for deep language understanding and other kinds of intelligent computer behavior. A computer with a formal knowledge representation of the full contents of Wikipedia could give semantically precise answers to advanced questions and could potentially even pass a high-school-level exam. In other words, such a machine would be close to passing the Turing test. Our team has been a trendsetter on this ambitious direction since the conception of the excellence cluster back in 2005. The YAGO knowledge base has become the most visible highlight. Later, the theme of automatic knowledge harvesting was also adopted by commercial players and led to projects like the Google Knowledge Graph, Microsoft Satori, and many more. We have also worked towards acquiring commonsense knowledge, and have explored, jointly with RA2, how to leverage this asset in computer vision tasks. Knowledge bases serve as a source of distant supervision and constraints for natural language understanding. In close collaboration with RA1, we have pursued this theme for a number of tasks, most notably for the disambiguation of names and phrases that denote entities, relations or general concepts, and also for deep question answering. Entity discovery has also inspired new approaches to information retrieval, with advanced functionality for semantic search and exploration. Storing, indexing and querying these kinds of very large knowledge bases, and interlinking them with entity-enriched text and data collections, also calls for novel approaches to scalable data management on distributed platforms. Finally, data mining and machine learning are vital assets for acquiring knowledge as well as for making sense of new data and text sources. RA5 has developed a variety of novel techniques for analyzing patterns, summarizing data, and deriving insight through data analysis.

RA 6 – Information Processing in the Life Sciences

The data-driven life sciences have experienced tremendous growth over the past two decades. This field deals with complex high-dimensional data that are hard to interpret. The generation of such data has risen to high volumes that are hard to handle both statistically and algorithmically. Interpreting such data continues to pose fundamental challenges to the fields of computational biology and bioinformatics. In the Excellence Cluster we have addressed such challenges in the following respects. We have developed methods for preprocessing and curating large sets of (mostly sequence) data. We have developed software that helps interpret molecular data for the purpose of gaining biological insight. This includes both software that processes the data in a batch fashion and software that facilitates interactive navigation through and visualization of data. We have performed translational research towards reaping benefits from bioinformatical analyses in terms of disease diagnosis and therapy. With regard to the type of data, we are concentrating on sequence and structure data. With regard to application scenarios, we are concentrating on epigenomics, concerning basic research, and on infectious diseases and cancer, concerning disease-oriented research. Furthermore, we qualify young scientists in interdisciplinary research who have the potential of shaping the field in the future.

RA 7 – Large-Scale Virtual Environments

Interactive 3D graphics has become ubiquitous through the availability of high-performance hardware on essentially all computers, including mobile devices. However, the tools to make use of these ubiquitous 3D technologies are immature and target mostly specialists: Exchanging 3D models, particularly with embedded simulations or animations, is still difficult; there is little support for material models exchangeable between applications; realistic lighting computations are too slow; and there are no accepted standards for 3D user navigation and interaction. Overcoming many of these shortcomings has been the target of this RA and we have made exciting progress in this area. As planned, we focused on creating fundamental technology addressing the challenges of enabling fully interactive, distributed, collaborative, large-scale, and visually rich virtual 3D environments. Specifically, we created an entire ecosystem of technologies around interactive Web-based 3D graphics, novel lighting simulation algorithms, new compiler technology for easily formulating high-level algorithms that beat even hand-optimized implementations across different hardware architectures, and several other technologies.

 

RA 8 – Synthetic Virtual Characters

Our long-term vision is to build virtual characters that look and behave like human beings, show emotions, and mimic the behavior of real people in an individual and character-specific way. Our virtual characters will be able to engage in a multimodal interaction with individual human users, a group of human users, or even among themselves for a limited task-oriented domain. The virtual characters can behave like celebrities (e.g., film stars, politicians, or talk show hosts) or imitate everyday people (you can create your personal virtual representative) on the basis of exchangeable persona modules. These modules contain empirically derived, mathematical models of the original person’s appearance and behavior (including gesture, posture changes, and head movement) which can be created by novice users by selecting suitable video clips of the human originals and using intuitive tools to extract the key factors that make up the person’s behavioral shell. The user can store, visualize, tweak, and merge persona modules to achieve perfect imitation or create new personalities using a number of pre-defined style dimensions. The persona modeling toolkit performs automatic video and speech analysis, exploiting computer vision and speech technology, machine learning and classification techniques in a human-in-the-loop editing environment. Highly accurate performance capture data can be imported and is fully compatible with these models. Mechanisms for high- level control are available to fulfill the complex needs of real-time behavior control for virtual characters, from walking through a door to changing the current topic of the dialog.

RA 9 – Multimodal Dialog Systems

Most current dialogue systems in research mainly cover scenarios that support multimodality as a combination of two modalities. A dialogue management system for a cyber-physical environment must be able to deal with massively multimodal interactions trying to concurrently address all human senses in heavily instrumented environments. On the one hand, this includes the free choice of modality, which means that any interaction should be, if possible, realizable by every modality available based on the preferences of the user. On the other hand, clearly more than two modalities should be integrated into a multimodal system that can also be used in combination. Massive multimodality also means that many heterogeneous devices of the same modality are used together in one application. This could be several microphones that collect speech input commands from a number of users. The main research strategy involved has been focused on moving from dual modality interaction paradigms (such as speech and gesture) as prevalent in the early years of the century to approaches that allow massive multimodal interactions. At the same time, we have moved from solutions that are adapted to a certain scale and a domain, to scale-independent multimodality across domains. Lastly, we have included important contextual aspects of a given domain into new multimodal interaction concepts. We have made use of computational models and developed demonstrators and prototypes to pursue our research roadmap. These engineering methods have been complemented with empirical research, including user studies in the lab and in the field to verify our assumptions on the suitability of massive multimodal interaction in several domains with different properties. In our research, a prototype of a massively multimodal dialogue platform called SiAM-dp was created with the aforementioned capabilities in mind. Numerous demonstrators were built for the following demonstration scenarios: smart homes, retail environments, smart factories, cars, production and car repair garages. Those were demonstrated at international fairs and conferences and have won several prizes.

Software Integration Platform

Our software integration platform and our open-source data collections and software, such as the YAGO knowledge base, the Cityscapes Dataset for visual understanding of urban traffic scenes, the GVVPerf- CapEva repository of human shape and performance capture datasets, our light field archive, the GIVE (Generating Instructions in Virtual Environments) challenge for evaluating natural language generation systems, the geno2pheno service for the analysis of HIV drug resistance, and the Geometry Algorithms Library CGAL, make the results of our research accessible to a broader audience and are widely used.

Principal Investigators

The 18 Principal Investigators, renowned, internationally acclaimed scientists from the fields of Computer Science, Computational Linguistics and Phonetics at Saarland University, the Max Planck Institute for Informatics, the German Research Center for Artificial Intelligence as well as the newly founded Max Planck Institute for Software Systems, were able to demonstrate their team spirit in jointly preparing the applications for funding.

Hans-Peter Seidel

Speaker
MPI-INF & UdS-CS

Manfred Pinkal

Vice-Speaker
UdS-LST

Michael Backes

Vice-Speaker
CISPA & UdS-CS

Matthew Crocker

UdS-LST

Peter Druschel

MPI-SWS & UdS-CS

Anja Feldmann

MPI-INF & UdS-CS

Matthias Hein

UdS-CS & UdS-MA, now: Prof., Tübingen U, DE

Antonio Krüger

UdS-CS & DFKI

Kurt Mehlhorn

MPI-INF & UdS-CS

Bernt Schiele

MPI-INF & UdS-CS

Raimund Seidel

UdS-CS

Philipp Slusallek

UdS-CS & DFKI

Elke Teich

UdS-LST

Hans Uszkoreit

UdS-LST & DFKI

Wofgang Wahlster

UdS-CS & DFKI

Joachim Weickert

UdS-CS & UdS-MA

Gerhard Weikum

MPI-INF & UdS-CS

Achievements and Results

Our main achievements and results can be grouped along the following four research themes
Deep Integration of Language and Knowledge

Language is the most effective means people have to express and communicate knowledge, both in text documents such as news, essays, books, and scientific articles, and in direct interaction. Conversely, interpretation and production of language must consider contextual knowledge, spanning meaning information and factual knowledge associated with previous text or utterances, the contextual knowledge of author, audience, or dialog participants, and the socio-cultural context of the discourse. Our research has unified and deeply integrated models of language and knowledge, with new representations that combine logical and statistical semantics over rich and structured feature spaces. We have also considered the visual contexts in which language appears and for which knowledge provides semantic background, contributing to enhanced knowledge acquisition from images and videos in news or social media. The developed integrated models enable much deeper disambiguation and understanding of language, robust detection of named entities and semantic relationships, and the analysis of negation, modalities, and temporal structures in discourses. They also provide a greatly enhanced basis for deep question answering and dialog systems. In turn, these deeper models also boost the ability to acquire new knowledge from text, speech, and combinations with visual contexts.

Augmented Reality

Mixed and Augmented Reality (AR) aims for completely immersive virtual environments with sophisticated scene representations and highest visual quality, fused seamlessly with the real world, along with the ability to interact with the environment in an intuitive and natural way. AR has been a buzz word for some time, but its tremendous potential remains uncontested. Using our combined expertise in both computer vision and computer graphics, we have successfully revisited image analysis and synthesis in an integrated fashion, by combining advanced reconstruction techniques from computer vision with the use of sophisticated scene and subject models from computer graphics and employing machine learning. Human-like synthetic characters provide a powerful systems interface, but can also be used to populate the environment. Our characters look and behave like human beings, show emotions, and mimic the behavior of real people in an individual and character-specific way. Our results on real time algorithms for both image analysis and image synthesis and the use of modern 3D Internet technology, are key to interaction and collaboration. Modern sensors and communication devices (smart phones, depth sensors, IMUs) provide novel interaction metaphors and feedback on the user’s location and actions. In combination with the high level scene representations mentioned above, this provides important visual context for speech understanding, language disambiguation, and natural dialog, backed by explicit knowledge bases as discussed above.

Multimodal Dialog with the Environment

Most current dialogue systems in research mainly cover scenarios that support multimodality as a combination of two modalities. A dialogue management system for a cyber-physical environment must be able to deal with massively multimodal interactions trying to concurrently address all human senses in heavily instrumented environments. On the one hand, this includes the free choice of modality. On the other hand, clearly more than two modalities should be integrated into a multimodal system that can also be used in combination. Massive multimodality also means that many homogeneous devices of the same modality are used together in one application. This could be several microphones that collect speech input commands from a number of users. We have made use of computational models and developed demonstrators and prototypes to move from dual modality interaction paradigms (such as speech and gesture) to massive multimodal interactions. These engineering methods have been complemented with empirical research, including user studies in the lab and in the field to verify our assumptions on the suitability of massive multimodal interaction in several domains with different properties. In our research, a prototype of a massively multimodal dialogue platform called SiAM-dp was created with the aforementioned capabilities in mind. Numerous demonstrators were built for the following demonstration scenarios: smart homes, retail environments, smart factories, cars, production and car repair garages. Those were demonstrated at international fairs and conferences and have won several prizes.

Information Privacy and Accountability

The recording, sharing and dissemination of multimedia objects by individuals using cameras and smartphones is now ubiquitous. As a result, vast numbers of images, videos and audio recordings are made in public places, and posted on public sharing sites or in online social networks. Today, it is impossible for an individual to keep track of all such recordings, much less control the publication or dissemination of material in which they (or their property) appear. Our research results help to trace the provenance of such information and to respect the privacy and property rights of users whose images, voice recordings, or physical and intellectual property appears in multimedia objects, and  to discover related multimedia content from different sources such that the applicable privacy laws and policies are respected, and those who retrieve the information can be held accountable for it. For instance, identifying recordings of the same event from different perspectives and with different modalities is important for accident investigations, criminal investigations, and research. We have developed techniques, protocols, and tools to support a repository for sharing and archiving multimedia data objects (images, video, text) in a way that ensures accountability and privacy. The repository employs sophisticated indexing and classification tools to automatically cross-reference related objects, independent of the objects’ source or time of publication.

Specific Results

The interdisciplinary and long-term nature of the MMCI Cluster has generated exciting results: Our research on knowledge harvesting has pioneered the automatic construction of large-scale knowledge bases from Internet sources. This work has provided the blueprint for industrial-strength knowledge graphs that are key assets for search engines, question answering, and text analytics (at Google, Microsoft, etc.). Our research on markerless capture of human pose and motion has pioneered methods for the reconstruction of detailed dynamic 3D models of humans in challenging settings. Our interdisciplinary work at the intersection of computer vision and computational linguistics has enabled automatic video description and visual grounding of semantic concepts, based on a translation approach to video narration and learned with minimal supervision. For information processing in the life sciences, a highlight has been the wide adoption of the geno2pheno service for the analysis of HIV drug resistance by the medical community. The service draws several thousand queries per month and has become a clinical standard for performing viral tropism testing in the context of AIDS therapy. We have developed vastly improved methods enabling any third party to verify the validity of arbitrary computations on authenticated data in a privacy-preserving manner. This lays the scientific groundwork for outsourcing computations with strong privacy guarantees. Our  SiAM-dp prototype platform enables massively multimodal dialog in several domains with different properties.

Software and open-source data collections

Our software integration platform and our open-source data collections and software, such as the YAGO knowledge base, the Cityscapes Dataset for visual understanding of urban traffic scenes, the GVVPerf-CapEva repository of human shape and performance capture datasets, our light field archive, the GIVE (Generating Instructions in Virtual Environments) challenge for evaluating natural language generation systems, the geno2pheno service for the analysis of HIV drug resistance, and the Geometry Algorithms Library CGAL, make the results of our research accessible to a broader audience and are widely used.

Other Measures of Success

We have published our research at the highest level. Our work has had significant impact, and our results have helped to shape the field of multimodal computing and interaction on an international scale. We have established a strong record of collaboration across different subfields, both quantitatively (493 joint publications across PIs and/or IRG leaders since the start of the Cluster), and qualitatively, e.g.: collaboration between computer vision and computational linguistics has enabled automatic video description and visual grounding of semantic concepts, based on a translation approach to video narration and learned from minimal supervision; and collaborative work between algorithms and human-computer interfaces has resulted in algorithmic design of computer interfaces that balance usefulness, user satisfaction, ease of use, and profitability. This high level of collaboration was significantly stimulated by the establishment of 43 Independent Research Groups.

Awards

Researchers in the Cluster received numerous prestigious grants and awards, both on the senior and early career levels. On the senior level this includes, e.g., one ERC Synergy Grant, five ERC Advanced Grants, a DFG Leibniz award, as well as several prestigious career awards. On the early career level this includes, e.g., 10 DFG Emmy Noether Grants, 21 ERC Starting Grants, and 6 ERC Consolidator Grants as well as several distinguished national awards. More than 230 PhD students did their PhD work in the Cluster, and more than 200 early career researchers of the Cluster moved on to faculty positions worldwide.

Former Independent Research Groups

A particular emphasis of MMCI has been on the promotion of young researchers, and as such, we have committed the majority of allocated funds to our independent research group (IRG) program: We attracted a pool of highly talented young researchers to MMCI and successfully hired 43 independent research group leaders during the reporting period. Our IRG leaders have achieved out- standing results, and we have seen an unusual amount of collaboration within the Cluster. Many former IRG leaders continue to maintain close ties to the Cluster, and a multitude of joint publications attest to the quality of this sustained collaboration.
  •  Mario Albrecht
    • Group: “Molecular Networks in Medical Bioinformatics”, 2008-2013
    • now: Senior Project Manager, Gesellschaft für Informatik e.V., Bonn, DE
  • Hannah Bast
    • Group: “Effcient Search and Indexing”, 2008-2009
    • now: Prof., University of Freiburg, DE
  •  Jan Baumbach
    • Group: “Computational System Biology”, 2010-2012
    • now: Prof., Technical University of Munich, DE
  •  Andrés Bruhn
    • Group: “Vision and Image Processing”, 2010-2011
    • now: Prof., University of Stuttgart, DE
  •  Andreas Bulling
    • Group: “Perceptual User Interfaces”, 2013-2018
    • now: Prof., University of Stuttgart, DE
  •  Giorgos Christodoulou
    • Group: “Algorithmic Game Theory”, 2010-2011
    • now: Senior Lecturer, University of Liverpool, UK
  •  Holger Dell
    • Group: “Foundations of Exact Algorithms”, 2014-2019
    • now: Prof., IT University of Copenhagen, DK
  •  Vera Demberg
    • Group: “Cognitive Models of Human Language Processing and their Application to Dialogue Systems”, 2010-2015
    • now: Prof., Saarland University, Saarbrücken, DE
  •  Piotr Didyk
    • Group: “Perception, Display and Frabrication”, 2014-2018
    • now: Assistant Prof., University della Svizzera Italiana; Lugano, CH
  • Elmar Eisemann
    • Group: “Real-Time Rendering and Representations”, 2008-2010
    • now: Prof., Delft University of Technology, NL
  •  Tobias Friedrich
    • Group: “Random Stuctures and Algorithms”, 2011-2012
    • now: Prof., Hasso Plattner Institute and University of Potsdam, DE
  • Jiong Guo
    • Group: “Efficiient Algorithms for Hard Problems”, 2009-2014
    • now: Prof., Shandong University, CN
  •  Alexis Heloir
    • Group: “Sign Language Synthesis and Interaction”, 2012-2017
    • now: Prof., Université Polytechnique Hauts-de-France, FR
  • Martin Hoefer
    • Group: “Dynamic Coordination in Networks”, 2012-2016
    • now: Prof., Goethe-University Frankfurt am Main, DE
  • Ivo Ihrke
    • Group: “GiAnA – Generalized Image Acquisition and Analysis”, 2010-2013
    • now: Staff Scientist, Carl Zeiss AG, Oberkochen, DE / INRIA, Bordeaux, FR
  • Aniket Kate
    • Group: “Cryptographic Systems”, 2012-2015
    • now: Assistant Prof., Purdue University, West Lafayette, US
  • Michael Kipp
    •  Group: “Embodied Agents”, 2008-2012
    • now: Prof., Hochschule Augsburg – University of Applied Sciences, DE
  • Alexander Koller
    • Group: “Efficient Algorithms in Computational Linguistics, 2008-2011
    • now: Prof., Saarland University, Saarbrücken, DE
  • Jens Krüger
    • Group: “Interactive Visualization and Data Analysis Group”, 2009-2013
    • now: Prof., University of Duisburg-Essen, DE
  • Hendrik Lensch
    • Group: “General Appearance Acquisition and Computational Photography“, 2007-2009
    • now: Prof., Tübingen University, DE
  • Matteo Maffei
    • Group: “Language-based Security”, 2008-2013
    • now: Prof., Technical University Wien, AT
  •   Alice McHardy
    • Group: “Computational Genomics and Epidemiology”, 2007-2010
    • now: Faculty, Helmholtz Centre for Infection Research, Braunschweig, DE
  • Sebastian Michel
    • Group for “Querying, Indexing, and Discovery in Dynamic Data”, 2009-2014
    • now: Prof., Technische University of Kaiserslautern, DE
  •  Meinard Müller
    • Group: “Multimedia Information Retrieval and Music Processing”, 2007-2012
    • now: Prof., Friedrich-Alexander-University, Erlangen-Nürnberg, DE
  •  Antti Oulasvirta
    • Group: “Human-Computer Interaction”, 2012-2014
    • now: Prof., Aalto University, Helsinki, SE
  •  Tobias Ritschel
    • Group: “Rendering and GPUs”, 2013-2015
    • now: Prof., University College London, UK
  •  Bodo Rosenhahn
    • Group for “Markerless Motion Capture”, 2007-2008
    • now: Prof., Leibniz University of Hannover, DE
  • Christian Rossow
    • Group: “System Security”, 2014-2016
    • now: Faculty, CISPA Prof., Saarland University, Saarbrücken, DE
  • Thomas Sauerwald
    • Group: “Efficient Algorithms for Massive Graphs”, 2012-2013
    • now: Lecturer, Cambridge University, UK
  • Ralf Schenkel
    • Group: “Effcient Search in Semistructured Data Spaces”, 2012 -2017
    • now: Prof., University of Trier, DE
  • Marcel Schulz
    • Group: “High-throughput Genomics and Systems Biology”, 2013-2018
    • now: Prof., Goethe University Frankfurt, DE
  • Matthias Seeger
    • Group: “Probabilistic Machine Learning and Medical Image Processing”, 2008-2010
    • now: Principal applied scientist at Amazon, DE
  • Caroline Sporleder
    • Group: “Computational Modelling of Discourse and Semantics”, 2008-2012
    • now: Prof., University Göttingen, DE
  • Maria Staudte
    • Group: “Embodied Spoken Interaction”, 2012-2019
    • now: PI on the SFB/CRC “Information Density and Linguistic Encoding”, Saarland University, DE
  • Jürgen Steimle
    • Group: “Embodied Interaction”, 2012-2016
    • now: Prof., Saarland University, Saarbrücken, DE
  • Ingmar Steiner
    • Group: “Multimodal Speech Processing”, 2012-2018
    •  now: Director Process Innovation & Development, audEERING GmbH,Gilching, DE
  • He Sun
    • Group “Randomized Algorithms”, 2013-2015
    • now: Lecturer, The University of Edinburgh, UK
  • Ivan Titov
    • Group: “Machine Learning for Natural Language Processing”, 2009-2013
    • now: Prof., University of Amsterdam, NL / University of Edinburgh, UK
  • Dominique Unruh
    • Group: “Cryptographic Protocols”, 2008-2011
    • now: Prof., University of Tartu, EE
  • Jilles Vreeken
    • Group: “Exploratory Data Analysis”, 2013-2018
    • now: Faculty, CISPA Prof., Saarland University, Saarbrücken, DE
  • Michael Wand
    • Group: “Statistical Geometry Processing”, 2008-2013
    • now: Prof., University Mainz, DE
  •  Verena Wolf
    • Group: “Analysis of Markovian Models”, 2009-2012
    • now: Prof., Saarland University, Saarbrücken, DE
  • Stefanie Wuhrer
    • Group: “Non-Rigid Shape Analysis”, 2011-2015
    • now: Researcher, INRIA, Grenoble Rhône-Alpes, FR

Scientific Advisory Board 

  • Prof. Dr. Anja Feldmann, formerly TU Berlin / Deutsche Telekom Laboratories, now Max Planck Institute for Informatics, DE
  • Prof. Dr. Nir Friedman, The Hebrew University of Jerusalem, ISL
  • Prof. Dr. Bernd Girod, Stanford University, USA
  • Prof. Dr. Andrew D. Gordon, Microsoft Research, UK
  • Prof. Dr. Thomas Gross, ETH Zürich, CH
  • Prof. Dr. Eduard Hovy, Carnegie Mellon University, USA
  • Prof. Dr. Martin Kersten, Centrum Wiskunde & Informatica (CWI), NL
  • Prof. Dr. Nelson Morgan, International Computer Science Institute, USA
  • Prof. Dr. Mark H. Overmars, formerly Utrecht University, NL, now CEO at fans4music.com
  • Prof. Dr. Holly Rushmeier, Yale University, USA (Chair)
  • Prof. Dr. Luc Van Gool, ETH Zürich, CH

Selected Publications

Most Important Publications
  • [1]  P. Aditya, R. Sen, P. Druschel, S. J. Oh, R. Benenson, M. Fritz, B. Schiele, B. Bhattacharjee, and T. T. Wu. I-Pic: A platform for privacy-compliant image capture. In Annual International Conference on Mobile Systems, Applications, and Services (MobiSys), Singapore, pages 235–248, 2016.
  • [2]  E. Alkassar, S. Böhme, K. Mehlhorn, and C. Rizkallah. A Framework for the Verification of Certifying Computations. J. of Automated Reasoning (JAR), 52(3):241–273, 2014.
  • [3]  F. Alvanaki and S. Michel. Tracking set correlations at large scale. In International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, June 22-27, 2014, pages 1507–1518, 2014.
  • [4]  Y. Assenov, F. Müller, P. Lutsik, J. Walter, T. Lengauer, and C. Bock. Comprehensive analysis of dna methylation data with rnbeads. Nature methods, 11:1138–1140, 9 2014.
  • [5]  A. Baak, M. Müller, G. Bharaj, H. Seidel, and C. Theobalt. A data-driven approach for real-time full body pose reconstruction from a depth camera. In Consumer Depth Cameras for Computer Vision, Research Topics and Applications, pages 71–98. 2013.
  • [6]  M. Backes, P. Berrang, M. Humbert, and P. Manoharan. Membership privacy in microrna-based studies. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, October 24-28, 2016, pages 319–330, 2016.
  • [7]  M. Backes, A. Kate, M. Maffei, and K. Pecina. Obliviad: Provably secure and practical online be- havioral advertising. In IEEE Symposium on Security and Privacy, SP 2012, 21-23 May 2012, San Francisco, California, USA, pages 257–271, 2012.
  • [8]  M. Backes, M. Maffei, and D. Unruh. Zero-knowledge in the applied pi-calculus and automated verification of the direct anonymous attestation protocol. In 2008 IEEE Symposium on Security and Privacy (S&P 2008), 18-21 May 2008, Oakland, California, USA, pages 202–215, 2008.
  • [9]  G. Bailly, A. Oulasvirta, T. Kötzing, and S. Hoppe. Menuoptimizer: Interactive optimization of menu systems. In Proceedings of the 26th annual ACM symposium on User interface software and tech- nology, pages 331–342. ACM, 2013.
  • [10]  H. Bast, E. Carlsson, A. Eigenwillig, R. Geisberger, C. Harrelson, V. Raychev, and F. Viger. Fast routing in very large public transportation networks using transfer patterns. In ESA 2010, pages 290–301, 2010.
  • [11] X. Bei, J. Garg, and M. Hoefer. Ascending-price algorithms for unknown markets. In Proceedings of
    the 2016 ACM Conference on Economics and Computation, EC ’16, Maastricht, The Netherlands, July 24-28, 2016, page 699, 2016.
  • [12] M. Bokeloh, M. Wand, and H.-P. Seidel. A connection between partial symmetry and inverse procedural
    modeling. 29:104:1–104:10, 2010.
  • [13] K. Bringmann. Why walking the dog takes time: Frechet distance has no strongly subquadratic algorithms
    unless SETH fails. In 55th IEEE Annual Symposium on Foundations of Computer Science,
    FOCS 2014, Philadelphia, PA, USA, October 18-21, 2014, pages 661–670, 2014.
  • [14] K. Bringmann. A near-linear pseudopolynomial time algorithm for subset sum. In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017, Barcelona, Spain, Hotel Porta Fira, January 16-19, pages 1073–1084, 2017.
  • [15] H. Brouwer, M. W. Crocker, N. J. Venhuizen, and J. C. J. Hoeks. A neurocomputational model of then400 and the p600 in language processing. Cognitive Science, 41:1318–1352, 2017.
  • [16] A. Brunton, T. Bolkart, and S. Wuhrer. Multilinear wavelets: A statistical shape space for human faces. In D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, editors, Computer Vision – ECCV 2014, pages 297–312, Cham, 2014. Springer International Publishing.
  • [17] S. Chakraborty, S. Canzar, T. Marschall, and M. H. Schulz. Chromatyping: Reconstructing nucleosome profiles from nome sequencing data. In B. J. Raphael, editor, Research in Computational Molecular Biology, pages 21–36, Cham, 2018. Springer International Publishing.
  • [18] G. Christodoulou, E. Koutsoupias, and P. G. Spirakis. On the performance of approximate equilibria in congestion games. Algorithmica, 61(1):116–140, 2011.
  • [19] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele. The cityscapes dataset for semantic urban scene understanding. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  • [20] R. Curticapean, H. Dell, and D. Marx. Homomorphisms are a good basis for counting small subgraphs. In H. Hatami, P. McKenzie, and V. King, editors, Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017, Montreal, QC, Canada, June 19-23, 2017, pages 210–223. ACM, 2017.
  • [21] F. Daiber, F. Kosmalla, F. Wiehr, and A. Krüger. Footstriker: A wearable ems-based foot strike
    assistant for running. In Proceedings of the 2017 ACM International Conference on Interactive
    Surfaces and Spaces, pages 421–424. ACM, 2017.
  • [22] V. Demberg, F. Keller, and A. Koller. Incremental, predictive parsing with psycholinguistically motivated tree-adjoining grammar. Computational Linguistics, 39(4):1025–1066, 2013.
  • [23] V. Demberg and A. Sayeed. The frequency of rapid pupil dilations as a measure of linguistic processing difficulty. PLOS ONE, 11(1):1–29, 1 2016.
  • [24] E. Derr, S. Bugiel, S. Fahl, Y. Acar, and M. Backes. Keep me updated: An empirical study of third-party library updatability on android. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 – November 03, 2017, pages 2187–2200, 2017.
  • [25] C. Dick, J. Krüger, and R. Westermann. GPUGPU ray-casting for scalable terrain rendering. In Proceedings of Eurographics 2009 – Areas Papers, pages 43–50, 2009.
  • [26] P. Didyk, T. Ritschel, E. Eisemann, K. Myszkowski, and H.-P. Seidel. A perceptual model for disparity. ACM Transactions on Graphics (Proc. of SIGGRAPH), 30(4), 2011.
  • [27] B. Doerr, M. Fouz, and T. Friedrich. Social networks spread rumors in sublogarithmic time. In Proceedings of the 43rd ACM Symposium on Theory of Computing, STOC 2011, San Jose, CA,
    USA, 6-8 June 2011, pages 21–30, 2011.
  • [28] B. Doerr, M. Fouz, and T. Friedrich. Why rumors spread so quickly in social networks. Commun. ACM, 55(6):70–75, 2012.
  • [29] A. Elhayek, E. de Aguiar, A. Jain, J. Tompson, L. Pishchulin, M. Andriluka, C. Bregler, B. Schiele, and C. Theobalt. Marconi – convnet-based marker-less motion capture in outdoor and indoor scenes. IEEE Trans. Pattern Anal. Mach. Intell., 39(3):501–514, 2017.
  • [30] J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H. Seidel. Motion capture using joint skeleton tracking and surface estimation. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA, pages 1746– 1753, 2009.
  • [31] A. Gautier, Q. Nguyen, and M. Hein. Globally optimal training of generalized polynomial neural networks with nonlinear spectral methods. In Advances in Neural Information Processing Systems 29 (NIPS), 2016.
  • [32] M. Hadwiger, R. Sicat, J. Beyer, J. Krüger, and T. Möller. Sparse PDF maps for non-linear multiresolution
    image operations. ACM Transactions on Graphics (Proceedings ACM SIGGRAPH, 31(6):133:1–133:12, 2012.
  • [33] A. Haeberlen, P. Kouznetsov, and P. Druschel. PeerReview: practical accountability for distributed systems. In Proceedings of the 21st ACM Symposium on Operating Systems Principles (SOSP), Stevenson, Washington, USA, pages 175–188, 2007.
    [34] N. Hasler, C. Stoll, M. Sunkel, B. Rosenhahn, and H.-P. Seidel. A statistical model of human pose and body shape. Comput. Graph. Forum, 28(2):337–346, 2009.
  • [35] J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum. YAGO2: A spatially and temporally enhanced knowledge base from wikipedia. Artif. Intell., 194:28–61, 2013.
  • [36] J. Hoffart, M. A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater, and G. Weikum. Robust disambiguation of named entities in text. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, 27-31 July 2011, John McIntyre Conference Centre, Edinburgh, UK, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 782–792, 2011.
  • [37] J. H. Hosang, R. Benenson, P. Dollár, and B. Schiele. What makes for effective detection proposals? IEEE Trans. Pattern Anal. Mach. Intell., 38(4):814–830, 2016.
  • [38] M. B. Hullin, J. Hanika, B. Ajdin, H. Seidel, J. Kautz, and H. P. A. Lensch. Acquisition and analysis of bispectral bidirectional reflectance and reradiation distribution functions. ACM Trans. Graph., 29(4):97:1–97:7, 2010.
  • [39] E. Insafutdinov, L. Pishchulin, B. Andres, M. Andriluka, and B. Schiele. Deepercut: A deeper, stronger, and faster multi-person pose estimation model. In European Conference on Computer Vision (ECCV), 2016.
  • [40] T. Jachmann, H. Drenhaus, M. Staudte, and M. W. Crocker. Influence of speakers’ gaze on situated language comprehension: Evidence from event-related potentials. Brain and Cognition, 135, 2019.
  • [41] A. Keller, P. Leidinger, A. Bauer, A. Elsharawy, J. Haas, C. Backes, A. Wendschlag, N. Giese, C. Tjaden, K. Ott, J.Werner, T. Hackert, K. Ruprecht, H. Huwer, J. Huebers, G. Jacobs, P. Rosenstiel, H. Dommisch, A. Schaefer, and E. Meese. Toward the blood-borne mirnome of human diseases. Nature methods, 8:841–843, 9 2011.
  • [42] M. Kipp, Q. Nguyen, A. Héloir, and S. Matthes. Assessing the deaf user perspective on sign language avatars. In The 13th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS ’11, Dundee, Scotland, UK, October 24-26, 2011, pages 107–114, 2011.
  • [43] I. Kondapaneni, P. Vevoda, P. Grittmann, T. Skˇrivan, P. Slusallek, and J. Kˇrivánek. Optimal multiple importance sampling. ACM Trans. Graph. (Proceedings Siggraph 2019), 38(4):37:1–37:14, July 2019.
  • [44] S. Krause, L. Hennig, A. Moro, D. Weissenborn, F. Xu, H. Uszkoreit, and R. Navigli. Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs. J. Web Semant., 37-38:112–131, 2016.
  • [45] S. Krause, H. Li, H. Uszkoreit, and F. Xu. Large-scale learning of relation-extraction rules with distant supervision from the web. In The Semantic Web–ISWC 2012, pages 263–278. Springer, 2012.
  • [46] J. Krupp, M. Backes, and C. Rossow. Identifying the scan and attack infrastructures behind amplification ddos attacks. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, October 24-28, 2016, pages 1426–1437, 2016.
  • [47] Y. T. Lee and H. Sun. Constructing linear-sized spectral sparsification in almost-linear time. In IEEE 56th Annual Symposium on Foundations of Computer Science, FOCS 2015, Berkeley, CA, USA, 17-20 October, 2015, pages 250–269, 2015.
  • [48] R. Leißa, K. Boesche, S. Hack, A. Pérard-Gayot, R. Membarth, P. Slusallek, A. Müller, and  B. Schmidt. Anydsl: A partial evaluation framework for programming high-performance libraries. Proc. ACM Program. Lang., 2(OOPSLA):119:1–119:30, Oct. 2018.
  • [49] M. Lentz, R. Sen, P. Druschel, and B. Bhattacharjee. Secloak: ARM trustzone-based mobile peripheral control. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2018, Munich, Germany, June 10-15, 2018, pages 1–13, 2018.
  • [50] L. Li and C. Sporleder. Classifier combination for contextual idiom detection without labelled data. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, 6-7 August 2009, Singapore, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 315–323, 2009.
  • [51] P. Lutsik, L. Feuerbach, J. Arand, T. Lengauer, J. Walter, and C. Bock. Biq analyzer ht: Locusspecific analysis of dna methylation by high-throughput bisulfite sequencing. Nucleic acids research, 39:W551–556, 5 2011.
  • [52] M. Malinowski, M. Rohrbach, and M. Fritz. Ask your neurons: A neural-based approach to answering questions about images. In IEEE International Conference on Computer Vision (ICCV), 2015.
  • [53] A. Manakov, J. Restrepo, O. Klehm, R. Hegedus, E. Eisemann, H.-P. Seidel, and I. Ihrke. A reconfigurable
    camera add-on for high dynamic range, multispectral, polarization, and light-field imaging.
    ACM Transactions on Graphics, 32(4):47–1, 2013.
  • [54] P. Mandros, M. Boley, and J. Vreeken. Discovering reliable approximate functional dependencies. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 – 17, 2017, pages 355–363, 2017.
  • [55] J. McIntosh, C. McNeill, M. Fraser, F. Kerber, M. Löchtefeld, and A. Krüger. Empress: Practical hand gesture classification with wrist-mounted emg and pressure sensing. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 2332–2342. ACM, 2016.
  • [56] D. Mehta, S. Sridhar, O. Sotnychenko, H. Rhodin, M. Shafiei, H. Seidel, W. Xu, D. Casas, and C. Theobalt. VNect: real-time 3d human pose estimation with a single RGB camera. ACM Trans. Graph., 36(4):44:1–44:14, 2017.
  • [57] N. Mitev, P. Renner, T. Pfeiffer, and M. Staudte. Towards efficient human–machine collaboration: effects of gaze-driven feedback and engagement on performance. Cognitive research: principles and implications, 3(1):51, 2018.
  • [58] A. Modi, I. Titov, V. Demberg, A. Sayeed, and M. Pinkal. Modeling semantic expectations: Using script knowledge for referent prediction. Transactions of ACL, 2017.
  • [59] S. Mukherjee, G. Weikum, and C. Danescu-Niculescu-Mizil. People on drugs: credibility of user
    statements in health communities. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, New York, NY, USA – August 24 – 27, 2014, pages 65–74, 2014.
  • [60] M. Müller, D. P. W. Ellis, A. Klapuri, and G. Richard. Signal processing for music analysis. J. Sel. Topics Signal Processing, 5(6):1088–1110, 2011.
  • [61] Q. Nguyen, A. Gautier, and M. Hein. A flexible tensor block coordinate ascent scheme for hypergraph matching. In CVPR, 2015.
  • [62] F. Nunnari and A. Heloir. Yet another low-level agent handler. Computer Animation and Virtual Worlds (CAVW), 30:1891, 2019.
  • [63] S. Oviatt, B. Schuller, P. Cohen, P. G. Sonntag, Daniel, and A. Krüger. The Handbook of Multimodal- Multisensor Interfaces, Volume 1: Foundations, User Modeling, and Common Modality Combinations. Morgan & Claypool, 2017.
  • [64] K. R. Patil, P. Haider, P. B. Pope, P. J. Turnbaugh, M. Morrison, T. Scheffer, and A. C. McHardy. Taxonomic metagenome sequence assignment with structured output models. Nature Methods, 8(3):191–192, 2011.
  • [65] A. Pérard-Gayot, R. Membarth, R. Leißa, S. Hack, and P. Slusallek. Rodent: Generating renderers without writing a generator. ACM Trans. Graph. Proceedings Siggraph 2019), 38(4):40:1–40:12, July 2019.
  • [66] M. Poenisch, P. Metz, H. Blankenburg, A. Ruggieri, J.-Y. Lee, D. Rupp, I. Rebhan, K. Diederich, L. Kaderali, F. S. Domingues, M. Albrecht, V. Lohmann, H. Erfle, and R. Bartenschlager. Identification of hnrnpk as regulator of hepatitis c virus particle production. PLOS Pathogens, 11(1):1–21, 1 2015.
  • [67] D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, and T. Marschall. Dense and accurate whole-chromosome haplotyping of individual genomes. Nature Communications, 8:1293, 12 2017.
  • [68] M. Regneri, A. Koller, and M. Pinkal. Learning script knowledge with web experiments. In J. Hajic, S. Carberry, and S. Clark, editors, ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, July 11-16, 2010, Uppsala, Sweden, pages 979–988. The Association for Computer Linguistics, 2010.
  • [69] M. Regneri, M. Rohrbach, D. Wetzel, S. Thater, B. Schiele, and M. Pinkal. Grounding action descriptions in videos. Transactions of the Association for Computational Linguistics, 1:25–36, 2013.
  • [70] E. Reinhard, G. Ward, S. Pattanaik, P. Debevec, W. Heidrich, and K. Myszkowski, editors. High Dynamic Range Imaging: Acquisition, Display, and Image-based Lighting. Elsevier (Morgan Kaufmann), Burlington, MA, 2. ed. edition, 2010.
  • [71] M. Rohrbach, W. Qiu, I. Titov, S. Thater, M. Pinkal, and B. Schiele. Translating video content to natural language descriptions. In IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, December 1–8, 2013, pages 433–440, 2013.
  • [72] T. Ruffing, A. Kate, and D. Schröder. Liar, liar, coins on fire!: Penalizing equivocation by loss of bitcoins. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, CO, USA, October 12-16, 2015, pages 219–230, 2015.
  • [73] M. Sagraloff and K. Mehlhorn. Computing real roots of real polynomials. J. Symb. Comput., 73:46–86, 2016.
  • [74] P. Sanders, K. Mehlhorn, M. Dietzfelbinger, and R. Dementiev. Sequential and Parallel Algorithms and Data Structures – The Basic Toolbox. Springer, 2019. 509 pages.
  • [75] T. Sauerwald and H. Sun. Tight bounds for randomized load balancing on arbitrary network topologies. In 53rd Annual IEEE Symposium on Foundations of Computer Science, FOCS 2012, New Brunswick, NJ, USA, October 20-23, 2012, pages 341–350, 2012.
  • [76] C. Schmaltz, P. Peter, M. Mainberger, F. Ebel, J. Weickert, and A. Bruhn. Understanding, optimising, and extending data compression with anisotropic diffusion. International Journal of Computer Vision, 108(3):222–240, July 2014.
  • [77] F. Schmidt, N. Gasparoni, G. Gasparoni, K. Gianmoena, C. Cadenas, J. K. Polansky, P. Ebert, K. Nordström, M. Barann, A. Sinha, S. Fröhler, J. Xiong, A. Dehghani Amirabad, F. Behjati Ardakani, B. Hutter, G. Zipprich, B. Felder, J. Eils, B. Brors, W. Chen, J. G. Hengstler, A. Hamann, T. Lengauer, P. Rosenstiel, J.Walter, and M. H. Schulz. Combining transcription factor binding affinities with openchromatin data for accurate gene expression prediction. Nucleic Acids Research, 45(1):54–66, 11
    2016.
  • [78] A. Schwarte, P. Haase, K. Hose, R. Schenkel, and M. Schmidt. Fedx: Optimization techniques for federated query processing on linked data. In International Semantic Web Conference (1), volume 7031 of Lecture Notes in Computer Science, pages 601–616. Springer, 2011.
  • [79] M. Seeger, H. Nickisch, R. Pohmann, and B. Schölkopf. Optimization of k-space trajectories for compressed sensing by Bayesian experimental design. Magnetic Resonance in Medicine, 63(1):116–126, 2010.
  • [80] N. Shervashidze, P. Schweitzer, E. van Leeuwen, K. Mehlhorn, and K. Borgwardt. Weisfeiler-Lehman Graph Kernels. Journal of Machine Learning Research (JMLR), 12:2539–2561, 2011.
  • [81] N. Speicher and N. Pfeifer. Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery. Bioinformatics (Oxford, England), 31:i268–i275, 6 2015.
  • [82] I. Steiner, S. Le Maguer, and A. Hewer. Synthesis of tongue motion and acoustics from text using a multimodal articulatory database. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(12):2351–2361, Dec. 2017.
  • [83] E. Teich, S. Degaetano-Ortlieb, P. Fankhauser, H. Kermes, and E. Lapshinova-Koltunski. The linguistic construal of disciplinarity: A data-mining approach using register features. JASIST, 67(7):1668–1678, 2016.
  • [84] A. Tevs, Q. Huang, M.Wand, H. Seidel, and L. J. Guibas. Relating shapes via geometric symmetries and regularities. ACM Trans. Graph. (Proceedings Siggraph 2014), 33(4):119:1–119:12, 2014.
  • [85] A. Tewari, M. Zollhöfer, H. Kim, P. Garrido, F. Bernard, P. Pérez, and C. Theobalt. MoFA: Modelbased deep convolutional face autoencoder for unsupervised monocular reconstruction. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pages 3735–3744, 2017.
  • [86] J. Thies, M. Zollhöfer, M. Stamminger, C. Theobalt, and M. Nießner. Face2face: real-time face capture and reenactment of RGB videos. Commun. ACM, 62(1):96–104, 2019.
  • [87] I. Titov and A. Klementiev. A bayesian approach to unsupervised semantic role induction. In Proceedings
    of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 12–22. Association for Computational Linguistics, 2012.
  • [88] O. T. Tursun, E. Arabadzhiyska-Koleva, M. Wernikowski, R. Mantiuk, H. Seidel, K. Myszkowski, and P. Didyk. Luminance-contrast-aware foveated rendering. ACM Trans. Graph., 38(4):98:1–98:14, 2019.
  • [89] D. Unruh. Universally composable quantum multi-party computation. In Advances in Cryptology – EUROCRYPT 2010, 29th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Monaco / French Riviera, May 30 – June 3, 2010. Proceedings, pages 486–505, 2010.
  • [90] S. Volz, A. Bruhn, L. Valgaerts, and H. Zimmer. Modeling temporal coherence for optical flow. In IEEE International Conference on Computer Vision (ICCV), pages 1116–1123, 2011.
  • [91] J. Vorba, O. Karlík, M. Sik, T. Ritschel, and J. Krivánek. On-line learning of parametric mixture models for light transport simulation. ACM Trans. Graph., 33(4):101:1–101:11, 2014.
  • [92] M. Weigel, T. Lu, G. Bailly, A. Oulasvirta, C. Majidi, and J. Steimle. iskin: Flexible, stretchable and visually customizable on-body touch sensors for mobile computing. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, Seoul, Republic of Korea, April 18-23, 2015, pages 2991–3000, 2015.
  • [93] F. Wiehr, A. Voit, D. Weber, S. Gehring, C. Witte, D. Kärcher, N. Henze, and A. Krüger. Challenges in designing and implementing adaptive ambient notification environments. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pages 1578–1583. ACM, 2016.
  • [94] C. Winkler, M. Löchtefeld, D. Dobbelstein, A. Krüger, and E. Rukzio. SurfacePhone: A mobile projection device for single- and multiuser everywhere tabletop interaction. In Proceedings of the
    32nd Annual ACM Conference on Human Factors in Computing Systems, pages 3513–3522. ACM, 2014.
  • [95] T. Wittkop, D. Emig, S. Lange, S. Rahmann, M. Albrecht, J. H. Morris, S. Böcker, J. Stoye, and J. Baumbach. Partitioning biological data with transitivity clustering. Nature Methods, 7(6):419–420, 2010.
  • [96] V. Wolf, R. Goel, M. Mateescu, and T. A. Henzinger. Solving the chemical master equation using sliding windows. BMC Systems Biology, 4:42, 2010.
  • [97] C. Wu, C. Stoll, L. Valgaerts, and C. Theobalt. On-set performance capture of multiple actors with a stereo camera. ACM Transactions on Graphics (Proceedings SIGGRAPH Asia), 32(6):161, 2013.
  • [98] Y. Yang and J. Guo. Controlling elections with bounded single-peaked width. In International conference on Autonomous Agents and Multi-Agent Systems, AAMAS ’14, Paris, France, May 5-9, 2014, pages 629–636, 2014.
  • [99] A. Zenner and A. Krüger. Shifty: A weight-shifting dynamic passive haptic proxy to enhance object perception in virtual reality. IEEE transactions on visualization and computer graphics, 23(4):1285–1294, 2017.
  • [100] X. Zhang, Y. Sugano, M. Fritz, and A. Bulling. Mpiigaze: Real-world dataset and deep appearancebased gaze estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41(1):162–175, 2019.
Research Area 1
  • [1] A. Alishahi, A. Fazly, J. Koehne, and M. W. Crocker. Sentence-based attentional mechanisms in word learning:
    Evidence from a computational model. Frontiers in Psychology, 3, 2012.
  • [2] H. Brouwer and M. W. Crocker. On the proper treatment of the n400 and p600 in language comprehension.
    Frontiers in Psychology, 8:1327, 2017.
  • [3] H. Brouwer, M. W. Crocker, N. J. Venhuizen, and J. C. J. Hoeks. A neurocomputational model of the n400 and
    the p600 in language processing. Cognitive Science, 41:1318–1352, 2017.
  • [4] F. Delogu, H. Brouwer, and M. W. Crocker. Event-related potentials index lexical retrieval (n400) and integration
    (p600) during language comprehension. Brain and Cognition, 135, 2019.
  • [5] V. Demberg and A. Sayeed. The frequency of rapid pupil dilations as a measure of linguistic processing difficulty.
    PLoS ONE, 11(1), 2016.
  • [6] S. Dutta and G. Weikum. Cross-document co-reference resolution using sample-based clustering with knowledge
    enrichment. TACL, 3:15–28, 2015.
  • [7] L. Frädrich, F. Nunnari, M. Staudte, and A. Heloir. Simulating listener gaze and evaluating its effect on human
    speakers. In International Conference on Intelligent Virtual Agents, pages 156–159. Springer, 2017.
  • [8] A. Friedrich, A. Palmer, and M. Pinkal. Situation entity types: automatic classification of clause-level aspect. In
    Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1757–
    1768, 2016.
  • [9] K. Garoufi, M. Staudte, A. Koller, and M.W. Crocker. Exploiting listener gaze to improve situated communication
    in dynamic virtual environments. Cognitive science, 40(7):1671–1703, 2016.
  • [10] C. Greenberg, A. B. Sayeed, and V. Demberg. Improving unsupervised vector-space thematic fit evaluation via
    role-filler prototype clustering. In NAACL HLT 2015, The 2015 Conference of the North American Chapter of the
    Association for Computational Linguistics: Human Language Technologies, pages 21–31, Denver, Colorado,
    USA, 2015.
  • [11] D. M. Howcroft, D. Klakow, and V. Demberg. The Extended SPaRKy Restaurant Corpus: designing a corpus
    with variable information density. Proceedings of Interspeech, pages 3757–3761, 2017.
  • [12] D. M. Howcroft, D. Klakow, and V. Demberg. Toward bayesian Synchronous Tree Substitution Grammars for
    sentence planning. In Proceedings of the 11th International Conference on Natural Language Generation,
    pages 391–396, 2018.
  • [13] T. Jachmann, H. Drenhaus, M. Staudte, and M. W. Crocker. Influence of speakers’ gaze on situated language
    comprehension: Evidence from event-related potentials. Brain and Cognition, 135, 2019.
  • [14] J. Koehne and M. W. Crocker. The interplay of cross-situational word learning and sentence-level constraints.
    Cognitive Science, 39(5):849–889, 2015.
  • [15] M. Kozhevnikov and I. Titov. Cross-lingual transfer of semantic role labeling models. In Proceedings of ACL,
    pages 1190–1200, 2013.
  • [16] S. Krause, H. Li, H. Uszkoreit, and F. Xu. Large-scale learning of relation-extraction rules with distant supervision
    from the web. In The Semantic Web–ISWC 2012, pages 263–278. Springer, 2012.
  • [17] H. Li, S. Krause, F. Xu, A. Moro, H. Uszkoreit, and R. Navigli. Improvement of n-ary relation extraction by
    adding lexical semantics to distant-supervision rule learning. In Proc. 7th Int. Conf. Agents Artif. Intell., Lisbon,
    Portugal, pages 317–324, 2015.
  • [18] A. Modi, I. Titov, V. Demberg, A. Sayeed, and M. Pinkal. Modeling semantic expectations: Using script knowledge
    for referent prediction. Transactions of ACL, 2017.
  • [19] A. Moro, H. Li, S. Krause, F. Xu, R. Navigli, and H. Uszkoreit. Semantic rule filtering for web-scale relation
    extraction. In The Semantic Web–ISWC 2013, pages 347–362. Springer, 2013.
  • [20] N. Nakashole, G. Weikum, and F. M. Suchanek. PATTY: A taxonomy of relational patterns with semantic
    types. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and
    Computational Natural Language Learning, EMNLP-CoNLL 2012, July 12–14, 2012, Jeju Island, Korea, pages
    1135–1145, 2012.
  • [21] S. Ostermann, M. Roth, A. Modi, S. Thater, and M. Pinkal. Machine comprehension using commonsense
    knowledge. In SemEval 12, pages 747–757, 2018.
  • [22] Y. Oualil, M. Magimai-Doss, F. Faubel, and D. Klakow. A probabilistic framework for multiple speaker localization.
    In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 3962–
    3966. IEEE, 2013.
  • [23] E. Raveh, I. Steiner, and B. Möbius. A computational model for phonetically responsive spoken dialogue
    systems. Interspeech, 2017.
  • [24] M. Regneri, A. Koller, and M. Pinkal. Learning Script Knowledge with Web Experiments. In Proceedings of the
    48th Annual Meeting of the Association for Computational Linguistics (ACL), Uppsala, Sweden, 2010.
  • [25] M. Regneri, M. Rohrbach, D. Wetzel, S. Thater, B. Schiele, and M. Pinkal. Grounding action descriptions in
    videos. Transactions of ACL, 1:25–36, 2013.
  • [26] M. Rohrbach, W. Qiu, I. Titov, S. Thater, M. Pinkal, and B. Schiele. Translating video content to natural language
    descriptions. In IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, December
    1–8, 2013, pages 433–440, 2013.
  • [27] M. Rohrbach, A. Rohrbach, M. Regneri, S. Amin, M. Andriluka, M. Pinkal, and B. Schiele. Recognizing finegrained
    and composite activities using hand-centric features and script data. International Journal of Computer
    Vision, pages 1–28, 2016.
  • [28] A. T. Rutherford, V. Demberg, and N. Xue. A systematic study of neural discourse models for implicit discourse
    relation. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL),
    2017.
  • [29] A. B. Sayeed, S. Fischer, and V. Demberg. Vector-space calculation of semantic surprisal for predicting word
    pronunciation duration. In Proceedings of ACL 2015, pages 763–773, 2015.
  • [30] X. Shen, H. Su, S. Niu, and V. Demberg. Improving variational encoder-decoders in dialogue generation.
    In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), pages 5456–5463,
    2018.
  • [31] W. Shi and V. Demberg. Next sentence prediction helps implicit discourse relation classification within and
    across domains. In Proceedings of EMNLP/IJCNLP, Hong Kong, 2019.
  • [32] C. Silberer and M. Pinkal. Grounding semantic roles in images. In Proceedings of the Conference on Empirical
    Methods in Natural Language Processing (EMNLP), pages 2616–2626, 2018.
  • [33] M. Staudte, M. Crocker, A. Heloir, and M. Kipp. The influence of speaker gaze on listener comprehension:
    Contrasting visual versus intentional accounts. Cognition, 133:317–328, 2014.
  • [34] É. Székely, I. Steiner, Z. Ahmed, and J. Carson-Berndsen. Facial expression-based affective speech translation.
    Journal on Multimodal User Interfaces, 8(1):87–96, 2014.
  • [35] O. Tilk, V. Demberg, A. B. Sayeed, D. Klakow, and S. Thater. Event participant modelling with neural networks.
    In EMNLP 2016.
  • [36] E. N. Tourtouri, F. Delogu, L. Sikos, and M. W. Crocker. Rational over-specification in visually-situated comprehension
    and production. Journal of Cultural Cognitive Science, 2019.
  • [37] N. J. Venhuizen, M. W. Crocker, and H. Brouwer. Expectation-based comprehension: Modeling the interaction
    of world knowledge and linguistic experience. Discourse Processes, 56(3):229–255, 2019.
  • [38] N. J. Venhuizen, P. Hendriks, M. W. Crocker, and H. Brouwer. A framework for distributional formal semantics.
    In R. Iemhoff, M. Moortgat, and R. de Queiroz, editors, Logic, Language, Information, and Computation, pages
    633–646, Berlin, Heidelberg, 2019. Springer.
Research Area 2
  • [1] M. Boshtayeva, D. Hafner, and J. Weickert. A focus fusion framework with anisotropic depth map smoothing.
    Pattern Recognition, 48(11):3310–3323, Nov. 2015. Invited Paper.
  • [2] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele.
    The cityscapes dataset for semantic urban scene understanding. In IEEE Conference on Computer Vision and
    Pattern Recognition (CVPR), 2016.
  • [3] T. Dahmen, J.-P. Baudoin, A. R. Lupini, C. Kübel, P. Slusallek, and N. de Jonge. Combined scanning transmission
    electron microscopy tilt- and focal series. Microscopy and Microanalysis: The Official Journal of Microscopy
    Society of America, Microbeam Analysis Society, Microscopical Society of Canada, pages 1–13, Feb. 2014.
  • [4] P. Dollár, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: An evaluation of the state of the art. IEEE
    Trans. Pattern Anal. Mach. Intell., 34(4):743–761, 2012.
  • [5] M. Granados, K. I. Kim, J. Tompkin, and C. Theobalt. Automatic noise modeling for ghost-free HDR reconstruction.
    ACM Transactions on Graphics (TOG), 32(6):201, 2013.
  • [6] J. H. Hosang, R. Benenson, P. Dollár, and B. Schiele. What makes for effective detection proposals? IEEE
    Trans. Pattern Anal. Mach. Intell., 38(4):814–830, 2016.
  • [7] E. Insafutdinov, L. Pishchulin, B. Andres, M. Andriluka, and B. Schiele. Deepercut: A deeper, stronger, and
    faster multi-person pose estimation model. In European Conference on Computer Vision (ECCV), 2016.
  • [8] J. H. Kappes, B. Andres, F. A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, T. Kröger,
    J. Lellmann, N. Komodakis, B. Savchynskyy, and C. Rother. A comparative study of modern inference
    techniques for structured discrete energy minimization problems. International Journal of Computer Vision,
    115(2):155–184, 2015.
  • [9] A. Khoreva, F. Galasso, M. Hein, and B. Schiele. Classifier based graph construction for video segmentation.
    In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
  • [10] M. Lapin, M. Hein, and B. Schiele. Scalable multitask representation learning for scene classification. In Proc.
    of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
  • [11] M. Malinowski, M. Rohrbach, and M. Fritz. Ask your neurons: A neural-based approach to answering questions
    about images. In IEEE International Conference on Computer Vision (ICCV), 2015.
  • [12] C. H. Nguyen, T. Ritschel, and H. Seidel. Data-driven color manifolds. ACM Trans. Graph., 34(2):20:1–20:9,
    2015.
  • [13] T. Orekondy, M. Fritz, and B. Schiele. Connecting pixels to privacy and utility: Automatic redaction of private
    information in images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
  • [14] T. Orekondy, B. Schiele, and M. Fritz. Knockoff nets: Stealing functionality of black-box models. In IEEE
    Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  • [15] B. Pepik, M. Stark, P. V. Gehler, and B. Schiele. Multi-view and 3D deformable part models. IEEE Trans. Pattern
    Anal. Mach. Intell., 37(11):2232–2245, 2015.
  • [16] M. Regneri, M. Rohrbach, D. Wetzel, S. Thater, B. Schiele, and M. Pinkal. Grounding action descriptions in
    videos. Transactions of the Association for Computational Linguistics, 1:25–36, 2013.
  • [17] M. Rempfler, M. Schneider, G. D. Ielacqua, X. Xiao, S. R. Stock, J. Klohs, G. Székely, B. Andres, and B. H.
    Menze. Reconstructing cerebrovascular networks under local physiological constraints by integer programming.
    Medical Image Analysis, 25(1):86–94, 2015.
  • [18] I. Reshetouski, A. Manakov, A. Bhandari, R. Raskar, H.-P. Seidel, and I. Ihrke. Discovering the structure of
    a planar mirror system from multiple observations of a single point. In Proc. IEEE Conference on Computer
    Vision and Pattern Recognition (CVPR), pages 89–96, 2013.
  • [19] A. Rohrbach, M. Rohrbach, R. Hu, T. Darrell, and B. Schiele. Grounding of textual phrases in images by
    reconstruction. In European Conference on Computer Vision (ECCV), 2016.
  • [20] C. Schmaltz, P. Peter, M. Mainberger, F. Ebel, J. Weickert, and A. Bruhn. Understanding, optimising, and
    extending data compression with anisotropic diffusion. International Journal of Computer Vision, 108(3):222–
    240, July 2014.
  • [21] R. Shetty, B. Schiele, and M. Fritz. A4NT: author attribute anonymity by adversarial training of neural machine
    translation. In USENIX Security, 2018.
  • [22] S. Tang, M. Andriluka, B. Andres, and B. Schiele. Multiple people tracking by lifted multicut and person reidentification.
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
  • [23] A. Tevs, A. Berner, M. Wand, I. Ihrke, M. Bokeloh, J. Kerber, and H. Seidel. Animation cartography – intrinsic
    reconstruction of shape and motion. ACM Transactions on Graphics, 31(2):12, 2012.
  • [24] J. Weickert, K. Hagenburg, M. Breuß, and O. Vogel. Linear osmosis models for visual computing. In A. Heyden,
    F. Kahl, C. Olsson, M. Oskarsson, and X.-C. Tai, editors, Energy Minimisation Methods in Computer Vision and
    Pattern Recognition. 2013.
  • [25] C.Wojek, S.Walk, S. Roth, K. Schindler, and B. Schiele. Monocular visual scene understanding: Understanding
    multi-object traffic scenes. IEEE Trans. Pattern Anal. Mach. Intell., 35(4):882–897, 2013.
  • [26] M. Zollhöfer, A. Dai, M. Innmann, C. Wu, M. Stamminger, C. Theobalt, and M. Nießner. Shading-based refinement
    on volumetric signed distance functions. ACM Transactions on Graphics (Proc. of ACM SIGGRAPH), 2015.
Research Area 3
  • [1] A. Adamaszek and A. Wiese. A QPTAS for maximum weight independent set of polygons with polylogarithmically
    many vertices. In SODA.
  • [2] A. Adamaszek and A. Wiese. Approximation schemes for maximum weight independent set of rectangles. In
    FOCS 2013, pages 400–409. IEEE, 2013.
  • [3] AFNOR. Interfaces utilisateurs – Dispositions de clavier bureautique français, NF Z71-300 Avril 2019. La Plaine
    Saint-Denis: AFNOR, Version de 2019-04-P, 85 p.
  • [4] E. Alkassar, S. Böhme, K. Mehlhorn, and C. Rizkallah. A Framework for the Verification of Certifying Computations.
    J. of Automated Reasoning (JAR), 52(3):241–273, 2014.
  • [5] K. Bringmann. Why walking the dog takes time: Frechet distance has no strongly subquadratic algorithms unless
    SETH fails. In 55th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2014, Philadelphia,
    PA, USA, October 18-21, 2014, pages 661–670, 2014.
  • [6] K. Bringmann. A near-linear pseudopolynomial time algorithm for subset sum. In Proceedings of the Twenty-
    Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017, Barcelona, Spain, Hotel Porta Fira,
    January 16-19, pages 1073–1084, 2017.
  • [7] T. Bühler, S. Rangapuram, S. Setzer, and M. Hein. Constrained fractional set programs and their application in
    local clustering and community detection. In Proc. of the 30th Int. Conf. on Machine Learning (ICML), pages
    624–632, 2013.
  • [8] P. Chalermsook, M. Goswami, L. Kozma, K. Mehlhorn, and T. Saranurak. Pattern-avoiding access in binary
    search trees. In FOCS, pages 410–423, 2015.
  • [9] P. Chalermsook, M. Goswami, L. Kozma, K. Mehlhorn, and T. Saranurak. Self-Adjusting Binary Search Trees:
    What Makes Them Tick?. In ESA, pages 300–312. 2015.
  • [10] B. Doerr, M. Fouz, and T. Friedrich. Why rumors spread so quickly in social networks. Commun. ACM, 55(6):70–
    75, 2012.
  • [11] R. Duan and K. Mehlhorn. A Combinatorial Polynomial Algorithm for the Linear Arrow-Debreu Market. Information
    and Computation, 243:112–132, 2015.
  • [12] N. Fountoulakis, K. Panagiotou, and T. Sauerwald. Ultra-fast rumor spreading in social networks. In SODA,
    pages 1642–1660, 2012.
  • [13] M. John and A. Karrenbauer. Dynamic sparsification for quadratic assignment problems. In M. Khachay,
    Y. Kochetov, and P. Pardalos, editors, Mathematical Optimization Theory and Operations Research, pages
    232–246, Cham, 2019. Springer International Publishing.
  • [14] A. Khoreva, F. Galasso, M. Hein, and B. Schiele. Classifier based graph construction for video segmentation.
    In CVPR, 2015.
  • [15] A. Kobel, F. Rouillier, and M. Sagraloff. Computing real roots of real polynomials . . . and now for real! ISSAC,
    pages 303–310, 2016.
  • [16] M. Lapin, M. Hein, and B. Schiele. Top-k multiclass SVM. In NIPS, 2015.
  • [17] M. Nazarieh, A. Wiese, T. Will, M. Hamed, and V. Helms. Identification of key player genes in gene regulatory
    networks. BMC Systems Biology, 10:88, 2016.
  • [18] Q. Nguyen, A. Gautier, and M. Hein. A flexible tensor block coordinate ascent scheme for hypergraph matching.
    In CVPR, 2015.
  • [19] L. Noschinski, C. Rizkallah, and K. Mehlhorn. Verification of certifying computations through AutoCorres and
    Simpl. In NASA Formal Methods, pages 46–61. 2014.
  • [20] M. Pilipczuk, M. Pilipczuk, P. Sankowski, and E. J. van Leeuwen. Network sparsification for steiner problems
    on planar and bounded-genus graphs. In FOCS, pages 276–285, 2014.
  • [21] M. Sagraloff and K. Mehlhorn. Computing real roots of real polynomials. J. Symb. Comput., 73:46–86, 2016.
Research Area 4
  • [1] P. Aditya, V. Erdélyi, M. Lentz, E. Shi, B. Bhattacharjee, and P. Druschel. EnCore: Private, context-based
    communication for mobile social apps. In Proc. 12th Annual International Conference on Mobile Systems,
    Applications, and Services (MobiSys), pages 135–148, 2014.
  • [2] M. Backes, M. Barbosa, D. Fiore, and R. M. Reischuk. ADSNARK: Nearly practical and privacy-preserving
    proofs on authenticated data. In Proc. 36th IEEE Symposium on Security & Privacy (S&P), pages 271–286,
    2015.
  • [3] M. Backes, F. Bendun, M. Maffei, E. Mohammadi, and K. Pecina. Symbolic malleable zero-knowledge proofs.
    In Proc. 28th IEEE Computer Security Foundations Symposium (CSF), pages 412–426, 2015.
  • [4] M. Backes, S. Bugiel, C. Hammer, O. Schranz, and P. von Styp-Rekowsky. Boxify: Full-fledged app sandboxing
    for stock Android. In Proc. 24th USENIX Security Symposium, pages 691–706, 2015.
  • [5] M. Backes, D. Fiore, and R. M. Reischuk. Verifiable delegation of computation on outsourced data. In Proc.
    16th ACM Conference on Computer and Communication Security (CCS), pages 863–874, 2013.
  • [6] M. Backes, T. Holz, B. Kollenda, P. Koppe, S. Nürnberger, and J. Pewny. You can run but you can’t read: Preventing
    disclosure exploits in executable code. In Proc. 20th ACM Conference on Computer and Communication
    Security (CCS), pages 1342–1353, 2014.
  • [7] M. Backes, M. Humbert, J. Pang, and Y. Zhang. walk2friends: Inferring social links from mobility profiles. In
    Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017,
    Dallas, TX, USA, October 30 – November 03, 2017, pages 1943–1957, 2017.
  • [8] M. Backes, A. Kate, P. Manoharan, S. Meiser, and E. Mohammadi. AnoA: A framework for analyzing anonymous
    communication protocols. In Proc. 26th IEEE Computer Security Foundations Symposium (CSF), pages 163–
    178, 2013.
  • [9] M. Backes, A. Kate, and S. Meiser. (Nothing else) MATor(s): Monitoring the anonymity of Tor’s path selection.
    In Proc. 20th ACM Conference on Computer Communication Security (CCS), 2014.
  • [10] M. Backes, P. Manoharan, and E. Mohammadi. TUC: Time-sensitive and Modular Analysis of Anonymous
    Communication. In Proc. 27th IEEE Computer Security Foundations Symposium (CSF), pages 383–397, 2014.
  • [11] M. Backes, S. Meiser, and M. Slowik. Your choice MATor(s): Large-scale quantitative anonymity assessment of
    tor path selection algorithms against structural attacks. In Proc. 16th Privacy Enhancing Technologies Symposium
    (PETS), 2016.
  • [12] M. Backes and S. Nürnberger. Oxymoron: Making fine-grained memory randomization practical by allowing
    code sharing. In Proc. 23rd USENIX Security Symposium, 2014.
  • [13] M. Bugliesi, S. Calzavara, F. Eigner, and M. Maffei. Resource-aware authorization policies in statically typed
    cryptographic protocols. In Proc. 24th IEEE Computer Security Foundations Symposium (CSF), pages 83–98,
    2011.
  • [14] F. Eigner and M. Maffei. Differential privacy by typing in security protocols. In Proc. 26th IEEE Computer
    Security Foundations Symposium (CSF), pages 272–286, 2013.
  • [15] S. Le Blond, D. Choffnes, W. Caldwell, P. Druschel, and N. Merritt. Herd: A scalable, traffic analysis resistant
    anonymity network for VoIP systems. In Proc. 2015 ACM Conference on Special Interest Group on Data
    Communication (SIGCOMM), pages 639–652, 2015.
  • [16] S. Le Blond, D. Choffnes, W. Zhou, P. Druschel, H. Ballani, and P. Francis. Towards efficient traffic-analysis resistant
    anonymity networks. In Proc. 2013 ACM Conference on Special Interest Group on Data Communication
    (SIGCOMM), pages 303–314, 2013.
  • [17] M. Lentz, V. Erdélyi, P. Aditya, E. Shi, P. Druschel, and B. Bhattacharjee. SDDR: Light-weight, secure mobile
    encounters. In Proc. 23rd USENIX Security Symposium, pages 925–940, 2014.
  • [18] K. Lu, S. Nuernberger, M. Backes, and W. Lee. How to make ASLR win the clone wars: Runtime rerandomization.
    In Proc. 23rd Annual Network and Distributed System Security Symposium (NDSS), 2016.
  • [19] M. Maffei, K. Pecina, and M. Reinert. Security and privacy by declarative design. In Proc. 26th IEEE Computer
    Security Foundations Symposium (CSF), pages 81–96, 2013.
Research Area 5
  • [1] A. Abujabal, R. S. Roy, M. Yahya, and G. Weikum. Never-ending learning for open-domain question answering
    over knowledge bases. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW
    2018, Lyon, France, April 23-27, 2018, pages 1053–1062, 2018.
  • [2] F. Alvanaki and S. Michel. Tracking set correlations at large scale. In International Conference on Management
    of Data, SIGMOD 2014, Snowbird, UT, USA, June 22-27, 2014, pages 1507–1518, 2014.
  • [3] A. Anand, S. J. Bedathur, K. Berberich, and R. Schenkel. Index maintenance for time-travel text search. In The
    35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’12,
    Portland, OR, USA, August 12–16, 2012, pages 235–244, 2012.
  • [4] K. Beedkar, K. Berberich, R. Gemulla, and I. Miliaraki. Closing the gap: Sequence mining at scale. ACM Trans.
    Database Syst., 40(2):8, 2015.
  • [5] K. Budhathoki and J. Vreeken. Causal inference by compression. In IEEE 16th International Conference on
    Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain, pages 41–50, 2016.
  • [6] L. D. Corro and R. Gemulla. ClausIE: Clause-based open information extraction. In 22nd International World
    Wide Web Conference, WWW ’13, Rio de Janeiro, Brazil, May 13–17, 2013, pages 355–366, 2013.
  • [7] M. Dylla, I. Miliaraki, and M. Theobald. Top-k query processing in probabilistic databases with non-materialized
    views. In 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, April 8–12,
    2013, pages 122–133, 2013.
  • [8] P. Ernst, A. Siu, and G. Weikum. KnowLife: A versatile approach for constructing a large knowledge graph for
    biomedical sciences. BMC Bioinformatics, 16:157, 2015.
  • [9] P. Ernst, A. Siu, and G. Weikum. Highlife: Higher-arity fact harvesting. In Proceedings of the 2018 World Wide
    Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23-27, 2018, pages 1013–1022, 2018.
  • [10] L. Galárraga, C. Teflioudi, K. Hose, and F. M. Suchanek. Fast rule mining in ontological knowledge bases with
    AMIE+. VLDB J., 24(6):707–730, 2015.
  • [11] B. R. Goldsmith, M. Boley, J. Vreeken, M. Scheffler, and L. M. Ghiringhelli. Uncovering structure-property
    relationships of materials by subgroup discovery. New Journal of Physics, 19(1):013031, 2017.
  • [12] A. Grycner and G. Weikum. POLY: mining relational paraphrases from multilingual sentences. In Proceedings
    of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas,
    USA, November 1-4, 2016, pages 2183–2192, 2016.
  • [13] J. Hoffart, Y. Altun, and G. Weikum. Discovering emerging entities with ambiguous names. In 23rd International
    World Wide Web Conference, WWW ’14, Seoul, Republic of Korea, April 7–11, 2014, pages 385–396, 2014.
  • [14] J. Hoffart, D. Milchevski, and G. Weikum. AESTHETICS: analytics with strings, things, and cats. In Proceedings
    of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM
    2014, Shanghai, China, November 3-7, 2014, pages 2018–2020, 2014.
  • [15] J. Hoffart, D. Milchevski, and G. Weikum. STICS: searching with strings, things, and cats. In The 37th International
    ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’14, Gold Coast ,
    QLD, Australia – July 06 – 11, 2014, pages 1247–1248, 2014.
  • [16] J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum. YAGO2: A spatially and temporally enhanced
    knowledge base from Wikipedia. Artif. Intell., 194:28–61, 2013.
  • [17] J. Hoffart, M. A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater, and G. Weikum.
    Robust disambiguation of named entities in text. In Proceedings of the 2011 Conference on Empirical Methods
    in Natural Language Processing, EMNLP 2011, 27-31 July 2011, John McIntyre Conference Centre, Edinburgh,
    UK, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 782–792, 2011.
  • [18] D. Koutra, U. Kang, J. Vreeken, and C. Faloutsos. Summarizing and understanding large graphs. Statistical
    Analysis and Data Mining, 8(3):183–202, 2015.
  • [19] S. Krause, L. Hennig, A. Moro, D. Weissenborn, F. Xu, H. Uszkoreit, and R. Navigli. Sar-graphs: A language
    resource connecting linguistic knowledge with semantic relations from knowledge graphs. Journal of Web
    Semantics: Science, Services and Agents on the World Wide Web, Special Issue on Knowledge Graphs, 2016.
    To appear.
  • [20] S. Krause, H. Li, H. Uszkoreit, and F. Xu. Large-scale learning of relation-extraction rules with distant supervision
    from the web. In The Semantic Web – ISWC 2012 – 11th International Semantic Web Conference, Boston,
    MA, USA, November 11-15, 2012, Proceedings, Part I, pages 263–278, 2012.
  • [21] P. Mandros, M. Boley, and J. Vreeken. Discovering reliable approximate functional dependencies. In Proceedings
    of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax,
    NS, Canada, August 13 – 17, 2017, pages 355–363, 2017.
  • [22] P. Mandros, M. Boley, and J. Vreeken. Discovering reliable dependencies from data: Hardness and improved
    algorithms. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17-20, 2018,
    pages 317–326, 2018.
  • [23] S. Mukherjee, G. Weikum, and C. Danescu-Niculescu-Mizil. People on drugs: credibility of user statements in
    health communities. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data
    Mining, KDD ’14, New York, NY, USA – August 24 – 27, 2014, pages 65–74, 2014.
  • [24] N. Nakashole, G. Weikum, and F. M. Suchanek. PATTY: A taxonomy of relational patterns with semantic
    types. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and
    Computational Natural Language Learning, EMNLP-CoNLL 2012, July 12–14, 2012, Jeju Island, Korea, pages
    1135–1145, 2012.
  • [25] D. B. Nguyen, A. Abujabal, K. Tran, M. Theobald, and G. Weikum. Query-driven on-the-fly knowledge base
    construction. PVLDB, 11(1):66–79, 2017.
  • [26] D. B. Nguyen, M. Theobald, and G. Weikum. J-NERD: joint named entity recognition and disambiguation with
    rich linguistic features. TACL, 4:215–229, 2016.
  • [27] M. Ponza, L. D. Corro, and G. Weikum. Facts that matter. In Proceedings of the 2018 Conference on Empirical
    Methods in Natural Language Processing, Brussels, Belgium, October 31 – November 4, 2018, pages 1043–
    1048, 2018.
  • [28] K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum. Where the truth lies: Explaining the credibility of emerging
    claims on the web and social media. In Proceedings of the 26th International Conference on World Wide Web
    Companion, Perth, Australia, April 3-7, 2017, pages 1003–1012, 2017.
  • [29] K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum. Credeye: A credibility lens for analyzing and explaining
    misinformation. In Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018,
    Lyon , France, April 23-27, 2018, pages 155–158, 2018.
  • [30] K. Popat, S. Mukherjee, A. Yates, and G. Weikum. Declare: Debunking fake news and false claims using
    evidence-aware deep learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language
    Processing, Brussels, Belgium, October 31 – November 4, 2018, pages 22–32, 2018.
  • [31] R. Rubino, S. Degaetano-Ortlieb, E. Teich, and J. van Genabith. Modeling diachronic change in scientific
    writing with information density. In COLING 2016, 26th International Conference on Computational Linguistics,
    Proceedings of the Conference: Technical Papers, December 11-16, 2016, Osaka, Japan, pages 750–761,
    2016.
  • [32] F. M. Schuhknecht, A. Jindal, and J. Dittrich. An experimental evaluation and analysis of database cracking.
    VLDB J., 25(1):27–52, 2016.
  • [33] N. Tandon, G. de Melo, F. M. Suchanek, and G. Weikum. WebChild: Harvesting and organizing commonsense
    knowledge from the web. In Seventh ACM International Conference on Web Search and Data Mining, WSDM
    2014, New York, NY, USA, February 24–28, 2014, pages 523–532, 2014.
  • [34] N. Tandon, C. D. Hariman, J. Urbani, A. Rohrbach, M. Rohrbach, and G. Weikum. Commonsense in parts:
    Mining part-whole relations from theWeb and image tags. In Thirtieth AAAI Conference on Artificial Intelligence.
    AAAI, 2016.
  • [35] E. Teich, S. Degaetano-Ortlieb, P. Fankhauser, H. Kermes, and E. Lapshinova-Koltunski. The linguistic construal
    of disciplinarity: A data-mining approach using register features. JASIST, 67(7):1668–1678, 2016.
  • [36] J. Vreeken. Causal inference by direction of information. In Proceedings of the 2015 SIAM International
    Conference on Data Mining, Vancouver, BC, Canada, April 30–May 2, 2015, pages 909–917, 2015.
  • [37] D. Weissenborn, L. Hennig, F. Xu, and H. Uszkoreit. Multi-objective optimization for the joint disambiguation
    of nouns and named entities. In Proceedings of the 53rd Annual Meeting of the Association for Computational
    Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation
    of Natural Language Processing, ACL 2015, July 26–31, 2015, Beijing, China, Volume 1: Long Papers, pages
    596–605, 2015.
  • [38] M. Yahya, K. Berberich, S. Elbassuoni, M. Ramanath, V. Tresp, and G. Weikum. Natural language questions for
    the web of data. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing
    and Computational Natural Language Learning, EMNLP-CoNLL 2012, July 12–14, 2012, Jeju Island,
    Korea, pages 379–390, 2012.
Research Area 6
  • [1] M. Ahmad, V. Helms, O. V. Kalinina, and T. Lengauer. Relative principal components analysis: Application to
    analyzing biomolecular conformational changes. Journal of Chemical Theory and Computation, 15(4):2166–2178, 2019.
  • [2] F. Albrecht, M. List, C. Bock, and T. Lengauer. Deepblue epigenomic data server: programmatic data retrieval
    and analysis of epigenome region sets. Nucleic Acids Research, 44:581–586, 04 2016.
  • [3] Y. Assenov, F. Müller, P. Lutsik, J.Walter, T. Lengauer, and C. Bock. Comprehensive analysis of DNA methylation
    data with RnBeads. Nat Methods, 11(11):1138–40, 2014.
  • [4] D. Becker, P. Lutsik, P. Ebert, C. Bock, T. Lengauer, and J. Walter. BiQ Analyzer HiMod: An interactive software
    tool for high-throughput locus-specific analysis of 5-methylcytosine and its oxidized derivatives. Nucleic Acids
    Research, 42(Webserver-Issue):501–507, 2014.
  • [5] M. J. P. Chaisson, A. D. Sanders, X. Zhao, A. Malhotra, D. Porubsky, T. Rausch, E. J. Gardner, O. L. Rodriguez,
    L. Guo, R. L. Collins, X. Fan, J. Wen, R. E. Handsaker, S. Fairley, Z. N. Kronenberg, X. Kong, F. Hormozdiari,
    D. Lee, A. M. Wenger, A. R. Hastie, D. Antaki, T. Anantharaman, P. A. Audano, H. Brand, S. Cantsilieris, H. Cao,
    E. Cerveira, C. Chen, X. Chen, C.-S. Chin, Z. Chong, N. T. Chuang, C. C. Lambert, D. M. Church, L. Clarke,
    A. Farrell, J. Flores, T. Galeev, D. U. Gorkin, M. Gujral, V. Guryev,W. H. Heaton, J. Korlach, S. Kumar, J. Y. Kwon,
    E. T. Lam, J. E. Lee, J. Lee, W.-P. Lee, S. P. Lee, S. Li, P. Marks, K. Viaud-Martinez, S. Meiers, K. M. Munson,
    F. C. P. Navarro, B. J. Nelson, C. Nodzak, A. Noor, S. Kyriazopoulou-Panagiotopoulou, A. W. C. Pang, Y. Qiu,
    G. Rosanio, M. Ryan, A. Stütz, D. C. J. Spierings, A. Ward, A. E. Welch, M. Xiao, W. Xu, C. Zhang, Q. Zhu,
    X. Zheng-Bradley, E. Lowy, S. Yakneen, S. McCarroll, G. Jun, L. Ding, C. L. Koh, B. Ren, P. Flicek, K. Chen,
    M. B. Gerstein, P.-Y. Kwok, P. M. Lansdorp, G. T. Marth, J. Sebat, X. Shi, A. Bashir, K. Ye, S. E. Devine,
    M. E. Talkowski, R. E. Mills, T. Marschall, J. O. Korbel, E. E. Eichler, and C. Lee. Multi-platform discovery of
    haplotype-resolved structural variation in human genomes. Nature Communications, 10(1):1784, 2019.
  • [6] S. Chakraborty, S. Canzar, T. Marschall, and M. H. Schulz. Chromatyping: Reconstructing nucleosome profiles
    from nome sequencing data. In B. J. Raphael, editor, Research in Computational Molecular Biology, pages
    21–36, Cham, 2018. Springer International Publishing.
  • [7] N. T. Doncheva, C. Klein, J. H. Morris, M. Wybrow, F. S. Domingues, and M. Albrecht. Integrative visual analysis
    of protein sequence mutations. BMC Proceedings, 8(Suppl 2):S2, 2014.
  • [8] J. Hasenauer, V. Wolf, A. Kazeroonian, and F. Theis. Method of conditional moments (MCM) for the chemical
    master equation. Journal of Mathematical Biology, 69(3):687–735, 2014.
  • [9] O. V. Kalinina, N. Pfeifer, and T. Lengauer. Modelling binding between CCR5 and CXCR4 receptors and their
    ligands suggests the surface electrostatic potential of the co-receptor to be a key player in the HIV-1 tropism.
    Retrovirology, 10(11):130, 2013.
  • [10] A. Keller, P. Leidinger, A. Bauer, A. Elsharawy, J. Haas, C. Backes, A. Wendschlag, N. Giese, C. Tjaden, K. Ott,
    J. Werner, T. Hackert, K. Ruprecht, H. Huwer, J. Huebers, G. Jacobs, P. Rosenstiel, H. Dommisch, A. Schaefer,
    and E. Meese. Toward the blood-borne mirnome of human diseases. Nature methods, 8:841–843, 09 2011.
  • [11] F. Klein, T. Schoofs, M. Braunschweig, K. F. Kreider, A. Feldmann, L. Nogueira, T. Oliveira, J. Lorenzi, G. H.
    Learn, A. P. West, M. Seaman, J. McElrath, N. Pfeifer, B. H. Hahn, M. Caskey, and M. C. Nussenzweig. HIV-1
    immunotherapy with monoclonal antibody 3BNC117 elicits host immune responses against HIV-1. Science,
    2016, in press.
  • [12] J. Z. Liu, J. R. Hov, T. Folseraas, E. Ellinghaus, S. M. Rushbrook, N. T. Doncheva, O. A. Andreassen, R. K.
    Weersma, T. J. Weismuller, B. Eksteen, P. Invernizzi, G. M. Hirschfield, D. N. Gotthardt, A. Pares, D. Ellinghaus,
    T. Shah, B. D. Juran, P. Milkiewicz, C. Rust, C. Schramm, T. Muller, B. Srivastava, G. Dalekos, M. M. Nothen,
    S. Herms, J. Winkelmann, M. Mitrovic, F. Braun, C. Y. Ponsioen, P. J. P. Croucher, M. Sterneck, A. Teufel,
    A. L. Mason, J. Saarela, V. Leppa, R. Dorfman, D. Alvaro, A. Floreani, S. Onengut-Gumuscu, S. S. Rich, W. K.
    Thompson, A. J. Schork, S. Naess, I. Thomsen, G. Mayr, I. R. Konig, K. Hveem, I. Cleynen, J. Gutierrez-
    Achury, I. Ricano-Ponce, D. van Heel, E. Bjornsson, R. N. Sandford, P. R. Durie, E. Melum, M. H. Vatn, M. S.
    Silverberg, R. H. Duerr, L. Padyukov, S. Brand, M. Sans, V. Annese, J.-P. Achkar, K. M. Boberg, H.-U. Marschall,
    O. Chazouilleres, C. L. Bowlus, C. Wijmenga, E. Schrumpf, S. Vermeire, M. Albrecht, J. D. Rioux, G. Alexander,
    A. Bergquist, J. Cho, S. Schreiber, M. P. Manns, M. Farkkila, A. M. Dale, R. W. Chapman, K. N. Lazaridis,
    A. Franke, C. A. Anderson, and T. H. Karlsen. Dense genotyping of immune-related disease regions identifies
    nine new risk loci for primary sclerosing cholangitis. Nature Genetics, 45(6):670–677, 2013.
  • [13] P. Lutsik, L. Feuerbach, J. Arand, T. Lengauer, J. Walter, and C. Bock. Biq analyzer ht: Locus-specific analysis
    of dna methylation by high-throughput bisulfite sequencing. Nucleic acids research, 39:W551–556, 05 2011.
  • [14] S. Müller, J. Weickert, and N. Graf. Automatic brain tumor segmentation with a fast Mumford-Shah algorithm. In
    M. A. Styner and E. D. Angelini, editors, Medical Imaging 2016: Image Processing, volume 9784. SPIE Press,
    Bellingham, 2016.
  • [15] F. Müller, M. Scherer, Y. Assenov, P. Lutsik, J. Walter, T. Lengauer, and C. Bock. Rnbeads 2.0: Comprehensive
    analysis of dna methylation data. Genome biology, 20:55, 03 2019.
  • [16] D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, and T. Marschall. Dense and accurate
    whole-chromosome haplotyping of individual genomes. Nature Communications, 8:1293, 12 2017.
  • [17] A. D. Sanders, S. Meiers, M. Ghareghani, D. Porubsky, H. Jeong, M. A. C. van Vliet, T. Rausch, P. Richter-
    Pecha´nska, J. B. Kunz, S. Jenni, D. Bolognini, G. M. C. Longo, B. Raeder, V. Kinanen, J. Zimmermann, V. Benes,
    M. Schrappe, B. R. Mardin, A. Kulozik, B. Bornhauser, J.-P. Bourquin, T. Marschall, and J. O. Korbel. Single-cell
    analysis of structural variations and complex rearrangements with tri-channel-processing. Nature Biotechnology. in press.
  • [18] F. Schmidt, N. Gasparoni, G. Gasparoni, K. Gianmoena, C. Cadenas, J. K. Polansky, P. Ebert, K. Nordström,
    M. Barann, A. Sinha, S. Fröhler, J. Xiong, A. Dehghani Amirabad, F. Behjati Ardakani, B. Hutter, G. Zipprich,
    B. Felder, J. Eils, B. Brors, W. Chen, J. G. Hengstler, A. Hamann, T. Lengauer, P. Rosenstiel, J.Walter, and M. H.
    Schulz. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression
    prediction. Nucleic Acids Research, 45(1):54–66, 11 2016.
  • [19] N. K. Speicher and N. Pfeifer. Integrating different data types by regularized unsupervised multiple kernel
    learning with application to cancer subtype discovery. Bioinformatics (Oxford, England), 31(12):i268–i275,
    June 2015.
  • [20] D. Stöckel, T. Kehl, P. Trampert, L. Schneider, C. Backes, N. Ludwig, A. Gerasch, M. Kaufmann, M. Gessler,
    N. Graf, E. Meese, A. Keller, and H.-P. Lenhof. Multi-omics enrichment analysis using the GeneTrail2 web service. Bioinformatics, 32(10):1502–1508, Jan. 2016.
Research Area 7
  • [1] M. Bokeloh, M. Wand, H. Seidel, and V. Koltun. An algebraic model for parameterized shape editing. ACM
    Transactions on Graphics (Proceedings ACM SIGGRAPH), 31(4):78:1–78:10, 2012.
  • [2] T. Bolkart and S. Wuhrer. A groupwise multilinear correspondence optimization for 3D faces. In IEEE International
    Conference on Computer Vision (ICCV), pages 3604–3612, 2015.
  • [3] A. Elhayek, C. Stoll, N. Hasler, K. I. Kim, H.-P. Seidel, and C. Theobalt. Spatio-temporal motion tracking with
    unsynchronized cameras. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages
    1870–1877, 2012.
  • [4] P. Garrido, L. Valgaerts, C. Wu, and C. Theobalt. Reconstructing detailed dynamic face geometry from monocular
    video. ACM Transactions on Graphics (Proceedings ACM SIGGRAPH Asia), 32(6):158, 2013.
  • [5] I. Georgiev, J. Kˇrivánek, T. Davidoviˇc, and P. Slusallek. Light transport simulation with vertex connection and
    merging. ACM Transactions on Graphics (Proceedings SIGGRAPH Asia), 31(6):192:1–192:10, 2012.
  • [6] I. Georgiev, J. Kˇrivánek, T. Hachisuka, D. Nowrouzezahrai, and W. Jarosz. Joint importance sampling of loworder
    volumetric scattering. ACM Transactions on Graphics (Proceedings SIGGRAPH Asia 2013), 32(6):164:1–
    164:14, 2013.
  • [7] J. Gregson, I. Ihrke, N. Thürey, and W. Heidrich. From capture to simulation – connecting forward and inverse
    problems in fluids. ACM Transactions on Graphics (Proceedings ACM SIGGRAPH), 33(4):139:1–139:11, 2014.
  • [8] P. Grittmann, I. Georgiev, P. Slusallek, and J. Kˇrivánek. Variance-aware multiple importance sampling. ACM
    Trans. Graph. (Proceedings SiggraphAsia 2019), 2019.
  • [9] M. Hadwiger, R. Sicat, J. Beyer, J. Krüger, and T. Möller. Sparse PDF maps for non-linear multi-resolution
    image operations. ACM Transactions on Graphics (Proceedings ACM SIGGRAPH, 31(6):133:1–133:12, 2012.
  • [10] J. Kalojanov, M.Wand, and P. Slusallek. Building Construction Sets by Tiling Grammar Simplification. Computer
    Graphics Forum (Proceedings EUROGRAPHICS), 35(5), 2016.
  • [11] F. Klein, K. Sons, D. Rubinstein, and P. Slusallek. XML3D and Xflow: Combining declarative 3D for the web
    with generic data flows. IEEE Computer Graphics & Applications (CG&A), 33(5):38–47, 2013.
  • [12] I. Kondapaneni, P. Vevoda, P. Grittmann, T. Skˇrivan, P. Slusallek, and J. Kˇrivánek. Optimal multiple importance
    sampling. ACM Trans. Graph. (Proceedings Siggraph 2019), 38(4):37:1–37:14, July 2019.
  • [13] J. Kˇrivánek, I. Georgiev, T. Hachisuka, P. Vévoda, M. Šik, D. Nowrouzezahrai, and W. Jarosz. Unifying points,
    beams, and paths in volumetric light transport simulation. ACM Transactions on Graphics (Proceedings ACM
    SIGGRAPH), 33(4):103:1–103:13, 2014.
  • [14] R. Leißa, K. Boesche, S. Hack, R. Membarth, and P. Slusallek. Shallow embedding of DSLs via online partial
    evaluation. In Proceedings of the 14th International Conference on Generative Programming: Concepts &
    Experiences (GPCE), pages 11–20, 2015. Best Paper Award.
  • [15] R. Leißa, K. Boesche, S. Hack, A. Pérard-Gayot, R. Membarth, P. Slusallek, A. Müller, and B. Schmidt.
    Anydsl: A partial evaluation framework for programming high-performance libraries. Proc. ACM Program. Lang.,
    2(OOPSLA):119:1–119:30, Oct. 2018.
  • [16] J. Miroll, A. Löffler, J. Metzger, P. Slusallek, and T. Herfet. Reverse genlock for synchronous tiled display
    walls with Smart Internet Displays. In IEEE International Conference on Consumer Electronics (ICCE), pages
    236–240, 2012.
  • [17] O. Nalbach, T. Ritschel, and H.-P. Seidel. Deep screen space. In Proceedings of the 18th Meeting of the ACM
    SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D ’14, pages 79–86, 2014.
  • [18] A. Pérard-Gayot, R. Membarth, R. Leißa, S. Hack, and P. Slusallek. Rodent: Generating renderers without
    writing a generator. ACM Trans. Graph. Proceedings Siggraph 2019), 38(4):40:1–40:12, July 2019.
  • [19] K. Sons, F. Klein, J. Sutter, and P. Slusallek. shade.js: Adaptive material descriptions. Computer Graphics
    Forum, 33(7):51–60, 2014.
  • [20] A. Tevs, Q. Huang, M. Wand, H. Seidel, and L. J. Guibas. Relating shapes via geometric symmetries and
    regularities. ACM Trans. Graph. (Proceedings Siggraph 2014), 33(4):119:1–119:12, 2014.
  • [21] C. Wu, C. Stoll, L. Valgaerts, and C. Theobalt. On-set performance capture of multiple actors with a stereo
    camera. ACM Transactions on Graphics (Proceedings SIGGRAPH Asia), 32(6):161, 2013.
  • [22] M. Zollhöfer, A. Dai, M. Innmann, C. Wu, M. Stamminger, C. Theobalt, and M. Nießner. Shading-based refinement
    on volumetric signed distance functions. ACM Transactions on Graphics (Proceedings ACM SIGGRAPH),
    2015.
Research Area 8
  • [1] A. Baak, M. Müller, G. Bharaj, H. Seidel, and C. Theobalt. A data-driven approach for real-time full body pose reconstruction from a depth camera. In A. Fossati, J. Gall, H. Grabner, X. Ren, and K. Konolige, editors, Consumer Depth Cameras for Computer Vision, Research Topics and Applications, Advances in Computer Vision and Pattern Recognition, pages 71–98. Springer, 2013.
  • [2] A. Dai, M. Nießner, M. Zollhöfer, S. Izadi, and C. Theobalt. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration. ACM Trans. Graph., 36(3):24:1–24:18, 2017.
  • [3] E. de Aguiar, C. Stoll, C. Theobalt, N. Ahmed, H.-P. Seidel, and S. Thrun. Performance capture from sparse
    multi-view video. ACM Trans. Graph., 27(3), 2008.
  • [4] A. Elhayek, E. de Aguiar, A. Jain, J. Tompson, L. Pishchulin, M. Andriluka, C. Bregler, B. Schiele, and C. Theobalt. Efficient ConvNet-based marker-less motion capture in general scenes with a low number of cameras. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA,
    June 7–12, 2015, pages 3810–3818, 2015.
  • [5] A. Elhayek, E. de Aguiar, A. Jain, J. Tompson, L. Pishchulin, M. Andriluka, C. Bregler, B. Schiele, and C. Theobalt. Marconi – convnet-based marker-less motion capture in outdoor and indoor scenes. IEEE Trans. Pattern Anal. Mach. Intell., 39(3):501–514, 2017.
  • [6] A. Elhayek, C. Stoll, N. Hasler, K. I. Kim, H. Seidel, and C. Theobalt. Spatio-temporal motion tracking with unsynchronized cameras. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 16–21, 2012, pages 1870–1877, 2012.
  • [7] O. Fried, A. Tewari, M. Zollhöfer, A. Finkelstein, E. Shechtman, D. B. Goldman, K. Genova, Z. Jin, C. Theobalt,
    and M. Agrawala. Text-based editing of talking-head video. ACM Trans. Graph., 38(4):68:1–68:14, 2019.
  • [8] J. Gall, B. Rosenhahn, T. Brox, and H.-P. Seidel. Optimization and filtering for human motion capture. International Journal of Computer Vision, 87(1-2):75–92, 2010.
  • [9] J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel. Motion capture using joint skeleton tracking and surface estimation. In CVPR, pages 1746–1753, 2009.
  • [10] P. Garrido, M. Zollhoefer, D. Casas, L. Valgaerts, K. Varanasi, P. Perez, and C. Theobalt. Reconstruction of
    personalized 3D face rigs from monocular video. ACM Trans. Graph. (Presented at SIGGRAPH 2016), 35, 2016.
  • [11] M. Granados, K. I. Kim, J. Tompkin, J. Kautz, and C. Theobalt. Background inpainting for videos with dynamic
    objects and a free-moving camera. In Computer Vision – ECCV 2012 – 12th European Conference on Computer Vision, Florence, Italy, October 7–13, 2012, Proceedings, Part I, volume 7572 of Lecture Notes in Computer Science, pages 682–695. Springer, 2012.
  • [12] M. Granados, J. Tompkin, K. I. Kim, O. Grau, J. Kautz, and C. Theobalt. How not to be seen – object removal from videos of crowded scenes. Comput. Graph. Forum, 31(2):219–228, 2012.
  • [13] M. Habermann, W. Xu, M. Zollhöfer, G. Pons-Moll, and C. Theobalt. Livecap: Real-time human performance capture from monocular video. ACM Trans. Graph., 38(2):14:1–14:17, 2019.
  • [14] N. Hasler, C. Stoll, M. Sunkel, B. Rosenhahn, and H.-P. Seidel. A statistical model of human pose and body shape. Comput. Graph. Forum, 28(2):337–346, 2009.
  • [15] A. Heloir and F. Nunnari. Towards an intuitive sign language animation authoring system for the deaf. Universal
    Access in the Information Society, pages 1–11, 2015.
  • [16] A. Jain, T. Thormählen, H.-P. Seidel, and C. Theobalt. Moviereshape: Tracking and reshaping of humans in videos. ACM Trans. Graph., 29(5), 2010.
  • [17] H. Kim, P. Garrido, A. Tewari, W. Xu, J. Thies, M. Nießner, P. Pérez, C. Richardt, M. Zollhöfer, and C. Theobalt. Deep video portraits. ACM Trans. Graph., 37(4):163:1–163:14, 2018.
  • [18] Y. Liu, J. Gall, C. Stoll, Q. Dai, H. Seidel, and C. Theobalt. Markerless motion capture of multiple characters using multiview image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 35(11):2720–2735, 2013.
  • [19] D. Mehta, S. Sridhar, O. Sotnychenko, H. Rhodin, M. Shafiei, H. Seidel, W. Xu, D. Casas, and C. Theobalt.
    VNect: real-time 3d human pose estimation with a single RGB camera. ACM Trans. Graph., 36(4):44:1–44:14, 2017.
  • [20] F. Mueller, F. Bernard, O. Sotnychenko, D. Mehta, S. Sridhar, D. Casas, and C. Theobalt. GANerated hands for
    real-time 3d hand tracking from monocular RGB. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 49–59, 2018.
  • [21] M. Neff, M. Kipp, I. Albrecht, and H.-P. Seidel. Gesture modeling and animation based on a probabilistic recreation
    of speaker style. ACM Trans. Graph., 27(1), 2008.
  • [22] H. Rhodin, N. Robertini, C. Richardt, H.-P. Seidel, and C. Theobalt. A versatile scene model with differentiable visibility applied to generative pose estimation. In Proceedings of the 2015 International Conference on Computer Vision (ICCV 2015), 2015.
  • [23] N. Robertini, E. de Aguiar, T. Helten, and C. Theobalt. Efficient multi-view performance capture of fine-scale surface detail. In 2nd International Conference on 3D Vision, 3DV 2014, Tokyo, Japan, December 8–11, 2014, Volume 1, pages 5–12, 2014.
  • [24] S. Sridhar, F. Mueller, A. Oulasvirta, and C. Theobalt. Fast and robust hand tracking using detection-guided optimization. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pages 3213–3221, 2015.
  • [25] A. Tewari, M. Zollhöfer, H. Kim, P. Garrido, F. Bernard, P. Pérez, and C. Theobalt. MoFA: Model-based deep
    convolutional face autoencoder for unsupervised monocular reconstruction. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pages 3735–3744, 2017.
  • [26] J. Thies, M. Zollhöfer, M. Nießner, L. Valgaerts, M. Stamminger, and C. Theobalt. Real-time expression transfer
    for facial reenactment. ACM Transactions on Graphics (TOG), 34(6), 2015.
  • [27] J. Thies, M. Zollhöfer, M. Stamminger, C. Theobalt, and M. Nießner. Face2Face: Real-time face capture and
    reenactment of RGB videos. In Proc. Computer Vision and Pattern Recognition (CVPR) (Oral), IEEE, 2016.
  • [28] J. Thies, M. Zollhöfer, M. Stamminger, C. Theobalt, and M. Nießner. Face2face: real-time face capture and reenactment of RGB videos. Commun. ACM, 62(1):96–104, 2019.
  • [29] C. von Tycowicz, C. Schulz, H. Seidel, and K. Hildebrandt. Real-time nonlinear shape interpolation. ACM Trans. Graph., 34(3):34, 2015.
  • [30] W. Xu, A. Chatterjee, M. Zollhöfer, H. Rhodin, D. Mehta, H. Seidel, and C. Theobalt. Monoperfcap: Human performance capture from monocular video. ACM Trans. Graph., 37(2):27:1–27:15, 2018.
Research Area 9
  • [1] M. Barz, F. Daiber, and A. Bulling. Prediction of gaze estimation error for error-aware gaze-based interfaces.
    In Proceedings of the ninth biennial acm symposium on eye tracking research & applications, pages 275–278. ACM, 2016.
  • [2] S. Castronovo, A. Mahr, and C. Müller. Multimodal Dialog in the Car: Combining Speech and Turn-And- Push Dial to Control Comfort Functions. In Proceedings of Interspeech (2010). ISCA, Makuhari, Japan, 26–30 September 2010.
  • [3] F. Daiber, F. Kosmalla, F. Wiehr, and A. Krüger. Footstriker: A wearable ems-based foot strike assistant for running. In Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces, pages 421–424. ACM, 2017.
  • [4] S. Gehring, E. Hartz, M. Löchtefeld, and A. Krüger. The media façade toolkit: Prototyping and simulating interaction with media façades. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 763–772. ACM, 2013. Intel Doctoral Honor Programme Fellowship.
  • [5] I. Giannopoulos, J. Schöning, A. Krüger, and M. Raubal. Attention as an input modality for post-WIMP interfaces
    using the viGaze eye tracking framework. Multimedia Tools and Applications, 75(6):2913–2929.
  • [6] R. Jung, L. Spassova, and G. Kahl. Product-Awareness Through Smart Audio Navigation in a Retail Environment.
    In Proceedings of the Seventh International Conference on Intelligent Environments. International Conference on Intelligent Environments (IE-2011), 7th, July 25-28, Nottingham, United Kingdom. IEEE Computer Society, 7 2011.
  • [7] F. Kosmalla, F. Daiber, and A. Krüger. ClimbSense: Automatic climbing route recognition using wrist-worn inertia
    measurement units. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pages 2033–2042. ACM, 2015.
  • [8] C. Lander, S. Gehring, A. Krüger, S. Boring, and A. Bulling. GazeProjector: Accurate gaze estimation and seamless gaze interaction across multiple displays. In Proceedings of the 28th Annual ACM Symposium on User Interface Software and Technology, pages 395–404. ACM, 2015.
  • [9] C. Lander, A. Krüger, et al. heyebrid: A hybrid approach for mobile calibration-free gaze estimation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4):149, 2018.
  • [10] M. M. Moniri and C. Müller. EyeVIUS: Intelligent vehicles in intelligent urban spaces. In Adjunct Proceedings of
    the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pages 1–6. ACM, 2014.
  • [11] R. Neßelrath. SiAM-dp: An Open Development Platform for Massively Multimodal Dialogue Systems in Cyber-
    Physical Environments. PhD thesis, Faculty of Natural Science and Technology I, Saarland University, 2015.
  • [12] R. Neßelrath and M. Feld. Towards a cognitive load ready multimodal dialogue system for in-vehicle humanmachine
    interaction. In Adjunct Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2013), pages 49–52. AutoUI’13, 2013.
  • [13] T. Schneeberger, S. von Massow, M. M. Moniri, A. Castronovo, C. Müller, and J. Macek. Tailoring mobile apps
    for safe on-road usage: How an interaction concept enables safe interaction with hotel booking, news, Wolfram Alpha and Facebook. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI), pages 241–248. ACM, 2015.
  • [14] D. Sonntag. Introspection and Adaptable Model Integration for Dialogue-Based Question Answering. In IJCAI’ 09: Proceedings of the 21st international jont conference on Artifical intelligence, pages 1549–1554, San Francisco, CA, USA, 2009. Morgan Kaufmann Publishers Inc.
  • [15] D. Sonntag. Intuition as instinctive dialogue. In Y. Cai, editor, Computing with Instincts, LNAI 5897. Springer, 2010.
  • [16] W. Wahlster, M. Feld, P. Gebhard, D. Heckmann, R. Jung, M. Kruppa, M. Schmitz, L. Spassova, and R. Wasinger. Resource-Adaptive Cognitive Processes, chapter The Shopping Experience of Tomorrow: Human-Centered and Resource-Adaptive, pages 205 – 237. Cognitive Technologies. Springer, 2010.
  • [17] C. Winkler, M. Löchtefeld, D. Dobbelstein, A. Krüger, and E. Rukzio. SurfacePhone: A mobile projection device for single- and multiuser everywhere tabletop interaction. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pages 3513–3522. ACM, 2014.
  • [18] A. Zenner and A. Krüger. Shifty: A weight-shifting dynamic passive haptic proxy to enhance object perception in virtual reality. IEEE transactions on visualization and computer graphics, 23(4):1285–1294, 2017.
  • [19] A. Zenner and A. Krüger. Drag:on: A virtual reality controller providing haptic feedback based on drag and
    weight shift. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, page 211. ACM, 2019.