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 Bullilng
    • 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.
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  • [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.
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  • [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.
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    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.
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  • [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.
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  • [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.
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    camera add-on for high dynamic range, multispectral, polarization, and light-field imaging.
    ACM Transactions on Graphics, 32(4):47–1, 2013.
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    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.
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Research Area 1
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    Evidence from a computational model. Frontiers in Psychology, 3, 2012.
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Research Area 4
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Research Area 5
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    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,
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    resource connecting linguistic knowledge with semantic relations from knowledge graphs. Journal of Web
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    health communities. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data
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    Computational Natural Language Learning, EMNLP-CoNLL 2012, July 12–14, 2012, Jeju Island, Korea, pages
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Research Area 6
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    analyzing biomolecular conformational changes. Journal of Chemical Theory and Computation, 15(4):2166–2178, 2019.
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    and analysis of epigenome region sets. Nucleic Acids Research, 44:581–586, 04 2016.
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    data with RnBeads. Nat Methods, 11(11):1138–40, 2014.
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    tool for high-throughput locus-specific analysis of 5-methylcytosine and its oxidized derivatives. Nucleic Acids
    Research, 42(Webserver-Issue):501–507, 2014.
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    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.
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    from nome sequencing data. In B. J. Raphael, editor, Research in Computational Molecular Biology, pages
    21–36, Cham, 2018. Springer International Publishing.
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    of protein sequence mutations. BMC Proceedings, 8(Suppl 2):S2, 2014.
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    master equation. Journal of Mathematical Biology, 69(3):687–735, 2014.
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    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.
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    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.
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    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.
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    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.
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    of dna methylation by high-throughput bisulfite sequencing. Nucleic acids research, 39:W551–556, 05 2011.
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    whole-chromosome haplotyping of individual genomes. Nature Communications, 8:1293, 12 2017.
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    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.
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    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.
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    learning with application to cancer subtype discovery. Bioinformatics (Oxford, England), 31(12):i268–i275,
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    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
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    Transactions on Graphics (Proceedings ACM SIGGRAPH), 31(4):78:1–78:10, 2012.
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    Conference on Computer Vision (ICCV), pages 3604–3612, 2015.
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    unsynchronized cameras. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages
    1870–1877, 2012.
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    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.
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    volumetric scattering. ACM Transactions on Graphics (Proceedings SIGGRAPH Asia 2013), 32(6):164:1–
    164:14, 2013.
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    problems in fluids. ACM Transactions on Graphics (Proceedings ACM SIGGRAPH), 33(4):139:1–139:11, 2014.
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    Trans. Graph. (Proceedings SiggraphAsia 2019), 2019.
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    image operations. ACM Transactions on Graphics (Proceedings ACM SIGGRAPH, 31(6):133:1–133:12, 2012.
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    Graphics Forum (Proceedings EUROGRAPHICS), 35(5), 2016.
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    with generic data flows. IEEE Computer Graphics & Applications (CG&A), 33(5):38–47, 2013.
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    sampling. ACM Trans. Graph. (Proceedings Siggraph 2019), 38(4):37:1–37:14, July 2019.
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    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.
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    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.
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    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.
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    Forum, 33(7):51–60, 2014.
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    regularities. ACM Trans. Graph. (Proceedings Siggraph 2014), 33(4):119:1–119:12, 2014.
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    camera. ACM Transactions on Graphics (Proceedings SIGGRAPH Asia), 32(6):161, 2013.
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    on volumetric signed distance functions. ACM Transactions on Graphics (Proceedings ACM SIGGRAPH),
    2015.
Research Area 8
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    multi-view video. ACM Trans. Graph., 27(3), 2008.
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    June 7–12, 2015, pages 3810–3818, 2015.
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    and M. Agrawala. Text-based editing of talking-head video. ACM Trans. Graph., 38(4):68:1–68:14, 2019.
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    personalized 3D face rigs from monocular video. ACM Trans. Graph. (Presented at SIGGRAPH 2016), 35, 2016.
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    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.
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    Access in the Information Society, pages 1–11, 2015.
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