Prof. Dr. Øyvind Eide is a Professor in Digital Humanities at the University of Cologne, affiliated with the Department of Digital Humanities under the Faculty of Arts and Humanities. He holds a PhD in Digital Humanities from King’s College London (2013). His research focuses on transformative digital intermedial studies, critical spatial formalization of cultural heritage information, and the relationship between texts and maps as media. He previously served as Chair of the European Association for Digital Humanities (EADH) from 2016–2019 and is actively involved in ICOM’s CIDOC committee. Eide’s work bridges theoretical and practical dimensions of digital humanities, emphasizing spatial analysis, ontology modeling, and interdisciplinary methodologies. His academic contributions include seminal works on network visualization in humanities, cultural heritage ontologies, and the application of critical formalization in media studies. Current research explores the interplay between verbal and spatial expressions of geography in historical narratives. Eide teaches courses such as 'Modelling and Digital Maps' and 'Digital Cultural Heritage,' reflecting his commitment to both research and education. Notable projects include the Ibsen Manuscripts digitization initiative, the GeoModelText tool for spatial narrative analysis, and contributions to archaeological document interconnection through the CIDOC CRM framework. He is Vice Director of the Scholarly Platform: Reflecting and Shaping the Digital Age at the University of Cologne.
Prof. Torsten Wolfgang Kuhlen serves as a Universitätsprofessor at RWTH Aachen University, leading the Teaching and Research Area for Virtual Reality and Immersive Visualization within the Department of Computer Science. He is affiliated with Chair of Computer Science 12 (High Performance Computing), the Visual Computing Institute, and remains an integral part of the RWTH IT Center where his research group operates one of the world's largest Virtual Reality laboratories including the 30 sqm aixCAVE visualization chamber. The group maintains strong connections with Computational Science & Engineering Division, National High Performance Computing Center for Computational Engineering Science (NHR4CES), and VR in Science and Industry Network NRW e.V. Prof. Kuhlen's research spans virtual reality, immersive visualization, and multimodal 3D user interfaces with applications across simulation science, production technology, neuroscience, and medicine. His work combines basic research on advanced methods and algorithms with interdisciplinary collaborations involving RWTH Aachen institutes, Forschungszentrum Jülich, and industry partners. Recent publications demonstrate strong focus on audiovisual perception, immersive analytics, collaborative virtual environments, and practical VR applications in education and manufacturing. His research group has produced significant work on listening effort in virtual environments, immersive authoring techniques, and VR applications for scientific visualization. Notable projects include VRScenarioBuilder for automated vehicle testing and applications in monitoring additive manufacturing processes. The group actively participates in major conferences including IEEE VIS and EuroVis, with several award-winning contributions. Prof. Kuhlen has advised PhD students including Martin Bellgardt who recently completed his doctoral degree on "Increasing Immersion in Machine Learning Pipelines for Mechanical Engineering". The research group maintains state-of-the-art VR infrastructure including the aixCAVE facility which is open to all RWTH research groups.
Joachim Weickert is a Professor of Mathematics and Computer Science at Saarland University where he heads the Mathematical Image Analysis Group since 2001. He received his diploma and Ph.D. in mathematics from the University of Kaiserslautern (1991, 1996), and a habilitation degree in computer science from the University of Mannheim (2001). Prior to his current position, he worked as a research assistant at the University of Kaiserslautern, as a post-doctoral researcher at the universities of Utrecht and Copenhagen, and as an assistant professor at the University of Mannheim. His research focuses on image processing, computer vision, and scientific computing, with special emphasis on techniques based on partial differential equations, variational principles, wavelets, morphological and nonlocal methods, as well as neuroexplicit approaches. He has developed mathematical models and efficient numerical algorithms for image restoration, enhancement, segmentation, compression, optic flow computation, stereo reconstruction, shape from shading, and signal processing methods for tensor fields. These ideas have been successfully applied in industry, biomedical image analysis, and other fields. Analysis of his recent publications reveals a strong trend toward combining traditional PDE-based methods with modern deep learning approaches, particularly in the areas of image inpainting and compression. His work increasingly explores the connections between numerical algorithms for partial differential equations and neural network architectures, demonstrating how mathematical foundations can inform cutting-edge AI techniques while maintaining strong theoretical guarantees. Gottfried Wilhelm Leibniz Prize (2010), considered the most important research award in Germany ERC Advanced Grant (2017) for "Inpainting-based Compression of Visual Data" Elected member of Academia Europaea - The Academy of Europe Jan Koenderink Prize for Fundamental Contributions in Computer Vision (2014) Multiple DAGM Prizes and Best Paper Awards throughout his career AAIA Fellow (2021) and Highly Ranked Scholar (2024) distinctions Professor Weickert has supervised over 250 bachelor's and master's theses and initiated the Master Programme in Visual Computing at Saarland University, the first of its kind in Germany taught in English. He has established numerous interdisciplinary collaborations with colleagues from medicine, bioinformatics, pharmacy, physics, mechatronics, and mechanical engineering. As Principal Investigator for Visual Computing within the Multimodal Computing and Interaction Cluster of Excellence, and former dean of the Faculty of Mathematics and Computer Science (2008-2010), he has played a significant leadership role in advancing visual computing research and education. He heads the Mathematical Image Analysis Group, which has been at the forefront of developing mathematical methods for image analysis. The group maintains strong connections with both theoretical mathematics and practical applications, bridging the gap between fundamental research and real-world implementation across various domains including medical imaging, industrial inspection, and multimedia processing.
Dr. Benjamin Busam is a Senior Research Scientist at the Technical University of Munich , affiliated with the Chair for Computer Science Applications in Medicine under Prof. Nassir Navab. Starting September 2025, he will hold the Professorship for Photogrammetry and Remote Sensing at TUM. His career includes leadership roles at FRAMOS Imaging Systems and Huawei Research in London. Education: Mathematics (TUM), Mathematics and Physics (ParisTech, University of Melbourne), PhD in Mathematics (TUM, 2014) His research focuses on 3D computer vision , multi-modal sensor fusion , and their applications in collaborative robotics and augmented reality . He specializes in projective geometry , 6D pose estimation , and neural radiance fields , with a particular emphasis on photometrically challenging environments. Recent publications highlight advancements in 3D scene understanding , neural rendering , and medical imaging , often leveraging machine learning and vision-language models . His work has been recognized through awards like the EMVA Young Professional Award (2015) and Innovation Pioneer of the Year (2019) , along with multiple Outstanding Reviewer distinctions at leading conferences. Dr. Busam has supervised numerous PhD and MSc students on topics including 6D pose estimation , medical augmented reality , and robotic ultrasound , collaborating with institutions like MIT , École Polytechnique , and University of Padova .
Jan Peters is a full professor (W3) at the Computer Science Department of Technische Universität Darmstadt and serves as the department head of the Systems AI for Robot Learning (SAIROL) at the German Research Center for AI (DFKI) . He is also a founding faculty member of the Hessian Centre for Artificial Intelligence . Peters holds a Ph.D. in Computer Science from the University of Southern California (2007) and dual master’s degrees in Computer Science and Electrical Engineering from USC and TU Munich respectively. Research Themes : Robot Learning, Reinforcement Learning, Imitation Learning, Tactile Sensing, Human-Robot Interaction, and Safe AI. Recent Article Trends : Focus on deep reinforcement learning (Iterated Q-Networks, Adaptive Q-Networks), safe robot foundation models , tactile-enhanced imitation learning , and physics-informed machine learning . Scientific Recognition : Recipient of the Dick Volz Best PhD Thesis Award , ERC Starting Grant , IEEE Fellow , and Amazon Research Award . Leadership : Founder of the IEEE RAS Technical Committee on Robot Learning and editor for journals including Autonomous Robots and IEEE Transactions on Robotics .
Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.
Irene Albers is a full Professor at Free University of Berlin's Department of Philosophy and Humanities, holding dual appointments at the Peter Szondi Institute for General and Comparative Literature and Institute for Romance Philology since 2004. She chairs both the Examination Board and Doctoral Committee for General and Comparative Literature, serves as Trustee of Studienstiftung des deutschen Volkes since 2004/05, and has been Principal Investigator for the Friedrich-Schlegel Graduate School for Literary Studies and Cluster Languages of Emotion since 2007. Her research bridges comparative literature, anthropology, and visual culture with institutional leadership spanning editorial projects and international conferences. Her educational background includes: Born in Bremen (1967) Romance Studies, Philosophy, and German Studies at Universities of Tübingen, Tours, and Konstanz M.A. from University of Konstanz (1993) Ph.D. from University of Konstanz (1999) on literature and photography in Émile Zola, Proust, and Claude Simon Albers' core research examines literary anthropology through colonial/postcolonial lenses, focusing on French literary engagement with Africa, surrealism, and literature-photography intersections. She pioneered studies on Michel Leiris' ethnological poetics and Claude Simon's photographic techniques, extending to historical emotion research (anatomy of affects, body language) and romance novella traditions. Her work consistently challenges Eurocentric literary canons through primitivism critique and heteronomy aesthetics, emphasizing textual restitution and cultural reclamation. Recent publications (2018-2025) reveal a sharp turn toward colonial restitution ethics, particularly in oral literature provenance. Articles increasingly analyze literature as colonial loot, connecting French modernism with African/Caribbean contexts through collaborative projects with anthropologists like Eléonore Devevey. This trajectory builds from earlier surrealist studies to concrete frameworks for decolonizing literary scholarship, spotlighting institutional complicity in cultural appropriation while advancing symmetrized modernity revisions. Scientific awards include: Senior Fellow and Prize Winner of the Zukunftskolleg, University of Konstanz (2009) Albers supervises master's and doctoral students through dedicated AVL colloquia while securing major research funding as Principal Investigator for Friedrich-Schlegel Graduate School (2007-present) and Cluster Languages of Emotion (2007-2012). Her current DFG-funded project 'Literature as Colonial Loot?' with Andreas Schmid pioneers philological provenance research. Earlier, she contributed to Collaborative Research Center 511 'Literature and Anthropology' (Konstanz, 1999-2002) and DFG-funded conferences on Claude Simon. She leads the DramaNet research project and co-edits the 'machina' series at transcript Verlag since 2010. As board member (2007-2009) and deputy (2008-2012) of Cluster Languages of Emotion, she coordinates Area D 'Cultural Codings of Emotions'. Her Zukunftskolleg fellowship (2012-2013) and Marcel Proust Society collaborations underscore sustained engagement with literary anthropology networks.
Ruben Portugues is a Professor of Brain Circuit Function and Dysfunction at the Institute of Neuroscience, Technical University of Munich (TUM). He is a full member of the Graduate School of Systemic Neurosciences (GSN), an associate and advisory board member of the Munich Center for Neurosciences (MCN), and leads a research group focused on understanding the neural basis of behavior. His lab uses larval zebrafish as a model organism to investigate sensorimotor control, decision-making, and motor learning through whole-brain imaging and circuit analysis. His research interests lie at the intersection of systems neuroscience and behavior. He investigates how brain circuits process sensory information, integrate it with motor output, and enable adaptive and flexible behavior. Key areas include the function of the cerebellum, heading direction networks, sensorimotor transformations, and the neural mechanisms of decision-making. His lab employs cutting-edge techniques including custom-built microscopes, behavioral assays, and computational analysis. The recent publications and preprints from his lab demonstrate a strong trend in decoding distributed neural circuits underlying navigation and decision-making in zebrafish. There is a clear focus on identifying specific brain regions (e.g., interpeduncular nucleus, cerebellum) and cell types involved in processing visual, motor, and spatial information. The work increasingly emphasizes whole-brain functional imaging and the emergence of cognitive-like representations such as allocentric heading direction. FENS-Kavli Network of Excellence (FKNE) PhD Thesis Prize (awarded to student Luigi Petrucco) Ruben Portugues actively mentors PhD students, including current advisees Luigi Petrucco, Ot Prat, and Shuhong Huang, and has successfully graduated Dr. Elena Dragomir and Dr. Vilim Štih. His lab engages in extensive collaborations, hosts visiting researchers, participates in teaching (e.g., CSHL Imaging Course, Cajal Course), and secures resources for advanced research. The lab is known for building its own microscopes and software, fostering technical innovation. The Portugues Lab operates as a dynamic, interdisciplinary team that combines experimental neuroscience with computational and engineering approaches. They regularly hold retreats, participate in scientific events, and contribute to community initiatives like the Munich Brain Day. The lab is preparing to relocate to the Department of Neurobiology and Behavior at Cornell University, marking a new phase in its research trajectory.
Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.
Prof. Dr.-Ing. Christoph Stiller is a full professor at the Karlsruher Institut für Technologie (KIT) and serves as the director of the Institute of Measurement and Control Technology (Institut für Mess- und Regelungstechnik, MRT). His work focuses on autonomous driving, sensor fusion, probabilistic estimation, HD mapping, motion planning, and intelligent transportation systems. Education: Details on his academic degrees are not provided in the text, but he holds the title of Dr.-Ing. indicating a doctoral degree in engineering. Research Interests: Prof. Stiller's research spans a wide array of topics critical to the development of autonomous vehicles. His work includes: Sensor Fusion: Integrating data from LiDAR, cameras, and radar to create robust perception systems. HD Mapping & Localization: Developing high-definition maps and precise localization techniques for urban and highway environments. Motion Planning & Decision Making: Creating algorithms for safe and efficient trajectory planning under uncertainty. Machine Learning & AI: Applying deep learning and reinforcement learning to perception, prediction, and control tasks. Publication Trends: His recent publications (2023–2025) emphasize robust traffic light detection, image stitching for panoramic views, motion prediction using redundancy reduction, and safety-enhanced model predictive control. The work increasingly integrates learning-based methods with classical control and estimation theory. Scientific Awards: No specific awards are listed in the provided text. Teaching & Supervision: Prof. Stiller teaches foundational and advanced courses in measurement and control systems, probabilistic estimation, and autonomous driving. He holds regular office hours during both summer and winter semesters and is actively involved in advising students and researchers. Labs & Teams: He leads the Institute of Measurement and Control Technology (MRT) at KIT, which is engaged in cutting-edge research in autonomous systems. The institute collaborates with industry and academia on large-scale projects such as UNICARagil and various European initiatives.
Katrin Bromber is a German Researcher at the Centre for Modern Oriental Studies in Berlin, where she has led the research group "Progress and its Discontents: Ideas, Practices, Materiality" since 2014. Her career spans senior research roles at ZMO and academic teaching positions at the University of Vienna. Nationality: German. Language skills: Native German, fluent English/Swahili, moderate Amharic, good reading knowledge in French/Russian, basic Tigrinya. Habilitation (2009–): African Studies, University of Vienna PhD (1993): African Linguistics, University of Leipzig MA (1988): Swahili Studies, University of Leipzig Katrin Bromber's research interrogates intersections of body politics , sports history , Swahili linguistics , and Ethiopian modernity . Her work traces how physical culture, media representation, and colonial/imperial narratives shape national identities across Africa and Asia. Current focus: "Progress" as contested concept in Ethiopian and Gulf urbanism. Key article trends reflect her translocal methodology: 2017 studies on Ethiopian stadiums and fitness culture ; 2014-2013 publications analyzing East African military press , sports branding in the Gulf , and Swahili poetry ; 2007-2000 works on colonial linguistic politics , Indian Ocean slavery , and German colonial Swahili promotion . Recurring sub-fields: body as political text, translocal modernity, media historiography. Scientific Awards: Research Grant of the Berlin City Council (1999-2000): Gendered Slavery study Service to the Scientific Community: Chairwoman, Academic Association for Horn of Africa Studies (2012–) Editorial Advisory Board - Stichproben. Vienna Journal of African Studies (2015–) Editorial Advisory Board - Studies of Department of African Languages and Cultures (2015–) Teaching Experience: Textlinguistics, Mediacommunication, African Linguistics, Swahili (elementary/advanced), Swahili Literature, Swahili in Arabic Script at Leipzig, Humboldt-Berlin, Free Berlin, and Vienna universities Anthropology of the Body at Mekelle University Summer School Field Research: Tanzania (1993-2003): Swahili Poetry, Intellectual History Kenya (2005): Indian Ocean WW2 UAE/Bahrain (2009): Sports and Body Cultures Ethiopia (2009-2015): Sports, Body Politics
John Nassour is a Researcher at the Technical University of Munich's School of Computation, Information and Technology, affiliated with the Chair of Cognitive Systems. He holds engineering degrees from Tishreen University (electronics), a Master's in intelligent systems from University of Cergy-Pontoise/École Nationale Supérieure de l'Électronique, and a joint PhD from University of Versailles/TUM. His interdisciplinary research focuses on computational cognitive systems applied to robotics, including wearable devices, humanoid robots, soft robotics, and robot learning for locomotion/manipulation. Before joining TUM in 2020, he was a lecturer/researcher at Chemnitz University of Technology. He teaches courses in cognitive systems, neuro-inspired engineering, and soft robotics.
Prof. Dr. Laura Bieger is a professor of American Studies at Ruhr University Bochum (Germany), specializing in literary theory, critical theory, and cultural history. She holds a Magister from Freie Universität Berlin and a Dr. Phil. from FU Berlin (2006), followed by a Habilitation in 2013. Previously, she held professorships at the Albert-Ludwigs-Universität Freiburg (2014–2017) and the Rijksuniversiteit Groningen (2017–2022). Her research explores the relationship between aesthetics, politics, and social practices in U.S. cultural history, with a focus on public space, literary engagement, and democracy. Education: Magister in American Studies, History, and Philosophy, Freie Universität Berlin Dr. Phil., Freie Universität Berlin (2006) Habilitation (Venia Legendi in Americanistik), Freie Universität Berlin (2013) Positions: Professorin für amerikanische Literatur und Kultur, Albert-Ludwigs-Universität Freiburg (2014–2017) Professorin für American Studies, Political Culture and Theory, Rijksuniversiteit Groningen (2017–2022) Universitätsprofessorin American Studies, Ruhr-Universität Bochum (since 2022) Her work bridges literary analysis with interdisciplinary inquiries into space, democracy, and cultural practices. Key publications include Ästhetik der Immersion (2007), Belonging and Narrative (2018), and the forthcoming Reading for Democracy (2024). She has been a research fellow at Harvard University, NYU, and UC Berkeley. Current projects examine literature’s role in fostering democratic engagement and rethinking humanist concepts in American art. Laura Bieger’s research emphasizes praxeological approaches, treating literature as a social practice shaped by networks of actors and institutions. Her work on public intellectuals, cultural fields, and transnational American Studies highlights intersections between aesthetics, politics, and societal change. Collaborative projects include edited volumes on Richard Wright, spatial narratives, and the humanist imagination in American art.
Dimitris N. Metaxas is a Professor in the Department of Computer Science within the School of Arts and Sciences at Rutgers University. His research spans computer vision, medical image analysis, and artificial intelligence, with a particular focus on medical applications including cardiac MRI analysis and foundation models for healthcare. Dr. Metaxas's research interests encompass medical image analysis, computer vision, deep learning, and artificial intelligence. His work demonstrates a strong emphasis on applying advanced machine learning techniques to medical imaging problems, particularly in cardiac analysis. He has made significant contributions to diffusion models, multimodal learning, and efficient AI techniques for medical applications. His research bridges the gap between theoretical computer vision and practical healthcare solutions, with numerous publications in top-tier conferences and journals. His recent publications show a clear trend toward foundation models for medical image analysis, with significant contributions to cardiac MRI segmentation, diffusion models, and multimodal learning. The research spans both theoretical advancements in AI techniques and practical applications in healthcare, particularly focused on improving medical diagnostics through computer vision. His work demonstrates expertise in adapting cutting-edge AI techniques like diffusion models and large language models for specialized medical applications. Dr. Metaxas has mentored numerous students and researchers, as evidenced by his extensive publication record with multiple co-authors across various institutions. His work has received significant attention in the research community, with numerous publications in top venues including CVPR, ICCV, MICCAI, and Medical Image Analysis. His research group focuses on medical image computing, computer vision, and machine learning applications in healthcare. The team works extensively with cardiac MRI data, developing advanced techniques for segmentation, reconstruction, and analysis of 4D cardiac imaging. They are particularly known for their contributions to foundation models in medical imaging and efficient adaptation techniques for specialized medical tasks.
Yuanbo Xiangli is a postdoctoral researcher at Cornell University , advised by Prof. Noah Snavely. Previously, he obtained his Ph.D. from the Multimedia Lab in the Department of Information Engineering at the Chinese University of Hong Kong (CUHK) , supervised by Prof. Dahua Lin. His research focuses on 3D computer vision and deep generative modeling for urban scene reconstruction. 3D scene reconstruction from sparse images Neural rendering and Gaussian splatting Deep generative modeling for urban environments Multi-source geospatial data processing City-scale modeling and synthetic datasets His recent work includes advanced NeRF extensions (BungeeNeRF, GridNeRF), Gaussian splatting enhancements (GSDF, Scaffold-GS), and urban scene datasets (MatrixCity, OmniCity). A pioneer in combining classical vision techniques with modern deep learning approaches. ICLR 2020 Spotlight Award Collaborates with leading researchers in photorealistic rendering, including Noah Snavely and Dahua Lin. Develops systems enabling efficient 3D reconstruction from diverse data sources like satellite imagery and street-level panoramas.
