Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays
Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.
Prof. Alessandro Golkar is a Professor at the Technical University of Munich (TUM), leading the Chair of Picosatellites, Nanosatellites, and Satellite Constellations. He joined TUM in September 2022 and previously served as one of the founding faculty members at Skoltech, a Moscow-based graduate research university. His research focuses on advanced space mission concepts, systems engineering for picosatellites, and federated satellite systems. Prior to academia, he held roles at Airbus CTO, contributing to technology roadmapping and planning. His academic background includes expertise in aerospace engineering, systems design, and agile development methodologies for space hardware. Key research areas include CubeSat constellations, distributed satellite systems, and the integration of AI tools like Large Language Models (LLMs) into spacecraft design processes. He has pioneered projects such as the FSSCat mission, winner of the ESA Sentinel Small Satellite Challenge, and has explored applications of additive manufacturing for lunar missions. Prof. Golkar’s awards include the 2021 Karman Fellowship and IEEE Senior Membership (2018). His recent work emphasizes optimizing satellite networks, digital twin implementation, and orbital maneuvering for collision avoidance. He actively contributes to technology roadmapping, focusing on future human landing systems and lunar infrastructure development. Education: Ph.D. in Aerospace Engineering (details not explicitly stated). Grants & Funding: Extensive grants for CubeSat projects, federated systems research, and space technology innovation. Labs/Teams: Leads the Chair’s research group at TUM and collaborates with industry partners like Airbus on advanced mission concepts. His publications span over two decades, addressing topics like constellation design, machine learning in space, and agile processes for hardware development. He advocates for hybrid agile methodologies to bridge traditional systems engineering and modern product development.
Teruko Mitamura is a prominent researcher at Carnegie Mellon University with over three decades of contributions to natural language processing, computational linguistics, and artificial intelligence. Her work spans from foundational research in event representation to advanced applications in multimodal systems and question answering. Her research interests focus on event detection and understanding, question answering systems, information retrieval, and multimodal processing. She has made significant contributions to event coreference resolution, timeline construction, and cross-document event analysis, developing methodologies that have become standard in the field. Her work often bridges theoretical advances with practical applications, particularly in complex information environments requiring deep semantic understanding. Natural Language Processing : Specializing in event extraction, coreference resolution, and narrative understanding with over 179 publications Question Answering Systems : Developing advanced techniques for complex question answering, particularly through NTCIR QA Lab and PoliInfo tasks Multimodal Processing : Integrating textual, visual, and temporal information for richer understanding in systems like ProMQA Evaluation Methodologies : Creating robust frameworks for assessing NLP systems through TAC KBP Event Tracks Her recent publication trends show a strong focus on leveraging large language models for event understanding, multimodal question answering, and timeline construction. She has expanded her research into specialized domains including patent analysis and novelty examination, demonstrating the breadth of her research impact across academic and practical applications. Active participant in major NLP conferences including ACL, EMNLP, NAACL, and AAAI with consistent publications Long-standing collaborator with researchers at CMU's Language Technologies Institute including Eduard H. Hovy and Eric Nyberg Contributor to shared tasks that have shaped research directions in event processing and question answering Organizer of multiple NTCIR QA Lab tasks focused on political information question answering Dr. Mitamura has mentored numerous researchers who have gone on to make their own contributions to the field, as evidenced by her extensive co-authorship network and the progression of her former students and collaborators into faculty and research positions. Her work continues to evolve with the field while maintaining her focus on deep semantic understanding of events and narratives.
Professor Matthias Mann is a world-leading scientist serving as Director of the Proteomics and Signal Transduction department at the Max Planck Institute of Biochemistry in Martinsried, Germany, and Director of the Proteomics department at the Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Denmark. With an h-index exceeding 277 and over 350,000 citations, he is recognized as the highest cited German researcher and one of the most influential scientists globally in proteomics. His educational background includes: Ph.D. in Chemical Engineering from Yale University (1988) Master's Degree in Physics from Georg August University Göttingen (1984) Bachelor's of Arts in Mathematics from Georg August University Göttingen (1982) Professor Mann's research focuses on advancing mass spectrometry-based proteomics to understand biological systems at the protein level. His work spans technological developments in mass spectrometry, bioinformatics and computational analysis, signal transduction and posttranslational modifications, and clinical proteomics applications for disease diagnosis and treatment. The Mann lab has pioneered groundbreaking methods like SILAC for quantitative proteomics and MaxQuant for proteome data analysis. Their vision is to translate proteomics knowledge into clinical practice for predictive, diagnostic, and preventive medicine, with recent work focusing on AI-guided platforms for analyzing proteomes from minimal tissue samples. Analysis of Professor Mann's recent publications reveals a strong trend toward clinical applications of proteomics, particularly in cancer research, metabolic diseases, and neurodegenerative disorders. His work increasingly integrates spatial proteomics, single-cell resolution techniques, and artificial intelligence approaches to uncover disease mechanisms and identify potential biomarkers, with a clear shift from basic technology development toward direct clinical applications and personalized medicine. Professor Mann has received numerous prestigious awards throughout his career: 2025: Elected member of the American National Academy of Sciences 2024: Dr. H.P. Heineken Award for Biochemistry and Biophysics 2023: Otto Warburg Medal 2019: Nominated member of the Bavarian Academy of Sciences 2013: Elected member of Leopoldina German National Academy of Sciences 2012: Körber European Science Award, Louis-Jeantet Foundation Prize for Medicine, Ernst Schering Prize, and Leibniz Prize Professor Mann leads a highly collaborative research team involved in multiple international networks including the Bill & Melinda Gates Foundation, Michael J. Fox Foundation for Parkinson's Research, CLINSPECT-M, and Munich Heart Alliance. His lab has mentored numerous successful researchers, with several former postdocs receiving prestigious ERC Starting Grants. The Mann group has developed innovative clinical proteomics pipelines for analyzing archived tissue specimens and body fluids, aiming to identify protein markers for early detection of diseases such as diabetes and cancer. The Mann lab operates across two major research centers with state-of-the-art mass spectrometry facilities. Their Clinical Knowledge Graph platform integrates multi-omics data with extensive metadata, creating an ecosystem for machine learning applications in proteomics. Current research focuses on developing highly sensitive methods that can profile thousands of proteins from minimal cell samples, enabling the identification of critical disease-related proteins and supporting the development of individualized therapies.
Tianmin Shu is an Assistant Professor in the Department of Computer Science at Johns Hopkins University , with a joint appointment in the Department of Cognitive Science . He is the founding director of the Social Cognitive AI (SCAI) Lab and was previously a Research Scientist at MIT, working with Josh Tenenbaum and Antonio Torralba. His research goal is to advance human-centered AI by engineering machine social intelligence —building systems that understand, reason about, and interact with humans in real-world settings. His work is inherently interdisciplinary, integrating machine learning, computer vision, robotics, and social cognition. Research Interests: Theory of Mind Reasoning: Developing models that infer human mental states from multimodal behavioral data. Embodied Assistance: Creating agents capable of assisting humans in physical environments through verbal and non-verbal collaboration. Learning from Human Feedback: Extracting reward-relevant preferences from rich human input to guide agent behavior. Social Scene Understanding: Recognizing and reasoning about group activities and social roles from visual and physical cues. Computational Social Cognition: Modeling how humans perceive and interpret social and physical interactions. Scientific Awards: Outstanding Paper Award at ACL 2024 for "MMToM-QA: Multimodal Theory of Mind Question Answering" Grants & Collaborations: Tianmin Shu has led or co-organized several high-impact workshops and tutorials, including the NeurIPS 2023 Tutorial on "Language Models Meet World Models" and the RSS 2024 Workshop on "Social Intelligence in Humans and Robots". His lab has also developed open-source platforms like VirtualHome-Social and SimWorld for multi-agent interaction research. Lab & Team: As director of the Social Cognitive AI (SCAI) Lab at Johns Hopkins University, Tianmin Shu leads a multidisciplinary team focused on building socially intelligent systems. His lab is located in Malone Hall 213 and collaborates closely with the Departments of Computer Science and Cognitive Science.
Franziska Mueller is a Research Scientist at Google Zurich , specializing in Augmented Perception . Prior to joining Google, she earned her Ph.D. in Computer Science at Saarland University under the supervision of Prof. Dr. Christian Theobalt, focusing on real-time hand reconstruction from RGB and depth images. Ph.D. in Computer Science (2016-2020) at Saarland University Master’s and Bachelor’s in Computer Science at Saarland University Research visits at Stanford University (2018) and Reality Labs Research (2019) Her research emphasizes the integration of model-based techniques and machine learning components for real-time 3D hand pose estimation, occlusion handling, and hand-object interaction tracking. Key contributions include methods for single-camera reconstruction and datasets like HandSeg. Scientific Awards : Dr. Eduard Martin Award (2021) Google PhD Fellowship (2017) Günter-Hotz-Medal (2016) Bachelor Award (2015) Völklinger Abiturpreis (2012)
Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.
Boris Murmann is Professor at Stanford University, specializing in integrated circuit design, mixed-signal computing, and energy-efficient AI hardware. His research advances neural interface technologies, analog design automation, and tinyML systems. Recent work develops ultra-low-power neural recording ICs for brain-computer interfaces, RRAM-based memory systems, and open-source semiconductor design frameworks. Publications demonstrate innovations in compressive sensing for neural data, hardware-algorithm co-design, and reinforcement learning for analog circuit synthesis. Significant contributions include Medusa (TinyML processor), EMBER (RRAM macro), and methodologies for coarsely-quantized computer vision and analog design automation.
Diogo Carbonera Luvizon is a Researcher at the Max-Planck-Institut für Informatik (MPI-INF) in Saarbrücken, Germany, and a member of the Visual Computing and Artificial Intelligence (VIA) Research Center. He holds a PhD in Computer Vision and Machine Learning from CY Cergy Paris University (2019), and Bachelor's and Master's degrees in Engineering and Applied Computing from UTFPR, Brazil. His research focuses on solving complex problems in Computer Vision, Computer Graphics, and Deep Learning, particularly in human modeling and real-time systems. Education: PhD (2019) - CY Cergy Paris University; M.Sc. (2015) - UTFPR; B.Sc. (2011) - UTFPR. Research interests include 3D human pose estimation, action recognition, multitask learning, and novel view synthesis. He has contributed to patents on multiplane image generation (Samsung) and vehicle speed measurement systems. His work has been recognized with awards like the Best Paper Honorable Mention at GCPR-VMV 2022 and Best Presentation Award at ETIS Lab (2018). He has developed open-source tools, including the deephar repository for human action recognition and pose estimation. His current affiliations include MPI-INF and the VIA Research Center, a partnership between MPI-INF and Google.
Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols
Sergey Tulyakov is the Director of Research at Snap Inc. , leading the Creative Vision team. His work focuses on enhancing creator capabilities through computer vision , machine learning , and generative AI , with applications in 2D/3D/4D video generation, editing, and personalization. He pioneered video generation frameworks like MoCoGAN and First Order Motion Model , and has been recognized for BEST IN SHOW AWARD at SIGGRAPH Real-Time Live! 2020. PhD (2012-2017): University of Trento, Italy MSc (2010): Belorusian State University of Informatics and Radioelectronics B.Eng (2009): Belorusian State University of Informatics and Radioelectronics His research interests span computer vision , generative models , 3D reconstruction , and personalization , with a focus on making large models efficient and mobile-compatible . Recent publications highlight advancements in 4D video generation , text-guided 3D composition , and lightweight architectures . Key scientific awards include the SIGGRAPH Real-Time Live! 2020 Best in Show for Interactive Video Stylization. He has also served on technical program committees for top-tier conferences like CVPR, ICCV, SIGGRAPH, and NeurIPS since 2022. His team organizes tutorials and keynotes, including courses on Deep Generative Models and Efficient Neural Networks . While no direct student names are listed, his collaborative work spans 60+ top-tier publications.
Prof. Dr. Jakob Beetz serves as a University Professor at RWTH Aachen University's Faculty of Architecture, leading the Design Computation (DC) research group. His work addresses critical challenges in sustainable built environments through digital innovation, focusing on integrating knowledge, information, and data across disciplines to reduce the sector's energy and material consumption—which accounts for over one-third of global totals—while advancing climate goals under the European Green Deal. His research spans Building Information Modeling (BIM), digital twins, and artificial intelligence, with emphasis on graph-based data federation, semantic web technologies, and large language models in construction. Key interests include evidence-based planning, parametric design optimization, building physics simulation, and networked knowledge modeling. Recent projects explore federated digital twin ecosystems for infrastructure management, intelligent damage assessment systems, and AI-driven solutions for wood structure preservation, directly contributing to sustainable development targets. Analysis of his 2024-2025 publications reveals a cohesive trajectory toward decentralized data environments and AI integration in Architecture, Engineering, and Construction (AEC). His work bridges theoretical foundations in knowledge representation with practical applications in bridge maintenance, road infrastructure, and timber construction, demonstrating consistent innovation in spatial data querying, federated issue management, and ontology-based process modeling. Prof. Beetz actively supervises PhD candidates, as evidenced by DC.Promotions 2024, and drives international collaboration through events like the Forum Construction Informatics 2025 and CIB W78 conferences. His research group engages with industry standards including Industry Foundation Classes (IFC) and Common Data Environments (CDEs), emphasizing open data principles and interoperability to transform construction workflows.
Prof. Dr.-Ing. H. Siegfried Stiehl is a retired Senior Professor (until Sept 2021) at the Department of Informatics, University of Hamburg. He previously held roles including Dean of the Faculty of Mathematics, Computer Science, and Natural Sciences (2001–2006), Vice President for Research (2007–2013), and Head of the Image Processing Research Group. His academic journey includes a PhD from TU Berlin (1980) and a Habilitation in Computer Vision (1987). Education: 1973: Ing. Degree in Ingenieur-Informatik, Fachhochschule Furtwangen 1976: Diploma in Computer Science, TU Berlin 1980: Dr.-Ing. Dissertation on medical image processing, TU Berlin Research focuses on Computer Vision , Computational Neuroscience , and Cognitive Science , with contributions to medical image registration, 3D landmark detection, and biomechanical modeling. Key projects include the EU-funded 'COVIRA' consortium (1989–1995) and leadership in the SFB 950 'Manuscript Cultures' project (2015–2019). His 110+ publications span biomedical image registration, elastic deformation algorithms, and real-time signal processing. Notable collaborations include work with institutions like the University of Pennsylvania, University of Birmingham, and Philips Research. Leadership roles include organizing scientific events, serving on editorial boards (e.g., Biological Cybernetics), and founding the Interdisciplinary Nanoscience Center Hamburg (INCH) in 2001. His research has addressed challenges in neurosurgical interventions, VLSI implementation of neural networks, and interdisciplinary education.
Dr. Marcell K. Peters is a Senior Academic Councillor at the Chair of Animal Ecology and Tropical Biology (Zoology III) at the University of Bremen. His research focuses on biodiversity patterns, ecosystem functioning, and climate-land use interactions in tropical and montane environments, with extensive fieldwork in East Africa and the Amazon. He leads projects under DFG and EU funding, including the UPSCALE initiative. Habilitation in Zoology (University of Würzburg, 2018) PhD in Biology (University of Bonn, 2008) Diploma in Biology (RWTH Aachen & University of Bonn, 2003) Research spans multi-taxa community ecology, army ants and ant-following birds, DNA barcoding applications, and climate change impacts on pollination networks. Google Scholar highlights recent work on climate-agriculture interactions in sub-Saharan Africa, trait-based community assembly, and network resilience in biodiversity hotspots. His publications emphasize elevational gradients, disturbance ecology, and functional diversity across Mount Kilimanjaro studies. Current affiliations include the DFG Research Unit Kilimanjaro and EU-funded UPSCALE project. He employs advanced methods like airborne LiDAR for biodiversity prediction and investigates nutrient use by ant communities across continents.
