Swiss Federal Institute of Technology in LausanneSwitzerland
Bassam El Rawas is a Researcher at the Biomedical Imaging Laboratory (LIB) within the Institute of Electrical Engineering (IEM) at École Polytechnique Fédérale de Lausanne (EPFL). He is currently enrolled in the Doctoral Program in Electrical Engineering and holds a Doctoral Assistant position. His work focuses on biomedical imaging and electrical engineering. He is affiliated with the School of Engineering and based in the BM Building , Lausanne, Switzerland. Outside EPFL, he serves on external committees for AGEPoly (Representation Team and Services Team).
Swiss Federal Institute of Technology in LausanneSwitzerland
Slavica Jonic is a Research Director at CNRS, affiliated with Sorbonne University at the Institute of Mineralogy, Materials Physics, and Cosmochemistry (IMPMC-UMR 7590) in Paris, France. She obtained her PhD in Image Processing from EPFL (Switzerland) in 2003 and a Research Director Habilitation from UPMC (France) in 2015. She leads the 'Image analysis for biomolecular structural and dynamics studies' subgroup under the BiBiP team and co-leads the IMPMC transversal axis on 'Theory, Artificial Intelligence, Big Data.' Her research focuses on developing algorithms that integrate image analysis , molecular mechanics simulation , and artificial intelligence to explore conformational dynamics of biomolecules via in vitro and in situ cryo-EM and cryo-ET. Key software contributions include ContinuousFlex , HEMNMA , StructMap , MDTOMO , and MDSPACE , which are used to study complexes like nucleosomes, ribosomes, SARS-CoV-2 spike, and ATPase p97. Her work is supported by ANR , CNRS , Sorbonne University , and GENCI . She mentors PhD students and Master interns and serves on editorial boards for journals like BMC Methods and Frontiers in Molecular Biosciences . She also organizes workshops on cryo-EM and AI.
Swiss Federal Institute of Technology in LausanneSwitzerland
Ravi Kiran Sarvadevabhatla is an Associate Professor at the International Institute of Information Technology Hyderabad (IIIT-H), with additional roles as Lead (Applied Projects and Academic Programs) at iHub-Data Mobility. His research spans Computer Vision , Machine Learning , and Human-Robot Interaction , focusing on interdisciplinary challenges involving multimodal data (images, sketches, eye-tracking, audio). Research Themes : Computer vision projects in Document Image Understanding , Sketch Recognition , and Depiction-Invariant Object Recognition Cognitive modeling through Eye Tracking Analysis and Pictionary-inspired Models Human-robot interaction studies on Proxemics , Panoramic Attention , and Extended Interaction Analysis Technical Contributions : Pioneered Category-Epitome representations for sparse sketch recognition Developed SketchParse - a deep network for automated sketch parsing Created PPSS-12 benchmark for evaluating object recognition robustness
Swiss Federal Institute of Technology in LausanneSwitzerland
Roger Hersch is a current researcher affiliated with the École Polytechnique Fédérale de Lausanne (EPFL) . His scholarly work spans disciplines including photonics, optical engineering, and computer graphics, with significant contributions to color imaging and electronic imaging technologies. Affiliation: PH-IC (Physics Section, Institute of Condensed Matter Physics) Units: Photonics Systems Laboratory (LSP), Vision Processing and Robotics Laboratory (IVRL), and others His research focuses on photonic systems , imaging science , and optical engineering , as reflected in his publications in journals like the Journal of Imaging Science and Technology and IEEE Computer Graphics and Applications . While specific article titles are not listed here, his work trends toward interdisciplinary applications in optical engineering and computational imaging. As a prolific academic, he has contributed to over 250 publications, primarily in conference papers and research articles, and holds 24 patents. His roles include collaborations with units such as ISIM (Integrated Systems for Microelectronics) and AVP-R-TTO (Research and Technology Transfer Office).
Swiss Federal Institute of Technology in LausanneSwitzerland
Daniel Thalmann is a distinguished Professor at École Polytechnique Fédérale de Lausanne (EPFL), specifically within the School of Computer and Communication Sciences and the Institute of Computer Science. As a current EPFL member, he leads the Virtual Reality Laboratory (VRLAB) and has established himself as a pioneer in virtual reality research with decades of international leadership in the field. His academic contributions span numerous publications, including the influential book "Stepping into Virtual Reality" (2023), which serves as both a textbook and reference for VR applications. Professor Thalmann's research interests encompass a broad spectrum of virtual reality and related technologies, with particular emphasis on computer animation, crowd simulation, virtual humans, and human-computer interaction. His work demonstrates exceptional depth in understanding how virtual environments can be designed to interact naturally with users, simulate complex social scenarios, and extend human capabilities through immersive technologies. He has made significant contributions to the theoretical foundations of VR while maintaining strong practical applications across various domains. The most recent publications reveal a clear progression in Thalmann's research trajectory, moving from core VR technologies toward increasingly sophisticated applications that integrate artificial intelligence, multi-sensory experiences, and real-world problem solving. His work now spans health sciences applications, brain-computer interfaces, sensory systems including smell and taste simulation, and the architectural foundations of the metaverse. This evolution demonstrates both continuity in his core expertise and strategic expansion into emerging application areas where virtual reality can have transformative impact. Through the Virtual Reality Laboratory at EPFL, Professor Thalmann has fostered numerous collaborations across disciplines and institutions, contributing significantly to the global VR research community. His leadership extends to organizing major international conferences including the Computer Graphics International (CGI) and Computer Animation and Social Agents (CASA) events, where he has served as editor and contributor to multiple proceedings volumes. The laboratory continues to advance research in social robotics, virtual humans, and immersive environments, with applications ranging from healthcare to cultural heritage preservation.
Swiss Federal Institute of Technology in LausanneSwitzerland
Neslihan Bayramoglu serves as a senior researcher at the Research Unit of Health Sciences and Technology within the Faculty of Medicine at the University of Oulu, Finland. Awarded the title Docent of Medical Imaging in November 2022, she holds an academic rank equivalent to Associate Professor in the Finnish system. Her research integrates artificial intelligence with medical applications, specializing in deep learning architectures for facial expression recognition, computer-assisted diagnostic systems, and advanced image processing techniques. Key methodological contributions span shape analysis, segmentation algorithms, classification frameworks, and 3D image retrieval systems – all targeting clinical translation in healthcare settings. This work bridges theoretical machine learning innovations with practical medical imaging solutions. Currently active in academic leadership, she serves as MICCAI2025 Area Chair (announced January 16, 2025) and recruits postdoctoral researchers and MSc thesis students as of early 2025. Her publication record is maintained on Google Scholar with ongoing contributions to medical AI literature.
Swiss Federal Institute of Technology in LausanneSwitzerland
Csaba Benedek serves as a Full Professor at the Faculty of Information Technology and Bionics, Péter Pázmány Catholic University (PPKE ITK) in Budapest and Deputy Director for scientific coordination at the Hungarian Research Network Institute for Computer Science and Control (HUN-REN SZTAKI). He leads the Geo-Information Computing (GeoComp) research group within SZTAKI's Machine Perception Research Laboratory, where he holds the position of Scientific Advisor (DSc). Additionally, he serves as Vice Chairman of the John von Neumann Computer Society and represents Hungary in several international academic organizations including the International Association of Pattern Recognition. His academic credentials include: DSc (Doctor of the Hungarian Academy of Sciences) in Engineering Sciences (Information Science), October 2020 Dr. habil., October 2017, Pázmány Péter Catholic University Ph.D. in Image Processing with Summa cum Laude, June 2008 M.Sc. in Computer Sciences with honors, June 2004 Dr. Benedek's research focuses on the interpretation and reconstruction of dynamic urban scenes from LIDAR point cloud sequences using aerial measurements and mobile/terrestrial data. His work spans medical image analysis, remote sensing, and pattern recognition using probabilistic and machine learning approaches. He has published the book 'Multi-Level Bayesian Models for Environment Perception' through Springer in 2022. His research methodology emphasizes mathematical modeling, Bayesian approaches, hierarchical scene analysis, and stochastic optimization techniques for change detection and 3D/4D reconstruction. His major scientific recognitions include: Master Teacher Golden Medal (2023) Michelberger Master Prize (2020) Bolyai Plaquette (2019) IEEE Senior Member status (2018) Multiple Publication Awards from SZTAKI Janos Bolyai Research Fellowships As an academic advisor, Dr. Benedek has successfully supervised numerous PhD students including Attila Börcs (2018), Balázs Nagy (2020), and Yahya Ibrahim (2023), with several current PhD candidates. He has served as principal investigator for multiple Hungarian Scientific Research Fund (OTKA) projects and leads various National, EU-funded, EDA and ESA projects including the current ESA project 'AI based Fusion of Satellite/Airborne data for Biodiversity Change Characterization' (2023-2025) and NKFIA OTKA project (2022-2026). Dr. Benedek's GeoComp research group maintains extensive international collaborations with institutions including TUDelft in the Netherlands, Australian Centre for Field Robotics, CSIRO in Australia, DLR Oberpfaffenhofen in Germany, and University of Pisa in Italy. His team specializes in developing advanced techniques for urban scene perception, multisensorial spatial data analysis, and practical applications in urban planning, medical imaging, and defense technologies.
Swiss Federal Institute of Technology in LausanneSwitzerland
Adel Bibi is a prominent Research Fellow at the University of Oxford 's Department of Engineering Science, with concurrent roles at Kellogg College and the ELLIS Society . He serves as R&D Distinguished Advisor for Softserve , specializing in AI Safety through robustness certification and optimization. Academic Background: PhD in Electrical Engineering (4.0/4.0), KAUST (2020) MSc in Electrical Engineering (4.0/4.0), KAUST (2016) BSc in Electrical Engineering (3.99/4.0), Kuwait University (2014) His research focuses on Trustworthy AI through three main pillars: AI Safety : Specializing in robustness certification, alignment, and security of foundational models in vision/language Continual Learning : Developing efficient frameworks for model updates and domain adaptation Optimization : Creating novel approaches for training stability and resource allocation Recent publications demonstrate expertise in large language model security , interpretable architectures , and robust training , with a particular focus on mitigating risks in agentic systems. Scientific Recognition: Systemic AI Safety Grant (~$250,000) - UK AI Security Institute (2025) Google Gemma 2 Academic Program - $10,000 GCP Credit (2024) Amazon Research Award - Machine Learning Algorithms & Theory (2022) 4 Best Paper Awards (NeurIPS23, ICML23, CVPR22, 2018 Optimization & Big Data) Notable Area Chair Award - NeurIPS23 (top 8.1%) He actively mentors through: 13 PhD/MSc students at Oxford (including 4 graduated PhDs) 3 Unofficial PhD Mentees at KAUST/MIT Supervision of 6 Graduated MSc Students
Swiss Federal Institute of Technology in LausanneSwitzerland
Dr. Marcel Binz is a research scientist and deputy head at the Institute for Human-Centered AI at Helmholtz Munich. His work bridges machine learning and cognitive science to develop foundation models of human cognition, aiming to unify theories of human behavior through computational modeling.
Swiss Federal Institute of Technology in LausanneSwitzerland
Haoyu Chen is a Tenure-track Assistant Professor at the Center for Machine Vision and Signal Analysis (CMVS), University of Oulu. He is also a co-founder of the AI startup Aitomore and an AI-based restaurant Aitofresh. His research focuses on Machine Learning, Human Behaviour Analysis, Emotion AI, and Adversarial Learning, with particular emphasis on Hybrid Intelligence and human-AI interaction. Dr. Chen received his Ph.D. from the University of Oulu, Finland, where he was advised by Academy Professor Guoying Zhao. During his PhD studies, he visited CEL, TU Delft, the Netherlands. Prior to that, he received his B.E. degree from China University of Geosciences, China, and Master degree from University of Oulu, Finland. Before joining as faculty, he conducted Postdoc research in CMVS, University of Oulu, with projects on Emotion AI (Academy Finland project) and trustworthy AI (Infotech project). His research spans multiple areas of artificial intelligence with a focus on understanding human behavior through AI systems. Dr. Chen's work particularly emphasizes emotion recognition, 3D pose transfer, micro-gesture analysis, and the development of hybrid intelligence systems where humans and AI mutually enhance each other's capabilities. His recent work has increasingly incorporated large language models for multimodal understanding of human emotions and behaviors, with significant publications in ICML, CVPR, and NeurIPS. Academy Research Fellow funding (622k euros), ranked 1st in AI panel ELLIS member IEEE Finland Jt. Chapter SP/CAS Best Paper Award Dr. Chen actively supervises multiple students including PhD candidates, Master's students, and research interns. His teaching includes courses on Affective Computing and Computer Graphics at the University of Oulu. He also organizes workshops and seminars, including the MiGA workshop series on Micro-gesture Analysis for Hidden Emotion Understanding and the HAECU tutorial on Human-AI mutual promotion for emotion and cognition understanding.
Swiss Federal Institute of Technology in LausanneSwitzerland
Prof. Dubravko Culibrk is a Full Professor at the Faculty of Technical Sciences , University of Novi Sad, Serbia. He is also an NVIDIA Deep Learning Institute University Ambassador and a Tandemlaunch startup foundry Ambassador. His academic work focuses on AI education and research. Current roles: Full Professor (since 2018), Senior Research Scientist at Tandemlaunch (since 2018), Founder of AI Research Institute (2021) Research interests span Deep Learning , Computer Vision , and Multimedia Processing . He has supervised 9 Bachelors and 9 Masters theses, and mentored 4 PhD students to completion. As an NVIDIA University Ambassador, he organizes free Deep Learning workshops for academic communities. He previously served as a postdoc researcher at the University of Trento (2013-2015), contributed to Caffe framework development, and founded companies including Coretex Systems (USA) and Panonit d.o.o. (Serbia).
Swiss Federal Institute of Technology in LausanneSwitzerland
Prof. Fabio Galasso heads the Perception and Intelligence Lab (PINLab) at the Department of Computer Science, Sapienza University of Rome. Previously, he founded and directed the Computer Vision Department at OSRAM in Munich, Germany, and conducted research at the University of Cambridge and Max Planck Institute for Informatics. His educational background includes a Master's Degree cum laude from RomaTre University and a PhD from the University of Cambridge, Department of Engineering. Prior to his academic career, he worked as a Researcher at Ericsson Laboratories and as a Project Engineer at Telecom Italia. Prof. Galasso's research focuses on fundamental aspects of computer vision and machine learning, with particular interest in distributed and multi-agent intelligent systems, perception tasks including detection, recognition, re-identification, and forecasting, and general intelligence encompassing reasoning, meta-learning, and domain adaptation. His work emphasizes sustainable AI frameworks with low-power consumption and constrained computational resources, as well as interpretable and verifiable AI systems. Earlier in his career, he conducted significant research on video analysis and segmentation, scene understanding, clustering, and 3D reconstruction from texture. His recent publications demonstrate strong contributions across multiple cutting-edge areas in computer vision, with a clear progression from fundamental research on video segmentation and texture analysis to practical applications in human motion forecasting, person search, and anomaly detection. His work consistently bridges theoretical computer vision with practical applications in smart lighting, retail, and city infrastructure. 2019 IoT/WT Innovation World Cup 2019 Digital Champions Award 2018 Deutscher Digital Award Prof. Galasso has coordinated a Marie Sklodowska-Curie Actions project (Horizon 2020) and served as Principal-Co-Investigator in multiple German-funded projects. He is actively involved in the academic community, serving as area chair for major conferences including NeurIPS, ECCV, and CVPR, and organizing workshops on specialized topics in computer vision. His leadership in the Perception and Intelligence Lab drives innovation in both theoretical understanding and practical implementations of computer vision technologies.
Swiss Federal Institute of Technology in LausanneSwitzerland
Nicolas Gillis is a Professor in the Department of Mathematics and Operational Research at the Faculty of Engineering, University of Mons, Belgium. His research focuses on theoretical and applied aspects of low-rank matrix approximations, nonnegative matrix factorization (NMF), optimization, and computational complexity, with applications in machine learning, data mining, and hyperspectral imaging. Research Interests: Low-rank matrix approximations, NMF, numerical linear algebra, machine learning, and signal processing. Publications: Authored the first comprehensive book on NMF, available in print and digital formats, with accompanying MATLAB code on GitLab. Teaching: Created a doctoral course on NMF with 6 lectures and 4 exercise sessions for the SOCN doctoral school. Upcoming Events: Organizing the third workshop on Low-Rank Models and Applications (LRMA 25) in September 2025, and the SOCN study day in Mons.
Swiss Federal Institute of Technology in LausanneSwitzerland
Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.
Swiss Federal Institute of Technology in LausanneSwitzerland
Rostyslav Hryniv serves as Professor of Applied Mathematics and Statistics at Ukrainian Catholic University (UCU), holding dual leadership roles as Deputy Dean for Research and Head of the Machine Learning Laboratory. His academic profile uniquely bridges rigorous mathematical physics with cutting-edge artificial intelligence applications, reflecting UCU's interdisciplinary research vision. Hryniv's educational foundation includes dual PhDs in Mathematics (1996, 2007) and a higher doctorate (dr hab.) from the Institute of Mathematics of the Polish Academy of Sciences (2009). His postdoctoral trajectory features prestigious appointments as a Humboldt Research Fellow at the University of Bonn (2003-2005) and PIMS Postdoctoral Fellow at the University of Calgary (1998-1999), complemented by teaching roles across institutions in Ukraine, Canada, Poland, and the United States. His research demonstrates a strategic evolution from foundational work in spectral theory and inverse problems to contemporary machine learning. Early career contributions focused on Schrödinger operators with singular potentials, trace formulas, and quantum scattering phenomena. Recent publications (2021-2025) reveal a decisive pivot toward applied AI, including Ukrainian NLP benchmarks, symmetry-based 3D reconstruction, and minimal solvers for computer vision. This transition exemplifies his commitment to leveraging deep mathematical insights for real-world technological challenges. Analysis of his publication trends indicates a clear interdisciplinary trajectory: while maintaining mathematical rigor, his work increasingly addresses Ukrainian language technology and geometric deep learning. The Machine Learning Laboratory under his direction has become a regional hub for AI innovation, particularly in developing resources for low-resource languages and symmetry-aware computer vision systems. Hryniv's scholarly recognition includes: Humboldt Research Fellowship (University of Bonn, 2003-2005) PIMS Postdoctoral Fellowship (University of Calgary, 1998-1999) As Head of UCU's Machine Learning Laboratory, Hryniv leads research initiatives spanning Ukrainian NLP, 3D vision, and quantum machine learning. The lab's recent focus on symmetry-based algorithms and Ukrainian language resources positions it at the forefront of culturally contextual AI development in Eastern Europe, while maintaining connections to his foundational work in mathematical physics through quantum-inspired computing approaches.
