Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow in the Department of Computing at Imperial College London. As the Principal Investigator (PI) of the CIRCLE group , his research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with theoretical interests in non-Euclidean inference and deep learning principles. Education: PhD and MSc in Computer Vision from Technical University of Munich (2018), BSc in Computer Science from Sabancı University (2008). Projects: PI for UKRI-EPSRC's UNTOLD (Topological Deep Learning), Royal Society's drug discovery initiative, and EPSRC's GNOMON (Generative Models in non-Euclidean Spaces). Leadership: Area Chair for CVPR, ICCV, and 3DV 2025 Publication Chair. His work bridges differential geometry, algebraic topology, and deep neural networks, with applications in quantum computer vision, 3D/4D generative priors, and medical imaging. Key contributions include novel frameworks for rotation forecasting, graph generation, and topological generalization bounds. Scientific Awards: UKRI Future Leaders Fellowship EMVA Young Professional Award
Ángel García-Fernández is an Associate Professor at the Polytechnic University of Madrid , specializing in Bayesian inference , multi-target tracking , and nonlinear filtering with applications in signal processing, robotics, and underwater mapping. His work includes the development of Poisson multi-Bernoulli mixture (PMBM) filters, iterated posterior linearization algorithms, and direction-of-arrival (DOA) measurement models.
Karteek Alahari is a Researcher (Directeur de Recherche) at Inria Grenoble - Rhône-Alpes, leading the Thoth project-team. He also holds associate memberships with the Visual Geometry Group at the University of Oxford and the WILLOW team at École Normale Supérieure (ENS). His research focuses on visual understanding with large-scale datasets, particularly learning robust visual representations under partial supervision, spanning frameworks like incremental learning, weakly-supervised learning, and adversarial training. Research Focus His work addresses challenges in computer vision , including semi-supervised instance segmentation , unsupervised video domain adaptation , and self-supervised contrastive representation learning . He explores intersections between deep learning and graphical models , emphasizing spatio-temporal relations in videos and geometric models for object recognition. Scientific Leadership & Recognition Deputy Scientific Director in charge of AI at Inria (2024) Co-Director of the PEPR IA research programme (2024) Area Chair for CVPR 2025, ECCV 2024, ICCV 2023, and others Editorial board member of IJCV (2019–2023) and CVIU (2018–2023) Students & Collaborations He has co-advised numerous PhD students, including Jules Bourcier (2021–2024), Lina Mezghani (2019–2023), and Zhiqi Kang (2021–present). Former advisees like Anand Mishra and Yang Hua are now faculty members at IIT Jodhpur and Queen’s University Belfast, respectively.
Dr. Jemil Avers Butt is a Lecturer at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering (D-BAUG), specializing in Geomatics and Geodetic Engineering. His research focuses on advanced geospatial technologies, including terrestrial radar interferometry, LiDAR sensing, and MIMO-SAR for structural and environmental monitoring. He develops novel methods for improving sensor accuracy, data fusion, and deformation analysis in civil engineering applications. His work addresses challenges in monitoring infrastructure such as bridges, wind turbines, and dams, leveraging machine learning and probabilistic modeling to enhance data interpretation. Recent projects include RGB-guided 3D displacement estimation, self-calibration of laser scanners, and phase ambiguity resolution in electronic distance measurements. He teaches courses like 'Computational Methods for Geospatial Analysis' and 'Project Parameter Estimation,' reflecting his expertise in both theoretical and applied aspects of geomatic engineering.
Charbel Toumieh is a Research Fellow at the École Polytechnique Fédérale de Lausanne (EPFL), based in the Intelligent Systems Laboratory (LIS) under the School of Engineering (STI). His research focuses on advanced robotics, particularly in aerial systems, motion planning, and autonomous systems. He holds a postdoctoral position and contributes to projects involving multi-agent coordination, high-speed navigation, and energy-efficient drone designs. Key research areas include teleoperation of aerial swarms, adaptive morphing for avian-inspired drones, and decentralized multi-agent planning. His work addresses challenges in cluttered environments, dynamic obstacle avoidance, and real-time trajectory optimization. The LIS lab, part of the Institute of Microengineering (IGM), emphasizes innovative solutions in intelligent systems and robotics. Recent publications highlight advancements in motion planning algorithms, safe corridor generation using voxel grids, and GPU-accelerated exploration techniques. His research bridges theoretical control systems with practical applications in autonomous robotics, aiming to enhance efficiency and resilience in robotic systems.
Torben Peters is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on 3D computer vision, deep learning, and generative models applied to geospatial analysis and photogrammetry. Research Focus: Peters develops computational tools for processing LiDAR point clouds, aerial imagery, and satellite data. His work enables automated environmental monitoring (e.g., forest inventories and avalanche mapping) and urban modeling through advanced segmentation and 3D reconstruction techniques. Generative models like TetraDiffusion expand capabilities in geometric deep learning. Publication Trends: Recent articles emphasize scalable geospatial AI, including war damage assessment in Ukraine, global biomass datasets, and self-supervised shape completion. Methodological innovations center on reducing annotation dependencies and improving geometric accuracy. Teaching: Leads courses on image-based mapping and geodetic data processing at ETH Zürich.
Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.
Federico Stella is a Researcher and Doctoral Assistant at the Computer Vision Laboratory (CVLAB) within the School of Computer and Communication Sciences (IC) at EPFL in Lausanne, Switzerland. His work focuses on advancing computer vision and 3D geometry processing through machine learning and neural network approaches. Research interests include implicit neural representations, 3D reconstruction, and applications of spherical CNNs for geometric problems. He has contributed to methodologies in mesh generation, surface detection, and self-supervised learning techniques. Recent publications highlight advancements in distance field networks, gradient-based surface detection, and differentiable meshing algorithms. His research bridges computer graphics and machine learning, with potential applications in robotics and virtual environments.
Dr. Francis Xiatian Zhang is a Research Fellow at the University of Edinburgh's Deanery of Clinical Sciences, developing visual navigation systems for bronchoscopic robotics. He completed his PhD in Computer Science at Durham University, focusing on geometric information integration in biomedical video analysis. Research spans bronchoscopic robotics vision, surgical workflow anticipation, and medical video analysis using geometric deep learning approaches. Recent work includes BREA-Depth for airway-geometric depth estimation in bronchoscopy. Articles demonstrate specialization in adapting computer vision techniques to medical contexts, particularly real-time applications in surgical environments. Key contributions include video inpainting methods and graph-based surgical workflow models. EPSRC Grant: U-care: Deep Ultraviolet Light Therapies (Research Associate) UK-Egypt TNE Grant: Course Development in Edge Computing (Research Assistant) Leads development of novel deep learning-based navigation for bronchoscopic robotics. Previously contributed to pose estimation for health professional education and edge computing curriculum development.
Dr. Jan Petrik is a full-time faculty member at ETH Zürich, affiliated with the Professorship for Advanced Manufacturing. His research focuses on integrating artificial intelligence with manufacturing processes, particularly in deep learning, reinforcement learning, and computer vision applications for metal forming and additive manufacturing systems. Current position: Professor, Advanced Manufacturing, ETH Zürich Research interests: AI-driven manufacturing optimization, microstructural control, and process modeling Recent work: Development of AI frameworks like DeepForge, RLTube, and CrystalMind for metal forming and additive manufacturing
Luca Di Grazia is a Researcher (Postdoctoral) in the STAR group at the University of Lugano (USI), Switzerland, supervised by Prof. Mauro Pezzè. He holds a PhD (summa cum laude) in Software Engineering from the University of Stuttgart, advised by Prof. Michael Pradel. Previously, he completed his Bachelor's and Master's degrees in Computer Engineering at the Polytechnic of Turin, Italy, with a minor in Embedded Systems. His research focuses on Generative AI, Program Repair, Software Evolution, and Code Search techniques. Education: Bachelor's and Master's in Computer Engineering, Polytechnic of Turin (Italy), with a thesis on "Protein classification using geometrical features for 3D face analysis". PhD in Computer Science (summa cum laude) from University of Stuttgart (Germany), thesis: "Supporting Software Evolution via Search and Prediction". Postdoctoral Researcher at USI, Switzerland. Research Interests: Generative AI for software testing and bug fixing (e.g., winning an Uber competition with a GenAI tool). Program Repair techniques, such as PyTy for Python type errors. Code Search and Change Retrieval (e.g., DiffSearch engine). Software Evolution and Type Annotation studies in Python. Achievements: ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2022 for work on Python type annotations. Second prize at ACM Student Research Competition at ICSE 2022. Won GenAI Uber competition (2023) with a project to boost developer productivity, beating 103 teams. Summa cum laude PhD (2024). Recipient of national scholarships during his studies at Polytechnic of Turin. Advising: Supervised seven students on projects including automated error repair and testing frameworks. Labs/Teams: STAR group at USI, collaborating with JetBrains and Uber.
Robin Bruneau is a Postdoctoral Researcher at the University of Zurich (UZH) in the Department of Quantitative Medicine. His research focuses on advancing multi-view 3D reconstruction techniques, particularly integrating normal and reflectance cues. He holds a PhD co-supervised between DIKU (University of Copenhagen) and IRIT (University of Toulouse), completed in 2024, and a Master's degree in Computer Science from Enseeiht (2017–2020). His work emphasizes high-fidelity surface reconstruction under challenging conditions, such as complex materials and refractive interfaces. Key applications include archaeological object digitization and transparent material modeling. Bruneau's research has been supported by grants from the Department of Quantitative Biomedicine at UZH, the French National Research Agency (ANR) via the ALICIA-Vision project, and DIKU's Copenhagen Data+ initiative. Collaborations include partnerships with institutions like the Musée Saint-Raymond (Toulouse) for archaeological data acquisition and OR-X (Switzerland) for translational surgery applications. His publications span top venues in computer vision, including CVPR and SPIE, with a focus on benchmarking datasets like DiLiGenT-MV and Skoltech3D. Notable contributions include the RNb-NeuS framework, which achieves state-of-the-art performance in multi-view photometric stereo.
Alexandre Alahi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Visual Intelligence for Transportation (VITA) laboratory. He is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), the Institute of Infrastructure (IIC), and also contributes to diversity initiatives at ENAC. His research focuses on integrating computer vision, machine learning, and robotics to develop socially-aware AI for transportation and autonomous systems. University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Architecture, Civil and Environmental Engineering Department: Institute of Infrastructure, IIC Research Lab: Visual Intelligence for Transportation (VITA) Alexandre's research interests center on computer vision, machine learning, robotics, and AI safety, particularly in human trajectory prediction, depth estimation, and socially-aware autonomous navigation. He investigates how AI can understand and predict human behavior in complex environments to improve safety in mobility systems. His work bridges theoretical advances with real-world applications in autonomous driving, urban planning, and healthcare. His recent publications span a wide array of topics including omnidirectional stereo matching, trajectory forecasting, cross-view localization, AI security, and depth estimation. These works demonstrate a strong trend toward building generalizable, robust, and socially-compliant AI systems, with increasing focus on uncertainty quantification, safety certification, and real-world deployment. The integration of multimodal data and the development of foundation models are recurring themes. Alexandre has received numerous scientific accolades, including: Top 100 Most Influential Scholar in Computer Vision (2022–2023) Editor’s Choice Award, Image and Vision Computing (2021) Honorable Mention, ICCV Workshop (2019) CVPR Open Source Award (2012) ICDSC Challenge Prize (2009) Top 20 Swiss Venture Leaders (2010) He has advised numerous PhD students whose theses cover diverse topics such as human motion prediction, person re-identification, trajectory forecasting, and AI security. His lab has secured significant recognition and funding, enabling impactful research with real-world applications. Alexandre has also co-founded startups like Visiosafe, demonstrating strong industry engagement and technology transfer. The VITA lab fosters interdisciplinary collaboration, working across computer vision, robotics, transportation engineering, and human-centered AI. The team develops datasets, benchmarks, and open-source tools to advance the field and promote reproducibility.
Amir Zamir is a Tenure-Track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology (EPFL) and leads the Visual Intelligence and Learning Lab (VILAB). Previously affiliated with UC Berkeley, Stanford, and UCF, his research spans computer vision, machine learning, and AI, focusing on embodied intelligence and multimodal learning. Current Appointments: EPFL (Tenure-Track Assistant Professor) Industry Experience: Chief Scientist at Aurora Solar (2015-2022), Chief Scientist at Duranta His research emphasizes advancing vision systems beyond narrow approaches toward general multi-modal/multi-task models that operate in real-world environments. Key projects include 4M (Multimodal Foundation Models), Taskonomy (Task Transfer), Gibson (Sim-to-Real), and Omnidata (Steerable Datasets). Current work explores flexible-length tokenization (FlexTok), visual personalization (ViPer), and robust cross-domain learning . Recent publications demonstrate expertise in autoregressive image generation , vision-language modeling , and embodied robotics . Awards include the ECCV Young Researcher Award , NVIDIA Pioneering Research Award , and multiple CVPR/SIGGRAPH best paper recognitions . He advises PhD students in vision systems, AI, and robotics, and teaches courses on Visual Intelligence, AI Product Management, and Autonomous Robotics.
Hanchen Wang is a Postdoctoral Research Fellow at Stanford AI Lab and Genentech, working under Jure Leskovec and Aviv Regev. He holds a PhD in Computer Science from Cambridge University completed in 3 years under Joan Lasenby, and a BS in Physics from Nanjing University where he was valedictorian. His research bridges artificial intelligence and biomedical discovery, with appointments spanning both academic and industry settings. Wang's research focuses on AI for Science , particularly developing autonomous agents for biomedical discovery. His work spans multi-omics analysis , spatial transcriptomics , live-cell imaging , and perturbation assays , with applications in cancer therapeutics , autoimmune diseases , and neurological disorders . He has pioneered multiple AI agent frameworks including Biomni (a general-purpose biomedical agent), SpatialAgent, and PerTurboAgent for specialized biological discovery tasks. His publication record demonstrates significant impact across both computer science and biology venues, with first-author papers in Nature , Nature Biotechnology , and NeurIPS . His research has been deployed by Anthropic, Amazon Web Services, and Genentech, and featured in Nature , The Economist , and DeepMind communications. Chan Zuckerberg Initiative Faculty Applicant Bootcamp participant UCSF Gladstone Institute Trainee-to-Tenure Track Program member OpenAI Researcher Access Program recipient Multiple conference travel awards Wang actively mentors early-career researchers including PhD students from institutions like CSHL, MIT, Harvard, and KAIST. He serves as Area Chair for ICLR 2026, organizes workshops on AI for Science at major conferences, and gives invited talks at leading institutions including Harvard, Yale, and the Broad Institute. His research is supported by Genentech internal funding ($500k/year) and OpenAI resources.
