جستجوی استاد راهنمای دانشگاهی

Fangjinhua Wang is a Researcher affiliated with the Department of Computer Science at ETH Zurich, working within the Professorship for Computer Science. Their role involves contributing to cutting-edge research in fields such as 3D reconstruction, computer vision, and neural networks. The research focuses on advancing methodologies like scene graph manipulation, multi-view stereo techniques, and holistic human-scene reconstruction. Collaborations and projects emphasize practical applications in robotics, computer graphics, and AI-driven systems. While no explicit education details are provided, the research trajectory reflects a deep engagement with computational methods for 3D modeling and vision-based systems. The work bridges theoretical advancements with real-world applications, addressing challenges in scene understanding, object interaction, and human-robot collaboration. Research interests are centered on interdisciplinary topics including neural representation learning, robust visual localization, and the integration of geometric priors for high-fidelity reconstructions. The output demonstrates a commitment to both foundational research and applied solutions in computer science and robotics.

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +۳ مورد دیگر

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

• 3D Computer Vision
• Geometric Deep Learning
• Topological Deep Learning
• +۱۰ مورد دیگر

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.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Janna Hastings is Assistant Professor for Medical Knowledge and Decision Support at the Institute for Implementation Science in Health Care (Faculty of Medicine) at the University of Zurich since August 2022, while also serving as Vice-Director of the School of Medicine at the University of St. Gallen. Her research focuses on digitalization in clinical contexts, examining how AI-driven knowledge systems reshape clinical practice, professional identity, and doctor-patient relationships. Education: PhD in Computational Biology (University of Cambridge, 2019), part-time MSc in Computer Science (University of South Africa, 2011), MSc in Philosophy (Open University, 2012) Prior Roles: Group Coordinator at European Bioinformatics Institute (2006-2015), Postdoctoral Researcher at Otto-von-Guericke University Magdeburg (2019-2022), Co-Leader of Human Behaviour-Change Project at University College London (2017-2022) Her research spans ontology development for biomedical domains (ChEBI, Human Behaviour Ontology), AI applications in healthcare decision-making, and behavior change interventions. Key projects include: Building ChEBI molecular ontology Developing Human Behaviour-Change Project knowledge system Ontology-driven mental health frameworks LLM applications in radiation oncology Time-series modeling of metabolism in ageing She explores the capabilities and limitations of clinical AI systems, with publications in JMIR and Lancet Digital Health , covering topics like bias prevention in generative AI and proteomic biomarker discovery. Her work bridges biomedical research with implementation science and digital ethics.

• Medical Knowledge Systems
• Artificial Intelligence in Healthcare
• Ontology Development
• +۴ مورد دیگر

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.

• 3D Computer Vision
• Deep Learning
• Generative Models
• +۲ مورد دیگر

Matthieu Wyart is a Full Professor of Theoretical Physics at École polytechnique fédérale de Lausanne (EPFL), holding a position in the School of Basic Sciences within the Institute of Physics. He leads research in the Physics of Complex Systems Laboratory (PCSL) at EPFL, where he investigates fundamental questions at the intersection of condensed matter theory, statistical mechanics, and emerging connections to machine learning. Wyart completed his education at prestigious French institutions, earning his physics degree with Honors from École Polytechnique in Paris in 2001, followed by a Diploma of Advanced Studies in Theoretical Physics with highest Honors from École Normale Supérieure, Paris in 2002. He obtained his doctoral degree in Theoretical Physics and Finance from SPEC, CEA Saclay, Paris in 2006 with a thesis on electronic markets. His academic journey included postdoctoral positions at Harvard University, Janelia Farm, and Princeton University before joining New York University as an Assistant Professor in 2010, where he was promoted to Associate Professor in 2014. He moved to EPFL in July 2015 as an Associate Professor of Theoretical Physics and was promoted to Full Professor in April 2024. His research spans multiple domains including condensed matter theory, statistical mechanics, quantum information, and biophysics, with particular focus on disordered systems, glass transitions, amorphous solids, and the emerging connections between physical systems and machine learning architectures. Wyart's work often reveals deep theoretical connections between seemingly disparate fields, such as demonstrating how principles governing amorphous materials relate to the behavior of neural networks. His recent publications explore hierarchical structures in data, diffusion models, learning curves for compositional data, and the physics of creep in disordered media. Through his laboratory (PCSL), Wyart fosters interdisciplinary research that bridges traditional physics with contemporary challenges in machine learning and complex systems. His work has established important theoretical frameworks for understanding the glass transition, jamming phenomena, and the geometric principles underlying both physical and artificial learning systems.

• Condensed Matter Theory
• Statistical Mechanics
• Quantum Information
• +۷ مورد دیگر

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.

• Computer Vision
• Computational Photography
• Machine Learning
• +۴ مورد دیگر

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.

• Computer Vision
• Machine Learning
• 3D Geometry Processing
• +۴ مورد دیگر

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.

• Bronchoscopic Robotics
• Geometric Computer Vision
• Surgical Workflow Prediction
• +۳ مورد دیگر

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

• Deep learning
• Reinforcement learning
• Computer vision
• +۳ مورد دیگر

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.

• Software Engineering
• Generative AI
• Deep Learning
• +۷ مورد دیگر

Michael F. Herbst is a tenure-track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) with joint appointments in Mathematics and Materials Science. He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on algorithm development for quantum-chemical simulations of solids and error control in computational modeling. His work bridges mathematics, solid-state physics, and computer science through interdisciplinary research. His research interests include: Density-functional theory (DFT) and Kohn-Sham equations Error propagation in materials property predictions High-throughput screening algorithms Julia programming for scientific computing Tensor networks and reduced basis modeling Self-consistent field convergence methods Recent publications highlight his contributions to: Efficient response property calculations in DFT Rotationally equivariant machine learning operators GPU-accelerated electronic structure methods Polarizable continuum solvation models Robust black-box quantum chemistry algorithms Open-source software development Teaching activities span mathematics, computer science, and chemistry curricula, including interdisciplinary workshops on electronic structure numerics and Julia programming for materials science. He has mentored PhD students Bruno Ploumhans and Niklas Frederik Schmitz.

• Quantum Chemistry
• Materials Science
• Mathematics
• +۱۲ مورد دیگر

Dr. Alexander Duncan is a Senior Scientific Collaborator at École polytechnique fédérale de Lausanne (EPFL), holding multiple roles across departments. Affiliated with the School of Basic Sciences (SB), Institute of Physics (IPHYS), and Laboratory of Electron Microscopy and Spectroscopy (LSME), he also serves in educational units like EDMX-ENS and SMX-ENS. His research focuses on advanced electron microscopy techniques, oxide heterostructures, and machine learning applications for nanoscale analysis. Materials Science Electron Microscopy Machine Learning for Materials Analysis Nanoscale Imaging Ferroelectric Materials Oxide Heterostructures Duncan's recent publications demonstrate expertise in EDX spectral denoising , symmetry breaking interfaces , and machine learning-aided phase quantification . He has developed novel algorithms for hyperspectral unmixing and atomic-scale reconstruction techniques, with applications in neuromorphic computing and deep mantle mineral analysis. He supervises PhD students including Aebersold Arthur Brian and Sblendorio Gabrielle Anne Laguisma , while actively contributing to open-source software development ( espm and emtables ). Duncan also participates in EPFL's Physics PhD program committee and teaches advanced electron microscopy courses.

• Materials Science
• Electron Microscopy
• Machine Learning for Materials Analysis
• +۳ مورد دیگر

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.

• Artificial Intelligence for Science
• Biomedical AI Agents
• Multi-omics Analysis
• +۷ مورد دیگر

Alexandre Caboussat is a Consultant Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences in the Mathematics Department. He contributes to the Euler Programme and the SMA - Teaching unit, focusing on advanced mathematical analysis and computational methods. Research Interests : Numerical Analysis, Scientific Computing, Numerical Optimization, Variational Problems, Computational Fluid Dynamics, Free Surface Flows, Air Quality Modeling, and Computational Chemistry. His recent work emphasizes numerical methods for partial differential equations, particularly the Monge-Ampère equation, adaptive finite element techniques, and physics-informed neural networks. Applications span from optimal transport to laser surface melting and environmental flows. Students : Girardin Maude, Peruso Anna, Diserens Léo Aurélio, Gourzoulidis Dimitrios, Landry Chantal, Mrad Arwa. Contact : alexandre.caboussat@epfl.ch

• Numerical Analysis
• Scientific Computing
• Numerical Optimization
• +۵ مورد دیگر