جست‌وجوی استاد و پژوهشگر

Kevin J Liangمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• 3D Computer Vision
• +۵ مورد دیگر

Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.

Paolo Favaroمشاهده پروفایل

استاد

• Computer Vision
• Computational Photography
• Machine Learning
• +۴ 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
• Machine Learning
• 3D Reconstruction
• +۳ مورد دیگر

Siwei Zhang is a researcher at the ETH Zürich , focusing on computer vision and machine learning. Their work intersects with robotics, human motion analysis, and egocentric vision. Email: siwei.zhang@inf.ethz.ch Research Interests 3D Human Body Modeling Egocentric Vision Systems Motion Reconstruction & Interaction Neural Architecture Optimization Surgical Pose Estimation Selected Publications (2025-2019) Recent work includes neural volumetric body models (2025), egocentric synthetic data generation (2024), robust human motion reconstruction (2024), surgical pose datasets (2023), and foundational studies in probabilistic mesh recovery, whole-body grasping, and face reenactment (2019-2022). Key trends combine computer vision, robotics, and deep learning with applications in scene understanding and human-computer interaction.

Marc Poulyمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Marc Pouly is a Professor of Artificial Intelligence at Lucerne University of Applied Sciences and Arts (HSLU), where he also serves as Co-Head of the Applied AI Research Lab. He holds a PhD in Artificial Intelligence from the University of Fribourg (2008) and has extensive industry experience as a Chief Data Scientist at Jaywalker AG and Scientific Advisor for Alpine AI and Artificialy SA. His research focuses on AI-driven solutions for healthcare, computer vision, natural language processing, and industrial optimization. Education: PhD in Artificial Intelligence (2004–2008), University of Fribourg MSc in Computer Science & Mathematics (2002–2004), University of Fribourg BSc in Computer Science (1999–2002), University of Fribourg Research Interests: His work spans Medical AI (e.g., dermatology diagnostics via computer vision), Generative Models , Recommender Systems , and AI Ethics . He co-developed the SkinApp for automated skin analysis and pioneered projects like CleanPatrick for image data quality audits. His expertise in self-supervised learning addresses data scarcity challenges in healthcare. Key Achievements: 2023: Best Paper Awards (IARIA, MICCAI) 2021: Research Mosaic recognition by swissuniversities 2017: Aha! Award for the SkinApp project Labs & Collaboration: He leads the Applied AI Lab and collaborates with institutions like ETH Zurich (co-supervising PhDs) and industry partners such as Prepress Media AG and the Swiss Allergy Centre. His work bridges academia and industry, emphasizing AI’s practical applications in healthcare, marketing, and cybersecurity.

Federico Wadehnمشاهده پروفایل

مدرس ارشد

• Digital Signal Processing
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Dr. Federico Wadehn is a Senior Lecturer at the Institute of Signal Processing and Wireless Communications within the School of Engineering at Zurich University of Applied Sciences (ZHAW), specializing in digital signal/image processing and machine learning applications for biomedical and engineering systems. His role includes teaching core courses and supervising graduate theses. Education: PhD in Statistical Signal Processing with Probabilistic Graphical Models, ETH Zürich (2014-2018) Teaching Certificate, ETH Zürich and ZHAW (2017-2018) Visiting Researcher in Neuromonitoring, MIT (2016) MSc/BSc in Electrical Engineering and Information Technology, ETH Zürich (2009-2014) Erasmus Exchange in Control Engineering, Imperial College London (2012-2013) Continuing Education: MATLAB C Coder (2023), Embedded C (2021), IBM Rational Rhapsody (2020) Research Interests: His work bridges Digital Signal Processing (classical, ML, and deep learning), Computer Vision , Sensor Fusion/Kalman Filtering for inertial systems, Probabilistic Graphical Models for navigation, and Physiological System Modeling (respiratory/cerebrovascular). He emphasizes model-based approaches for real-world biomedical applications, particularly in neuromonitoring and respiratory care. Publication Trends: His 2015-2020 publications reveal a consistent focus on advanced signal processing for physiological monitoring, featuring model-based separation of biological signals (eye movements, blood flow), sparse state-space modeling, and Kalman smoothing innovations. The work integrates probabilistic graphical models with biomedical constraints, demonstrating strong cross-disciplinary impact in IEEE journals and conferences. Scientific Awards: Study and doctoral scholarship from German Academic Scholarship Foundation (2014) Abiturpreis Physik from Deutsche Physikalische Gesellschaft (2008) Advising and Professional Activities: He supervises bachelor's/master's theses at ZHAW while maintaining industry connections through prior roles as Systems Engineer at Bosch Sensortec (2021-2023), Research Engineer at Hamilton Medical AG (2019-2021), and Computer Vision Freelancer (2021). His dual academic-industry experience informs practical curriculum development. Laboratories and Teams: He contributes to ZHAW's Digital Health Lab and Medical Systems Platform, focusing on translating signal processing innovations into clinical tools for respiratory monitoring and motion analysis, with recent patent work in ventilation technology.

Alexander Hägeleمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Artificial Intelligence
• +۱ مورد دیگر

Alexander Hägele is a researcher at the Laboratoire d'apprentissage automatique et d'optimisation (Machine Learning and Optimization Laboratory, MLO) within the College of Computer and Communication Sciences at École polytechnique fédérale de Lausanne (EPFL). His work focuses on theoretical and practical aspects of machine learning optimization.

Raoul de Charetteمشاهده پروفایل

استاد

• Visual Scene Understanding
• Computer Vision
• 3D Reconstruction
• +۳ مورد دیگر

Raoul de Charette is a Research Director in computer vision at Inria Paris, leading the Astra-Vision group within the ASTRA team. His academic journey includes a PhD from Mines Paris (2012) and Habilitation (HDR) in 2022, with research stints at Carnegie Mellon University (2011), Mines Paris (2013), and the University of Makedonia (2014). His educational background comprises: PhD from Mines Paris (2012) Habilitation (HDR) (2022) De Charette's research centers on robust and interpretable visual scene understanding , spanning 3D scene reconstruction, domain adaptation, material recognition, and physics-grounded vision foundation models. His work integrates physical principles and synthetic data to enhance model robustness in real-world scenarios like autonomous driving and urban environments. Key contributions include uncertainty-aware 3D scene completion (PaSCo), material extraction from single images (Material Palette), and prompt-driven domain adaptation (PODA). Recent publications reveal a strategic shift toward vision-language integration, material-centric scene understanding, and foundation models that minimize labeled data dependency. His group pioneers physics-informed approaches to improve interpretability and resilience against environmental challenges like adverse weather conditions. Key scientific recognition includes: Best Paper Honorable Mention at EGSR 2025 for MatSwap ELLIS Membership PR[AI]RIE-PSAI Fellowship De Charette actively mentors four PhD students—Fatima Balde, Mohammad Fahes, Ivan Lopes, and Tetiana Martyniuk—often in industry collaborations with Valeo.ai and Kyutai. He secures funding through fellowships and industry partnerships, regularly opening PhD positions (including a 2025 opening for Physics-Grounded Vision Foundation Models). As an area chair for CVPR, ECCV, WACV, and IROS, he shapes the field through conference leadership and co-organizing initiatives like the African Computer Vision Summer School. He directs the Astra-Vision group within Inria Paris' ASTRA team, driving interdisciplinary research at the intersection of computer vision, machine learning, and physics-based modeling for real-world deployment in robotics and intelligent transportation systems.

Fateme Jamshidiمشاهده پروفایل

پژوهشگر

• Causal Inference
• Bayesian Networks
• Multi-Armed Bandits
• +۳ مورد دیگر

Fateme Jamshidi is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Management of Technology and the Institute for Management, Technology and Economics. Her research focuses on: Causal inference and discovery Bayesian network modeling Multi-armed bandit algorithms Context-specific independence analysis Hidden confounding in causal networks Nonparametric statistical testing Recent work trends demonstrate expertise in causal modeling under uncertainty, with applications to machine learning and decision systems. Her publications explore graph-dependent regret bounds, causal imitability, and structural side information integration.

• Signal Processing
• Information Theory
• Multimedia Security
• +۵ مورد دیگر

Svyatoslav Voloshynovskyy is a Professor at the University of Geneva's Faculty of Science, Department of Computer Science, with an impressive publication record of 195 works and supervision of 26 graduate projects. His extensive research portfolio demonstrates significant contributions to signal processing, information theory, and multimedia security, evolving into cutting-edge applications in medical imaging and privacy-preserving technologies. Dr. Voloshynovskyy's research interests span multiple interconnected domains at the intersection of theoretical foundations and practical applications. His work has progressed from foundational research in data hiding and watermarking to contemporary applications in privacy-preserving medical imaging. Key research areas include: Information-theoretic approaches to multimedia security Advanced signal processing techniques for image and data analysis Federated learning frameworks for healthcare applications Privacy-preserving machine learning methodologies Medical imaging analysis with focus on PET/CT technologies Theoretical foundations of data hiding and watermarking Analysis of his recent publications (2022-2024) reveals a strategic shift toward healthcare applications, particularly in medical imaging with privacy preservation. His work integrates federated learning, differential privacy, and deep learning to address critical challenges in healthcare data analysis while maintaining patient confidentiality. This represents a natural evolution from his earlier theoretical work in signal processing and information theory to impactful real-world healthcare applications requiring multi-institutional collaboration. With 26 supervised works documented in the University of Geneva archive, Dr. Voloshynskyy has demonstrated significant commitment to academic mentoring and research supervision. His publication pattern suggests successful grant acquisition for projects at the intersection of signal processing, machine learning, and healthcare applications, though specific funding details aren't provided in the available information. His research trajectory indicates leadership in a specialized research group focused on signal processing and machine learning applications, with recent emphasis on medical imaging. The consistent mention of multi-institutional collaborations in his latest publications suggests an active research team working on federated learning approaches for medical image analysis, likely involving partnerships with healthcare institutions across multiple countries.

• Data Science
• Computational Statistics
• Bayesian Models
• +۵ مورد دیگر

Antonietta Mira is a Full Professor of Statistics at the University of Italian Switzerland (USI), where she also serves as the founder and director of the Data Science Lab. She holds a part-time professorship at Università dell'Insubria and has held visiting roles at institutions such as the University of Cambridge and Université Paris-Dauphine. Her academic leadership includes a Vice-Dean role in the Faculty of Economics (2013-2015). She earned a PhD in Computational Statistics from the University of Minnesota (1998), a Master's in Statistics (1996), and a Doctorate in Methodological Statistics from the University of Trento (1995). Her research focuses on Bayesian statistical models, computational statistics, and interdisciplinary applications in healthcare, finance, and social sciences. Mira is a Fellow of the International Society for Bayesian Analysis (ISBA) and the Istituto Lombardo Accademia di Scienze e Lettere. Her work has been recognized with awards for both research and teaching excellence. She leads Swiss National Science Foundation-funded projects and serves on editorial boards of journals like Bayesian Analysis and Statistica Sinica . Her publications span topics such as approximate Bayesian computation (ABC), spatio-temporal modeling of health events, and data science applications in public policy. Mira also engages in public outreach, including scientific exhibitions like Numbed by Numbers! and lectures on data-driven societal issues.

• International Trade
• Economic Forecasting
• Bayesian Neural Networks
• +۵ مورد دیگر

Davide Arioldi is a Postdoctoral Researcher and Scientific Collaborator at the Institute for Economic Research (IRE) within the Faculty of Economic Sciences at the University of Italian Switzerland (USI). He holds a PhD in Economics from USI (2018) with a thesis on international trade and has prior experience as a quantitative analyst and portfolio manager in investment firms. His current research focuses on Bayesian Neural Networks, Probabilistic Programming, causal structure learning via graphical models, and text analysis. He is affiliated with the Observatory of Public Finance and Energy (O-FPE), where he develops tax revenue forecasting models and compiles comparative studies on cantonal public finances. He also contributed to the PanelCODE project and the contati.ch platform analyzing policy impacts. Research expertise includes international trade dynamics, consumer behavior, productivity-wage gaps, bargaining theory, firm production, and network analysis. His work spans economic policy evaluation, unstructured data analysis, and regional economic disparities. He is co-developer of the contati.ch platform funded by a private foundation to assess policy impacts on local populations. Key projects include annual cantonal public finance studies, tax revenue modeling at O-FPE, and business dynamics analysis using unstructured online data since 2016. He collaborates across interdisciplinary teams in Lugano, Switzerland.

Taiji Suzukiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +۶ مورد دیگر

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.

Nicolas Macrisمشاهده پروفایل

استاد مدعو

• Statistical Mechanics
• Information Theory
• Error Correcting Codes
• +۳ مورد دیگر

Nicolas Macris is an Adjunct Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He is affiliated with multiple groups including the Information Processing group and the Lab for Statistical Mechanics of Inference in Large Systems . His academic career spans roles at the Faculty of Basic Science at EPFL and visiting appointments at institutions like the University College Dublin and ETH Zürich. Macris received his PhD in theoretical physics from EPFL and conducted postdoctoral research at the mathematics department of Rutgers University. His research bridges statistical mechanics , information theory , and quantum information , with a focus on error-correcting codes, graphical models, and learning theory. His work often employs methods from mathematical and statistical physics, probability, and discrete mathematics. His recent publications analyze phase transitions in high-dimensional models, quantum algorithms, and spatial coupling techniques. He has received recognition through IBM Quantum contest awards for his contributions. He actively teaches courses in quantum information processing and mentors PhD students in interdisciplinary research areas. 2019: 2nd place, IBM Quantum contest (Best Paper) 2018: 2nd place, IBM Quantum contest (Best Paper) Macris's work is supported by the Swiss National Foundation for Science , and he collaborates with labs such as the Communication Theories Laboratory and Information Processing group .

Corentin Dumeryمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• 3D Reconstruction
• +۴ مورد دیگر

Corentin Dumery is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Computer Vision Laboratory (CVLAB) and Machine Learning and Optimization Laboratory (MLO) within the School of Computer and Communication Sciences . His research bridges Computer Vision and Computer Graphics , focusing on 3D scene reconstruction, garment modeling, and neural rendering techniques. Previously, he interned at Meta Redmond , worked at CEA Paris-Saclay on polycube mapping, and was a visiting researcher at ETH Zurich under Prof. Olga Sorkine-Hornung . Corentin holds dual MSc degrees in Computer Science from National University of Singapore (NUS) and Télécom Paris . His work emphasizes 3D content creation for AR/VR Diffusion models for garment reconstruction Neural radiance field optimization Polycube mapping for hexahedral meshing His recent publications (2022–2025) span top venues like SIGGRAPH , ICCV , and CVPR , addressing challenges in 3D Gaussian splatting, view-consistent NeRF training, and single-view garment recovery. He also contributes to academic service as an Outstanding Reviewer at CVPR25 and co-organizes workshops like OpenSUN3D . At EPFL, he serves as Head Teaching Assistant for courses CS433 Machine Learning (2023–2024) and CS442 Computer Vision (2023–2024). Additionally, he is the VP/Treasurer of EPIC , EPFL's computer science PhD association.

Nuria Oliverمشاهده پروفایل

پژوهشگر

• Computational Models of Human Behavior
• Human-Computer Interaction
• Intelligent User Interfaces
• +۱۲ مورد دیگر

Nuria Oliver, PhD is a pioneering computer scientist and ACM Fellow , recognized as the first female Spanish computer scientist to achieve both ACM Distinguished Scientist and IEEE Fellow status. Current affiliations include Microsoft Research and former roles at MIT Media Lab. First female Spanish ACM Fellow IEEE Fellow Academia Europaea member Her research spans human behavior modeling , intelligent user interfaces , and mobile computing , with notable work in: Wearable context-aware systems (DyPERS, HealthGear) Music-adaptive exercise platforms (MPTrain, TripleBeat) Social network analysis (information propagation, community link modeling) Computer vision interfaces (LAFTER facial expression recognition) Scientific achievements include: 41 patents 15+ years of continuous multimodal systems research Recognized for LAFTER system (2000 paper) She actively promotes technology accessibility through: Media collaborations Technical/non-technical keynotes STEM outreach for girls