Dominique Pioletti is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding multiple positions across the institution. He serves as Director of the Laboratory of Biomechanical Orthopedics (LBO) within the School of Engineering, and has additional appointments in the Institute of Bioengineering (IBI-STI), the School of Engineering Mechanical Engineering (STI-SGM), the Doctoral Program in Bioengineering (EDBB-ENS), and the Institute of Materials (IGM). His office is located at MED 3 2626, Station 9, 1015 Lausanne, Switzerland. Dr. Pioletti received his Master in Physics from EPFL in 1992 and continued at the same institution to earn his PhD in biomechanics in 1997, where he developed original constitutive laws accounting for viscoelasticity in large deformations. Following his doctoral studies, he spent two years as a post-doctoral fellow at UCSD (University of California, San Diego), gaining expertise in cell and molecular biology, particularly in gene expression of bone cells in contact with orthopedic implants. In April 2006, he was appointed Assistant Professor tenure-track at EPFL and became director of the Laboratory of Biomechanical Orthopedics. He was promoted to Associate Professor in 2013 and subsequently to Full Professor. His research focuses on orthopedic biomechanics, tissue engineering, and mechano-biology, with specific interests in biomechanics and tissue engineering of musculoskeletal tissues, mechano-transduction in bone, and development of orthopedic implants as drug delivery systems. The Laboratory of Biomechanical Orthopedics (LBO) under his direction is dedicated to advancing techniques and technology for patient care in the musculoskeletal system through fundamental research, applied research, and teaching. Analysis of his recent publications reveals a strong emphasis on hydrogel-based biomaterials for cartilage repair, with significant work on temperature effects in cartilage engineering, adhesive hydrogels, and mechanical properties of biomaterials. His research increasingly incorporates computational approaches including AI and deep learning for biomechanical modeling and prediction. There is also a consistent focus on understanding the relationship between mechanical stimuli and biological responses in musculoskeletal tissues. Professor Pioletti has supervised numerous doctoral students throughout his career, with current PhD candidates including Bouchez Mi-Lane Elodie, Mohammadi Ramin, Nottegar Alexander Arthur, Raja Sruthi, Reitzel Antoine, and Turgut Deniz Cemre. His past students form an extensive list spanning multiple cohorts, reflecting his long-standing commitment to academic mentorship. The Laboratory of Biomechanical Orthopedics (LBO) serves as the primary research hub for Professor Pioletti's work, focusing on the advancement of techniques and technology for patient care in the musculoskeletal system. The lab's mission encompasses fundamental research, applied research, and teaching, with current work emphasizing biomechanical considerations in orthopedic applications. The lab website (https://lbo.epfl.ch/) provides additional details about ongoing projects and team members.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۳۱-۴۵ از ۱۰۰ استاد راهنما
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Carl Allenمشاهده پروفایل
استادیار
Carl Allen is a Laplace Junior Chair in Machine Learning at École Normale Supérieure, Paris, working in the research group of Stéphane Mallat, Giulio Biroli and Garbiele Peyré. Previously, he was a postdoctoral fellow at ETH Zurich and completed his PhD in Machine Learning in 2021 at the University of Edinburgh under the supervision of Professors Tim Hospedales and Iain Murray. His educational background includes a BSc in Mathematics & Chemistry from the University of Southampton, an MSc in Mathematics and the Foundations of Computer Science (MFoCS) from the University of Oxford, and MScs in Artificial Intelligence and Data Science from the University of Edinburgh. Before transitioning to AI/ML research, he spent several years in Project Finance. Allen's research focuses on mathematically understanding mechanisms behind successful machine learning methods, particularly neural networks. He investigates how machine learning models exploit aspects of data distribution from a probabilistic perspective. His current topics include explaining how VAEs disentangle independent factors of data, identifying mathematical models behind self-supervised learning, and deriving probabilistic interpretations of softmax classification. His PhD work investigated neural representations of discrete objects and their relationships, with a main result explaining how word embeddings can seemingly be added and subtracted (e.g., queen ≈ king - man + woman), which received Best Paper (honorable mention) at ICML 2019. His research spans theoretical foundations of machine learning, with particular emphasis on representation learning, disentanglement, and probabilistic modeling of neural networks. His work connects mathematical principles with practical machine learning applications, aiming to develop more interpretable and reliable algorithms. Allen has received notable recognition including a Best Paper honorable mention at ICML 2019 and a research grant from the Hasler Foundation. He has delivered invited talks at prestigious institutions including Harvard Center of Mathematical Sciences & Applications and Astra-Zeneca. His collaborative work spans multiple institutions, including a notable internship at Samsung AI Centre, Cambridge, where he worked at the intersection of representation learning and logical reasoning. His research has significant implications for developing more interpretable, reliable, and theoretically grounded machine learning systems.
Dr. Francesco Paolo Casale serves as Principal Investigator in Machine Learning in Biomedicine at the Helmholtz Munich Institute AI for Health, part of Helmholtz Zentrum München and affiliated with Ludwig-Maximilians-Universität München's Biomedical Center. His research develops machine learning and statistical tools to analyze genetic cohorts with deep molecular and phenotypic data, addressing fundamental biomedical questions about disease mechanisms and progression. His academic foundation includes: PhD in Statistical Genetics from University of Cambridge & EMBL-EBI (2012-2016) M.Sc. in Physics of Complex Systems from Università di Napoli Federico II (2009-2012) B.Sc. in Physics from Università di Napoli Federico II (2009) Casale's research integrates machine learning, statistical inference, and systems genetics to develop scalable tools for genetic association studies, deep learning models for imaging genetics, and computational methods examining gene-environment interactions. His work emphasizes model robustness and interpretability while investigating molecular and cellular traits associated with disease severity. Current projects focus on rare variant analysis, aberrant gene expression prediction, and longitudinal omics data integration. His publication record shows a clear progression from foundational statistical genetics methods toward increasingly sophisticated integration of machine learning with multi-omics data. Recent work emphasizes practical biomedical applications including disease risk prediction through Mendelian randomization frameworks, advanced single-cell analysis techniques, and histopathology image classification. The research demonstrates consistent methodology development focused on scalability for large datasets while maintaining biological interpretability. Key recognitions include: Highly Recognized article in PloS Genetics Research Prize (2018) Microsoft Research New England Postdoctoral Fellowship (2017) EMBL studentship (2012) Honors for MSc and BSc degrees from Università di Napoli Federico II Throughout his career at Microsoft Research, Insitro, and Helmholtz Munich, Casale has led research teams developing computational approaches at the intersection of human genetics and machine learning. His work contributes to landmark projects including the 1000 Genomes Project and Blueprint initiative, with conference presentations at major venues including NeurIPS, ASHG, and EASL. Current grant support likely stems from Helmholtz Association funding mechanisms and collaborative biomedical research programs. He directs the Systems Genetics and Machine Learning Research team at Helmholtz Munich, which operates within the Biomedical Center ecosystem of LMU Munich. The team focuses on leveraging large-scale genetic datasets with machine learning to understand disease biology, with particular emphasis on target identification and characterization for therapeutic development. Current research directions include multi-timepoint omics analysis, disease subtyping, and developing interpretable models for clinical translation.
Desi R. Ivanovaمشاهده پروفایل
پژوهشگر ارشد
Desi R. Ivanova is a research fellow at the University of Oxford's Department of Statistics under the Florence Nightingale Bicentennial Fellowship. Her work bridges probabilistic machine learning, Bayesian experimental design, and LLM evaluation frameworks. She holds a DPhil in Statistics from Oxford's StatML CDT program (2020-2024) and an MMORSE in Mathematics from University of Warwick (2011-2016) with Erasmus exchange at LMU Munich. Research spans causal machine learning and uncertainty quantification Developed CO-BED and Step-DAD frameworks Focus on LLM evaluation methodology and calibration Expert in real-time adaptive experimental systems Her publications demonstrate expertise in Bayesian self-consistency methods, neural data compression, and privacy-preserving dataset merging. Key contributions include improving amortized inference efficiency and developing gradient-based causal experimental designs. Current work emphasizes rigorous statistical evaluation of language models, advocating for appropriate uncertainty quantification when analyzing performance across small datasets. She critiques CLT-based methods for LLM evaluation and proposes more robust frequentist and Bayesian alternatives.
Melih Kandemirمشاهده پروفایل
دانشیار
Melih Kandemir is an Associate Professor of Machine Learning at the University of Southern Denmark, Department of Mathematics and Computer Science. He earned his PhD in 2013 from Aalto University under Prof. Samuel Kaski, followed by postdoctoral work at Heidelberg University (with Prof. Fred Hamprecht) and an assistant professorship at Ozyegin University, Turkey. Prior to joining SDU, he led research at Bosch Center for Artificial Intelligence. Education: PhD (Aalto University, 2013), Postdoc (Heidelberg University). Previous Roles: Assistant Professor (Ozyegin University), Research Group Leader (Bosch CAI). His research focuses on Bayesian inference, stochastic process modeling with deep neural networks, and applications to reinforcement learning and continual learning. He leads the SDU Adaptive Intelligence (ADIN) Lab and is an ELLIS Member, reflecting his standing as a top European AI researcher. His work addresses critical challenges in uncertainty quantification, exploration strategies, and theoretically grounded algorithms for decision-making systems. Recent publications highlight expertise in model-based reinforcement learning (e.g., MOMBO for offline RL), PAC-Bayesian bandits, evidential learning for robust classification, and neural stochastic differential equations. His scientific awards include a Best Paper Award (2017) and ELLIS Membership. He also explores interdisciplinary applications of natural sciences to sustainable technology development.
Emmanuel Dupoux is a Professor at École des Hautes Études en Sciences Sociales (EHESS), affiliated with the School of Advanced Studies in Social Sciences and the Laboratory of Cognitive Science and Psycholinguistics (LSCP). His work bridges cognitive science, computational linguistics, and machine learning to study infant language acquisition and social cognition. Co-creator and director of EHESS Cognitive Science Master program Former LSCP laboratory director (1998-2009) Current research focuses on textless speech modeling and ecological audio analysis Research areas include: Modeling early language acquisition mechanisms using Bayesian models and HMM Investigating phonological 'deafness' and critical age plasticity Studying social cognition development through infant-toddler experiments His technical contributions span speech processing toolkits like Shennong, emergent communication frameworks (STOP dataset), and innovative approaches to self-supervised speech modeling. Recent publications demonstrate breakthroughs in: Topography-inspired CNN designs for better memory efficiency Prosody-aware generative spoken language models (pGSLM) Textless emotion conversion systems He actively explores how machine learning can reverse-engineer infant language learning processes from ecological audio data, with applications in neurodegenerative disease diagnostics. Key collaborations include: INRIA's Cognitive Machine Learning (CoML) team Facebook AI Research (FAIR) partnerships VoxPopuli multilingual speech corpus development
Janna Hastingsمشاهده پروفایل
استادیار
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.
- Data Science
- Machine Learning
- Spatial Analysis
- +۵ مورد دیگر
Curdin Derungs is a Lecturer in Data Science at the Lucerne School of Computer Science (HSLU), Switzerland. He specializes in applying statistical and machine learning techniques to energy systems, spatial analysis, and natural language processing. His professional competencies include data-driven automation, deep learning, time series analysis, and energy systems optimization. Derungs holds a PhD in Natural Sciences (University of Zurich, 2013) with a focus on NLP and spatial analysis, and a DAS in Applied Statistics (ETH Zurich, 2019). Earlier degrees include a Master's in Geography (2008) and Atmospheric Physics (ETH Zurich, 2008). His research explores intersections between data science and environmental sustainability, including energy efficiency optimization, spatial language dynamics, and landscape modeling. Recent work focuses on occupant behavior in energy systems, geotagged text analysis, and soil formation modeling. Key projects include SCCER FEEB&D Work Package 2 on renewable energy systems, the Romande Energy Demonstrator, and urban greening studies. He actively contributes to interdisciplinary initiatives like the IGE Innovationswettbewerb 2019 and Innovationspark Zentralschweiz.
Mirko Birbaumer is a Professor at the Lucerne School of Engineering and Architecture (HSLU), specializing in data science and machine learning applications. He holds a PhD in computer-assisted image analysis from ETH Zurich and has completed advanced executive education at MIT in Innovation and Strategy. PhD in Systems Biology (ETH Zurich, 2010) Executive Certificate in Innovation and Strategy (MIT, 2022) Advanced Executive Certificate in Innovation, Strategy, and Technology (MIT, 2024) His research integrates statistical data analysis , computer vision , and digital health , with applications spanning industrial quality control, medical imaging, and environmental monitoring. Recent work focuses on physics-informed neural networks, interpretable anomaly detection, and causal machine learning. His projects include AI-driven solutions for medical diagnostics, predictive maintenance in manufacturing, and environmental anomaly detection. He has supervised over 20 student theses on topics like deep learning for knee osteoarthritis detection, monocular depth estimation, and AI-assisted logo similarity analysis. He leads the Data Science profile in the Master of Science and Engineering program and teaches courses on deep learning, Bayesian machine learning, and computer vision. His methodological expertise includes agent-based modeling, statistical analysis, and explainable AI.
- Privacy
- Cybersecurity
- Machine Learning
- +۸ مورد دیگر
Mathias Humbert is an Associate Professor at the University of Lausanne, with affiliations at the Department of Information Systems in the Faculty of Business and Economics. Previously, he held roles as a Scientific Project Manager at the Cyber-Defence Campus, Senior Data Scientist at the Swiss Data Science Center (SDSC) at ETHZ and EPFL, and Postdoctoral Researcher at CISPA in Saarbrücken. He earned his Ph.D. in 2015 from EPFL (Switzerland) after completing B.Sc./M.Sc. studies at EPFL and UC Berkeley. His research focuses on privacy, cybersecurity, and machine learning, particularly examining privacy risks in MLaaS, online social networks, wearable devices, and spectrum monitoring, while developing novel privacy-preserving frameworks for biomedical data and social graphs. Key research areas: Privacy in data sharing, interdependent privacy, genomic privacy, location privacy, and graph-based machine learning Notable contributions: GraphEraser for graph unlearning, KGP Meter for genomic privacy awareness, and SVT² for differential privacy in methylation data His recent publications explore machine unlearning vulnerabilities, privacy risks in DNA methylation data, and usability challenges in web security mechanisms, with a focus on empirical studies and cryptographic solutions. He received a Distinguished Paper Award at NDSS 2019 for his work on MBeacon and has contributed extensively to privacy research across ACM CCS, IEEE EuroS&P, and USENIX Security venues.
Sven Seukenمشاهده پروفایل
دانشیار
Prof. Dr. Sven Seuken is an Associate Professor at the University of Zurich's Department of Informatics, leading the Computation and Economics Research Group. He is affiliated with ETH Zurich's AI Center and co-directs the Zurich Center for Market Design. His roles include Chief Economist at Worldcoin and founder of Market Design Consulting GmbH. He holds a Ph.D. from Harvard University and has been recognized with awards such as the ERC Starting Grant and Google Faculty Award. His research bridges AI, game theory, and market design, focusing on practical applications like auction mechanisms and resource allocation. Education: Ph.D. in Computer Science, Harvard University (Advisor: David C. Parkes) Research Interests: Market Design Artificial Intelligence Economics and Computation Computational Mechanism Design Algorithmic Game Theory Machine Learning Recent Work Trends: Articles emphasize AI-driven market mechanisms, dynamic auctions, mobile network optimization, and scalable multi-agent systems. Key themes include truthful aggregation, cross-optimization, and reinforcement learning in strategic environments. Awards: ERC Starting Grant (2020) Google Faculty Research Award "Top 40 under 40" by Capital magazine (2017) Grants & Roles: Secured major grants including the ERC MIAMI project. Advises on market design for companies and serves as a mentor for the German Academic Scholarship Foundation. Active in organizing conferences like EC'22. Labs/Teams: Leads the Computation and Economics Research Group and collaborates with the Zurich Center for Market Design to advance applied market solutions.
Jean-Marc Odobezمشاهده پروفایل
استاد مدعو
- Computer Vision
- Machine Learning
- Deep Learning
- +۶ مورد دیگر
Jean-Marc Odobez is a Senior Scientist at the IDIAP Research Institute and Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he is affiliated with the School of Engineering and serves on the Electrical Engineering Doctoral committee (EDEE). He leads the Perception & Activity Understanding Group at Idiap and has extensive teaching responsibilities across multiple departments. Dr. Odobez received his PhD in Computer Science from Rennes University in 1994. His research focuses on multimodal perception systems combining computer vision, statistical machine learning, and deep learning for activity recognition, behavior understanding, and human-robot interaction. His work spans diverse application domains including human health assessment, social robotics, and media content analysis. His recent research shows strong trends in gaze estimation, human activity recognition, and multimodal processing. His team has developed innovative solutions for gaze tracking, head pose estimation, and activity recognition using depth sensors and neural networks. His work increasingly bridges computer vision with digital humanities, particularly in the analysis of ancient Maya glyphs. IEEE member Associate Editor of Machine Vision and Applications journal Dr. Odobez has supervised numerous PhD students and serves as a committee member for the Electrical Engineering Doctoral program. He has been principal investigator for over 16 European and Swiss research projects and has worked on 10 technology transfer projects with SMEs. He co-founded Klewel SA and Eyeware SA, focusing on eye tracking and attention modeling technologies. His research group actively collaborates with industry partners and maintains strong connections with the computer vision and human-computer interaction research communities.
Philip Garner is a Lecturer at the Laboratory for Intelligent Data Analysis and Processing (LIDIAP), part of the School of Engineering (STI) at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Institute of Electrical Engineering (IEM) and conducts research in intelligent data analysis, with a focus on biologically inspired neural models for speech and cognitive processing. Despite holding a 'Guest' status, he actively supervises PhD students and co-directs doctoral research, indicating a significant academic role. His research interests lie at the intersection of neuroscience and machine learning, particularly in spiking neural networks , speech recognition , neuromorphic computing , and deep learning architectures . He investigates how biologically plausible models can replicate brain-like computation for efficient information processing. His work often explores temporal dynamics, recurrence, memory mechanisms, and neural oscillations in artificial systems. The publications associated with his advisees and research group reflect a strong trend in developing energy-efficient, brain-inspired computing models for sequential data processing. These works frequently involve surrogate gradient learning, Bayesian recurrent units, and analysis of cross-frequency coupling in neural networks, contributing to both cognitive science and practical applications in speech technology. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: Philip Garner supervises multiple doctoral students, including Abdennadher Yesmine, Chen Haolin, Coppieters De Gibson Louise Clothilde, He Mutian, and has co-supervised several completed theses such as those of Bittar Alexandre, Honnet Pierre-Edouard, Schnell Bastian, Taghizadeh Mohammadjavad, and Tong Sibo. His advising role is central to the LIDIAP lab’s research output in neural computation. While specific grants are not listed, his involvement in cutting-edge AI and neuroscience research suggests active participation in funded projects. Labs and Teams: He is based at the IDIAP Research Institute, closely affiliated with EPFL’s LIDIAP laboratory, which specializes in intelligent data analysis and processing. The lab fosters interdisciplinary research in machine learning, signal processing, and cognitive modeling, often in collaboration with neuroscience and engineering groups.
Jonathan Dongمشاهده پروفایل
پژوهشگر
Jonathan Dong is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) within the School of Engineering. He works at the Biomedical Imaging Laboratory (LIB), focusing on advanced imaging techniques and computational models. PhD Students: Hu Zhiyuan, Liu Yan His research spans biomedical imaging, computational optics, and machine learning applications in imaging inverse problems. Recent work emphasizes phase retrieval, optical reservoir computing, and quantum information in microscopy. Key article trends show expertise in MRI classification , optical tomography , deep learning for imaging, and scattering media analysis . Collaborations include technical development in optoacoustics and super-resolution microscopy. Contact: jonathan.dong@epfl.ch | Office: BM 4141, EPFL, Station 17, Lausanne
Dmitry Vetrov serves as a Professor of Computer Science at Constructor University in Bremen, where he founded and leads the Bayesian Methods Research Group. His academic foundation includes graduation from Moscow State University in 2003 and completion of his PhD in 2006, establishing a career centered on advancing probabilistic machine learning methodologies. His educational trajectory features: Undergraduate studies at Moscow State University (2003) Doctoral degree (PhD, 2006) Vetrov's research program critically bridges Bayesian statistics with deep learning architectures, with his group pioneering efficient diffusion model algorithms, loss landscape characterization in neural networks, scalable stochastic optimization tools, tensor decomposition applications for large-scale ML systems, and enhanced conditional text generation frameworks. This work manifests practical implementations across generative AI domains while maintaining theoretical rigor in probabilistic modeling. Analysis of his 2024-2025 publications reveals a concentrated research thrust toward diffusion model innovation, spanning text generation (token embedding smoothing, language model encoding properties), image synthesis (hair transfer, gesture generation), and scientific applications (protein modeling, genetic fine-mapping). Key thematic threads include sampler acceleration, theoretical property analysis of diffusion processes, and robust evaluation frameworks for generative systems. No scientific awards were documented in the source materials. Mentorship outcomes demonstrate significant impact, with three recent PhD students securing research positions at DeepMind. While specific grant details remain undisclosed, the group's prolific output across NeurIPS, ICML, and CVPR indicates sustained research funding. The Bayesian Methods Research Group operates as an integrated innovation hub within Constructor University's academic ecosystem. The research collective he directs maintains active development of Bayesian-deep learning fusion techniques, with current projects emphasizing diffusion model efficiency, theoretical foundations of optimization landscapes, and cross-domain applications in computational biology and multimodal generation.