جست‌وجوی استاد و پژوهشگر

Caglar Gulcehreمشاهده پروفایل

استادیار

• Reinforcement Learning
• Foundation Models
• Large Language Models
• +۶ مورد دیگر

Caglar Gulcehre is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) and Principal Investigator of the CLAIRE (Caglar Gulcehre Laboratory of Artificial Intelligence Research) lab. Previously, he worked as a Staff Research Scientist at Google DeepMind, Microsoft Research, and IBM Research. His research focuses on reinforcement learning , foundation models , LLM alignment , and sequence modeling . Current Position : Assistant Professor, EPFL Lab : CLAIRE Lab Previous Roles : Staff Research Scientist at DeepMind, MSR, IBM Research His work spans reinforcement learning , deep learning , and neural architecture design , with a focus on safety , trustworthy AI , and real-world applications . He has published in top venues including Nature , NeurIPS , ICML , and JMLR . Scientific contributions include: Best paper award at NeurIPS Nonconvex Optimization workshop Honorable mention for best paper at ICML 2019 Co-organizer of seven workshops at NeurIPS, ICML, and ICLR He supervises PhD students in areas related to AI for algorithm discovery , neural architectures , and foundation models , including: Skander Moalla Justin Samuel Deschenaux Liangze Jiang Xiuying Wei Yitao Xu

Lucas Paoliمشاهده پروفایل

پژوهشگر

• Microbiome Research
• Microbial Immunity
• Ecological Genomics
• +۴ مورد دیگر

Lucas Paoli is a Researcher and Course Instructor at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He leads the Paoli Lab (Microbiome Immunity and Ecology) within the Global Health Institute (GHI) under the School of Life Sciences (SV). His roles include scientific collaboration and teaching responsibilities in life sciences engineering. Paoli holds a PhD in Microbiome Research from ETH Zürich (2018-2023), an M.Phil. in Environmental Policy from the University of Cambridge (2017-2018), and an M.Sc. in Ecology and Evolution from École normale supérieure (2015-2017). Research Focus: His work bridges microbial immunity and ecology, investigating how microbes defend against viral infections across ecosystems like oceans and human microbiomes. Techniques include global-scale metagenomics and functional genomics to study immune strategies and ecological interactions. Key themes involve microbial community dynamics, viral defense mechanisms, and the impact of environmental factors on microbial immunity. Lab & Education: The Paoli Lab explores microbiome-immunity interactions with a focus on ecological contexts. Paoli supervises PhD students in Life Sciences Engineering. His research outputs span microbial ecology, metagenomic tool development, and marine microbiome studies. Contact: lucas.paoli@epfl.ch, AAB 1 39, +41 21 693 16 99.

Paolo Tonellaمشاهده پروفایل

استاد

• Software Engineering
• Software Testing and Analysis
• Web Engineering
• +۴ مورد دیگر

Paolo Tonella is a Full Professor and Director of the Software Institute at the Faculty of Informatics, Università della Svizzera italiana (USI) in Lugano, Switzerland. He also holds an Honorary Professorship at University College London (UK) and previously led the Software Engineering group at Fondazione Bruno Kessler (Italy). His research focuses on software testing, analysis, and AI-driven systems. He has authored over 200 peer-reviewed papers and 100 journal articles, with an H-index of 72. He teaches courses in Data and Software Engineering and Informatics, including Information Modeling, Probability & Statistics, and Knowledge Search. Key contributions include foundational work on web application testing (ICSE MIP award), evolutionary testing techniques (eToc/EvoSuite tools), and reverse engineering of object-oriented systems. He led the ERC-funded PRECRIME project on anticipatory testing. His recent work addresses AI dependability, autonomous systems testing, and deep learning fault analysis. Scientific awards include the ICSE MIP Award (2001) and ERC Advanced Grant (2018). He has served on editorial boards for major journals like IEEE Transactions on Software Engineering and ACM TOSEM. Current roles include leadership in the Software Institute and organizing the SIESTA summer school.

• Artificial Intelligence
• Deep Learning
• Recurrent Neural Networks
• +۱۲ مورد دیگر

Juergen Schmidhuber is Associate Professor at the Faculty of Informatics of Università della Svizzera italiana and a leading researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). He is also Chief Scientist at NNAISENSE, a company dedicated to building practical general-purpose AI. His work has profoundly influenced modern artificial intelligence, particularly through the development of Long Short-Term Memory (LSTM) networks in 1991, now deployed across billions of devices for speech recognition, machine translation, and virtual assistants. His research interests span Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Universal AI, Meta-Learning, Algorithmic Information Theory, Artificial Curiosity, Robotics , and Low-Complexity Art . He has pioneered mathematically rigorous frameworks for self-improving AI systems and formal theories of creativity and beauty. His work bridges theoretical foundations with real-world applications in computer vision, natural language processing, and autonomous robotics. The recent articles reflect a consistent trajectory of innovation, combining deep theoretical insights with scalable machine learning architectures. His publications emphasize sequence modeling, universal learning, intrinsic motivation, and computational creativity , demonstrating both foundational contributions and industrial impact. From LSTM to Goedel machines, his work consistently targets the long-term goal of self-improving general AI. Scientific Awards: Numerous awards in AI and machine learning (specific names not listed) Schmidhuber leads a research group at IDSIA, where he mentors students and researchers in advancing the frontiers of AI. His lab has secured significant recognition and industrial collaboration, though specific grants are not detailed. He promotes the 'New AI'—general, sound, and relevant to physics—and continues to explore the convergence of intelligence, computation, and the universe. Labs and Teams: Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) NNAISENSE (as Chief Scientist)

Mirko Trajkovskiمشاهده پروفایل

استاد

• Metabolic Diseases
• Obesity
• Insulin Resistance
• +۵ مورد دیگر

Professor Mirko Trajkovski leads the Laboratory of Metabolic Diseases at the Faculty of Medicine, University of Geneva. He completed his PhD at the International Max Planck School in Dresden (2005), followed by postdoctoral research at ETH Zurich, before establishing his lab at University College London (2012) and moving to Geneva (2013). His work focuses on adipose tissue plasticity , gut microbiota , and their roles in obesity , diabetes , and insulin resistance . Swiss National Science Foundation Professor (2014) ERC Starting Grant (2014) & Consolidator Grant (2019) Dr Walter Seipp Prize & Carl Gustav Carus Prize (2005) His lab investigates fat browning mechanisms , microbiota-host communication , and multi-tissue metabolic regulation using in vivo , in vitro , and human cohort approaches. Recent publications emphasize microbiome-based therapies , temperature effects on metabolism , and gut-bone-adipose crosstalk . Current advisees include PhD student Silas Kieser, with past members like Jing Xue, Salvatore Fabbiano, and Claire Chevalier contributing to immuno-metabolism and microbial engineering projects.

Soeren Lienkampمشاهده پروفایل

استادیار

• Molecular Biology
• Genetics
• Developmental Biology
• +۵ مورد دیگر

Prof. Dr. Soeren Lienkamp is an Assistant Professor at the Institute of Anatomy , Faculty of Medicine , University of Zurich . His work bridges digital education and genetic research , focusing on enhancing medical teaching through innovative formats. Research Interests : Genetics, developmental biology, kidney disease modeling, CRISPR applications, digital medical education, and advanced microscopy. Methodologies : Combines Xenopus tropicalis models, deep learning , and bioengineering to study genetic kidney disorders and improve diagnostic tools. Publication Trends : His recent articles highlight predictable genome editing , 3D imaging technologies , and mechanistic insights into kidney and eye development. Earlier works focus on ciliary function , Wnt signaling , and metabolic stress in renal cells.

Sebastijan Dumancicمشاهده پروفایل

استادیار

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

Yuval Noah Harariمشاهده پروفایل

مدرس

• World History
• Medieval History
• Military History
• +۷ مورد دیگر

Yuval Noah Harari is a historian, philosopher, and lecturer at the Department of History at the Hebrew University of Jerusalem. Born in Haifa, Israel in 1976, he received his PhD from the University of Oxford in 2002. He is best known as the author of the internationally acclaimed books Sapiens: A Brief History of Humankind (2014), Homo Deus: A Brief History of Tomorrow (2016), 21 Lessons for the 21st Century (2018), Nexus: A Brief History of Information Networks from the Stone Age to Artificial Intelligence , and the Unstoppable Us children's series. Harari's research focuses on macro-historical questions including the relationship between history and biology, the essential difference between Homo sapiens and other animals, justice in history, historical directionality, human happiness throughout history, and the ethical questions raised by science and technology in the 21st century. His work bridges history, biology, philosophy, and economics, taking both macro and micro perspectives to understand not only what happened and why, but also how it felt for individuals. His books have sold over 45 million copies in 65 languages, with Sapiens alone selling 25 million copies since its publication. He is considered one of the world's most influential public intellectuals today. Harari has given keynote speeches at major international forums including the World Economic Forum in Davos and has presented as a digital avatar in a TED talk. Sapiens spent 96 consecutive weeks in the top 3 of the Sunday Times bestseller list Co-founded Sapienship, an international social impact company focused on education and storytelling, with his husband Itzik Yahav Regularly speaks at major international events on topics of technology, history, and the future of humanity Harari's recent work focuses on the challenges of the information age, particularly the crisis of truth and trust in an era of disinformation and artificial intelligence. He explores how humans can navigate the complex ethical questions raised by emerging technologies while maintaining democratic values and human dignity.

Rachid Guerraouiمشاهده پروفایل

استاد

• Distributed Computing
• Concurrent Computing
• Byzantine Fault Tolerance
• +۷ مورد دیگر

Rachid Guerraoui is a Full Professor at the École polytechnique fédérale de Lausanne (EPFL) where he leads the Distributed Computing Laboratory (DCL) within the School of Computer and Communication Sciences. He holds appointments in multiple departments including IC-SSC and IC-SIN for teaching, and serves on the IC Academic Evaluation Committee. A Moroccan/Swiss/French researcher, Guerraoui has previously been affiliated with Commissariat à l'Energie Atomique in Saclay, Hewlett-Packard Labs in Palo Alto, the Massachusetts Institute of Technology in Boston, and Collège de France in Paris. Guerraoui's research focuses on distributed and concurrent computing across various scales, from multiprocessors to wide-area networks. His work spans Byzantine fault tolerance, distributed machine learning, blockchain technologies, transactional memory, and consensus algorithms. His recent publications reveal a strong emphasis on Byzantine-resistant machine learning, decentralized learning systems, and the theoretical foundations of distributed consensus. The research demonstrates significant contributions to making distributed systems more robust, efficient, and secure against adversarial conditions. Guerraoui has received numerous prestigious awards including ACM Fellow (2012), Professor at College de France (2018), Nygaard-Dahl Award (2024), and Barroso Award (2025). His work has earned multiple best paper awards at top conferences including DISC, ICDCS, IPDPS, and ACM Middleware. He serves as Associate Editor of the Journal of the ACM (2010-2025) and has chaired program committees for major conferences such as PODC, DISC, and Middleware. As an educator, Guerraoui supervises numerous doctoral students and has mentored many successful researchers who now work at leading institutions and companies including Meta, Oracle Labs, Chainlink Labs, and Protocol Labs. He teaches courses on Distributed Algorithms and Concurrent Algorithms at EPFL, emphasizing both theoretical foundations and practical implementations. His educational initiatives include Wandida, a library of scientific e-synopses, and Zettabytes, projects aimed at making computer science accessible to broader audiences.

Chen Liuمشاهده پروفایل

استادیار

• Machine Learning
• Adversarial Robustness
• Privacy-Preserving Machine Learning
• +۹ مورد دیگر

Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.

Kathryn Hess Bellwaldمشاهده پروفایل

استاد

• Algebraic Topology
• Homotopy Theory
• Neuroscience
• +۴ مورد دیگر

Kathryn Hess Bellwald is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in both the School of Life Sciences and School of Basic Sciences . She leads the Laboratory for Topology and Neuroscience and serves as Academic Director for the Euler Programme . Her work bridges pure mathematics and interdisciplinary applications in neuroscience, materials science, and data analysis. Education : PhD in Mathematics (MIT, 1989), preceded by positions at Stockholm, Nice, and Toronto universities. Her research spans algebraic topology , homotopy theory , operad theory , and algebraic K-theory , with applications in neuroscience and materials science . She has pioneered topological data analysis methods for classifying neuronal morphologies , microglia phenotypes , and nanoporous materials , creating a parameter-free framework linking neural network structure to activity. The 15 most recent publications highlight her work on topological inverse problems , neuroinflammation , and equivariant homotopy . These studies often involve collaborations with the Blue Brain Project and EPFL teams in neuroscience , machine learning , and materials science . Scientific Awards : Fellow, American Mathematical Society (2017); Distinguished Speaker, European Mathematical Society (2017); Crédit Suisse Teaching Prize (2012); Polysphère d'Or (2013); Full Member, Swiss Academy of Engineering Sciences (2016); Chaire de la Vallée Poussin (2023); Fellow, Association for Women in Mathematics (2024). She has mentored numerous PhD students in mathematics and neuroscience, including Adélie Eliane Garin , Varvara Karpova , and Dimitri Zaganidis . Her EPFL Mathematics affiliations include the DIVISION MATH , while her Neuroscience lab operates under the Brain Mind Institute (BMI) in the School of Life Sciences (SV). Grants and collaborations are evident in her work on neurodegenerative diseases , synthetic materials , and machine learning frameworks .

Dietmar Maringerمشاهده پروفایل

استاد

• Computational Economics
• Computational Finance
• Artificial Intelligence in Finance
• +۸ مورد دیگر

Dietmar Maringer is Professor of Computational Economics and Finance at the University of Basel's Faculty of Business and Economics (WWZ), where he leads research at the intersection of finance, computational methods, and artificial intelligence. His work focuses on risk management, portfolio optimization, algorithmic trading, and financial simulations. His research interests span computational finance, artificial intelligence in finance, data analysis, risk management, portfolio optimization, algorithmic and high-frequency trading, financial networks, complex adaptive systems, and market simulations. He applies advanced computational and heuristic optimization techniques to solve real-world financial problems, contributing significantly to quantitative finance and financial engineering. His recent publications demonstrate a consistent focus on applying evolutionary algorithms, reinforcement learning, and numerical optimization to portfolio management, market impact modeling, and financial forecasting. The research integrates econometrics, machine learning, and financial theory, emphasizing practical implementation and robust risk-aware decision-making. Several best-paper awards Maringer has served as Chair of the Portfolio Optimization Section of the IEEE Computational Economics and Finance Technical Committee from 2008 to 2018 and is frequently involved in organizing and program committees of international conferences. He has advised or collaborated with numerous researchers, though specific student names are not listed. His research has been supported through academic affiliations and likely institutional or conference-based grants, though explicit funding sources are not detailed. He is affiliated with several research groups, including IEEE Computational Economics and Finance TC, COMISEF, ERCIM, Centre for Innovative Finance, and the European Financial Management Association, reflecting a broad collaborative network in computational finance and economics.

Lenka Zdeborováمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

Qiang Cuiمشاهده پروفایل

استاد

• Computational Chemistry
• Biophysics
• Molecular Biology
• +۳ مورد دیگر

Qiang Cui is a Professor of Computational Chemistry at Boston University, specializing in developing and applying advanced computational methods to study complex biomolecular systems. His research focuses on understanding mechanisms of enzymes, biomolecular machines, and bio-material interactions through multi-scale simulations, including quantum mechanical/molecular mechanical (QM/MM) approaches and coarse-grained models. Education: B.S., Chemical Physics, University of Science & Technology of China (1993) Ph.D., Physical Chemistry, Emory University (1997) Postdoctoral Associate, Harvard University (1998-2001) Research Interests: Development of novel computational techniques for simulating complex systems Study of energy transduction in molecular machines (e.g., myosin, DNA repair enzymes) Investigation of biomaterial interfaces and nanotechnology applications Protein allostery and mutational effects using machine learning Labs/Teams: The Cui Group at Boston University advances computational methodologies and collaborates on projects spanning biophysics, material science, and molecular biology.

Joel Zindelمشاهده پروفایل

استاد

• Tissue Repair
• Macrophage Biology
• Mesothelial Cell Biology
• +۳ مورد دیگر

Prof. Dr. Joel Zindel is a faculty member at ETH Zurich's Institute of Molecular Health Sciences, leading a translational research group focused on tissue repair and scarring. He previously established his lab at the University of Bern (2023) before migrating to ETH Zürich in August 2025. His work bridges clinical surgery and immunology, targeting post-surgical complications like adhesions and fibrosis. Education: Medical Degree & MD, University of Bern PhD in Immunology, University of Bern SNSF-sponsored Research Fellowship, Canada Research Focus: The Zindel Lab investigates three key areas: (i) peritoneal macrophage biology in injury detection, (ii) mesothelial-to-mesenchymal transition in scarring, and (iii) foreign body responses in body cavities. Using intravital microscopy and clinical cohort studies, the lab explores immune-mesothelial interactions and druggable pathways. Publication Trends: Recent work emphasizes macrophage roles in regeneration, EGFR signaling in fibrosis, and technological innovations in surgical skill assessment. Key methodologies include spatial transcriptomics and multiphoton imaging. Scientific Awards: Max Cloëtta Research Fellowship SNSF Starting Grant Advising & Collaborations: Leads a team including PhD students (John Li Flores Alvarez, Agnes Huber, Brooke Slade) and postdoc Tea Kocijan. Collaborates with (bio)material engineers like Prof. Inge Herrmann and Prof. Mark Tibbitt.