جستجوی استاد راهنمای دانشگاهی

Antoine Bosselutمشاهده پروفایل

استادیار

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Antoine Bosselut is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the EPFL NLP group. His research focuses on developing AI reasoning agents that can model, represent, and reason about human and world knowledge, with applications in health, education, and global fairness. His research interests span multiple critical areas in modern AI: LLM Representations of Knowledge: Understanding what language models know and how they represent that knowledge internally Reasoning Algorithms: Developing methods to improve LLMs' reasoning capabilities through symbolic systems, neuroscience, and cognitive science Large-scale AI Development: Creating open-source foundation models with multilingual capabilities AI Democratization: Ensuring equitable access to AI technologies across different cultural and regulatory contexts His recent publications reveal a strong focus on the intersection of language models and cognitive science, with multiple studies examining how LLMs align with human cognition. He's also deeply engaged in practical applications of NLP technology, particularly in education and multilingual settings, as evidenced by his work on evaluating AI's impact on higher education and developing culturally-aware language models. His scientific achievements have been recognized with several prestigious awards: Outstanding Paper Award at NAACL 2025 ELLIS Scholar designation in 2024 Outstanding Paper Award at ACL 2023 Inclusion in Forbes 30 Under 30 list for Science & Healthcare in 2021 Bosselut actively mentors numerous PhD students across diverse NLP research areas and has secured significant recognition for his work in both academic and media circles. His lab has been featured in major publications including Communications of the ACM, The Atlantic, and Quanta Magazine, highlighting the societal impact of his research on commonsense reasoning and AI capabilities.

Robert Westمشاهده پروفایل

دانشیار

• Natural Language Processing (NLP)
• Machine Learning
• Computational Social Science
• +۴ مورد دیگر

Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.

Pascal Frossardمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Graph Signal Processing
• +۶ مورد دیگر

Pascal Frossard is a Full Professor at the Department of Electrical Engineering in the School of Engineering (STI) at EPFL, with a courtesy appointment in the School of Computer and Communication Sciences. He founded and directs the LTS4 laboratory since 2003, co-leads the EPFL AI Center and Swiss Data Science Center, and serves as Associate Dean for Research at STI. Research Focus: Machine Learning, Graph Signal Processing, AI Applications in Healthcare, Computer Vision Academic Leadership: IEEE Fellow, ELLIS Fellow, Conference Chair roles Key Projects: Digital Pathology for Oncology, Cardiac Digital Twins, Robust Machine Learning Research Interests: His work bridges signal processing, machine learning, and applied mathematics, emphasizing biomedical applications. Recent research includes adversarial robustness in classifiers, network representation learning, and 360-degree video analysis. Scientific Awards: IEEE Fellow ELLIS Fellow Leadership in IEEE technical committees Advising & Grants: Supervised 20+ PhD students and postdocs. Secured major grants from PHRT, Hasler Foundation, FNS-Sinergia, Armasuisse, Google, and Cisco.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Elisa Ricciمشاهده پروفایل

استاد

• Computer Vision
• Deep Learning
• Robotics Perception
• +۵ مورد دیگر

Elisa Ricci is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento and serves as Head of the Research Unit Deep Visual Learning at Fondazione Bruno Kessler. She coordinates the Doctoral Program in Information Engineering and Computer Science at the University of Trento and holds prestigious fellowships from ELLIS and IAPR. Her research focuses on advancing computer vision and deep learning systems capable of operating in open-world environments. Key interests include domain adaptation, continual learning, and self-supervised learning for visual and multi-modal data processing. Her work addresses critical challenges in enabling machines to adapt to new domains without forgetting prior knowledge, with applications spanning robotics perception, medical imaging, and privacy-preserving AI systems. Recent publications reveal a dominant trend toward leveraging vision-language models for open-vocabulary tasks, training-free adaptation methods, and federated learning architectures. Significant research thrusts include machine unlearning for privacy, robustness against bias in visual classifiers, and novel class discovery using foundation models—particularly evident in 2025 publications addressing medical imaging, 3D segmentation, and collaborative generative systems. Her major recognitions include: ELLIS Fellow IAPR Fellow As Doctoral Program Coordinator at the University of Trento, she oversees PhD training while leading the Deep Visual Learning unit at Fondazione Bruno Kessler. Her research group secures competitive grants in AI-driven perception systems, though specific funding sources aren't detailed in the source material. The Deep Visual Learning Research Unit specializes in open-world computer vision challenges, developing frameworks for domain adaptation, continual learning, and multi-modal perception. Current projects integrate generative models with robotics applications while addressing privacy concerns in vision-language systems through unlearning techniques.

Michael Moorمشاهده پروفایل

استادیار

• Medical AI
• Machine Learning in Healthcare
• Causal Learning
• +۳ مورد دیگر

Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.

Armelle Choplinمشاهده پروفایل

دانشیار

• Urban Planning
• Governance
• Construction Sector
• +۷ مورد دیگر

Armelle Choplin is an Associate Professor at the University of Geneva's Department of Geography and Environment within the Geneva School of Social Sciences, and affiliated with the Global Studies Institute since 2019. She serves as Director of the Institute of Environmental Governance and Territorial Development (IGEDT), leading research on urban transformations in the Global South. Research Focus Her research examines urban policies, social transformations, and development challenges in African cities and the Global South. Core interests include: urban planning dynamics, governance frameworks, construction industry impacts, poverty alleviation, sustainable development pathways, globalization effects, digital innovation in urban contexts, and postcolonial urban studies. Her geographical specialization focuses on West Africa and MENA regions. Publication Analysis Recent publications (2017-2024) demonstrate consistent focus on material urbanism, governance innovations, and spatial justice in African cities. Key thematic clusters include: urban material flows (concrete, energy), participatory planning methods, transnational urban corridors, and postcolonial urban theory. Methodologically, her work combines spatial analysis with political economy and ethnographic approaches. Research Leadership She directs multiple international projects including: Global Urb: Global Urbanism from the South MatéRhône: Construction materials in Rhone Valley West African Coastal Corridor study (Accra-Lagos axis) URBACOT: West African Coastal Urban Dynamics Observatory Map&Jerry: Participatory mapping in Cotonou Student Advising Currently supervising eight doctoral candidates researching urban development topics across Africa. Her teaching portfolio includes courses on urban globalization, metropolitan governance, spatial justice, and regional project development at both bachelor's and master's levels.

Freek Persynمشاهده پروفایل

استاد

• Urban Design
• Spatial Planning
• Urban and Social Transformation
• +۵ مورد دیگر

Freek Persyn is a Full Professor of Architecture and Urban Transformation at ETH Zurich, where he has held the NEWROPE Professorship since 2019. He leads the Design in Dialogue Lab within the Department of Architecture, focusing on spatial design and social innovation. Persyn co-founded the spatial planning firm 51N4E in 1998, which specializes in urban and social transformation across Europe. Persyn's research centers on the concept of 'Design in Dialogue,' which emphasizes transdisciplinary collaboration to address the complexity of contemporary urban challenges. His work explores innovative solutions for European urban spaces, aiming to rediscover Europe as a habitable terrain and shared living space. Key research areas include urban design, spatial planning, social transformation, and the intersection of architecture with social innovation. The NEWROPE initiative seeks to examine diverse urban practices that shape Europe, fostering radical openness and learning from various urban practitioners through transdisciplinary projects. His scholarly output demonstrates a strong focus on reimagining urban spaces through dialogue-based approaches. Persyn's work examines European urban transformation, rural-urban connections, industrial heritage sites, and innovative design methodologies. Key themes across his publications include sustainable approaches to building, the social dimensions of space, and transformative design processes that challenge conventional architectural practices. His research often bridges theoretical frameworks with practical design applications, emphasizing participatory methods and material investigations. Persyn delivered his inaugural lecture at ETH Zurich on October 20, 2021, titled 'Wouldn't it be nice if architects started dreaming about building less?' The lecture explored his professional background, inspiration, and future plans for the NEWROPE Chair. The event featured collective sensing exercises developed by students of Studio Seebach with theater and dance maker Manuela Runge, highlighting his commitment to experiential and collaborative approaches to architectural education and practice. The Design in Dialogue Lab serves as a hub for exploring innovative approaches to urban transformation through projects across European cities including Brussels, Piraeus, Basel, and Plovdiv. Current initiatives examine topics such as urban spaces for more than human communities, collaborative processes, design approaches, and hybrid applications for European urban environments. Persyn's work consistently emphasizes transdisciplinary collaboration, bringing together spatial and strategic knowledge with other disciplines and urban practitioners to address complex contemporary challenges.

• Educational Technology
• Human-Computer Interaction
• Data Science
• +۴ مورد دیگر

Prof. April Wang is an Assistant Professor in the Department of Computer Science at ETH Zurich. Her work focuses on Educational Technology, Human-Computer Interaction, and AI-driven tools for programming education. She is affiliated with the Professur Educational Technology group and teaches courses such as Human Computer Interaction and Ethics in Computing. Her research explores interactive learning environments, collaborative data science workflows, and AI-augmented educational systems. Her research interests span computational notebooks, live coding interfaces, and human-AI collaboration in software development. Notable contributions include frameworks like DBox for algorithm learning and EDBooks for interactive programming narratives. She also investigates challenges in real-time collaboration tools and automated documentation systems for data scientists. Recent work emphasizes understanding user needs in automated copilot systems and resolving conflicts in collaborative notebook environments. Her projects often bridge theoretical HCI principles with practical educational tools, aiming to enhance both instructor efficacy and student engagement. Prof. Wang’s teaching portfolio includes courses on user-centered interface design and ethical scientific practices. She actively contributes to advancing interdisciplinary computational literacy through initiatives like datAR, which uses everyday objects to teach data concepts.

Dominique Forayمشاهده پروفایل

استاد

• Economics of Innovation
• Science and Technology Indicators
• Technology Policy
• +۳ مورد دیگر

Dominique Foray is a Professor at Ecole Polytechnique Fédérale de Lausanne (EPFL) and holds the Chair of Economics and Management of Innovation (CEMI). He is a member of the Swiss Council for Science, chairman of the KOF Swiss Economic Research Institute's Advisory Board, and a foreign member of Columbia University's Center of Capitalism and Society. His career includes extensive advisory roles, such as with the European Commission's 'Knowledge for Growth' group and Germany's Expert Commission for Research and Innovation. Education: Ph.D. in Economics, University Lumière of Lyon (1984) Habilitation à diriger des recherches, University Lumière of Lyon (1992) Foray's research focuses on the economics of innovation, knowledge-based economy dynamics, technology policy, smart specialization strategies, and institutional frameworks for innovation. He has authored seminal works including The Economics of Knowledge (MIT Press, 2004) and Smart Specialisation (Routledge, 2015). His recent publications explore mission-oriented innovation policies, smart specialization in EU contexts, and knowledge diffusion mechanisms. Foray's work bridges academic theory with practical policy design, influencing regional and national innovation strategies globally. Advising: Past EPFL PhD students: Aouinait Camille Marguerite, Ayoubi Charles Chadi, Baruffaldi Stefano Horst, Bednyagin Denis, Bogers Marcellus, Coffano Monica, Conti Annamaria, Cristelli Gabriele, Gaulé Patrick, Hamdan Livramento Intan Maizurah, Marino Marianna, Muñoz Tellez de Kieffer Viviana Carolina, Pellegrin Claudia, Simeth Markus Foray's institutional roles include leadership in European policy initiatives and contributions to the World Bank's studies on innovation's role in economic growth. His interdisciplinary approach integrates economics, policy analysis, and technology management to address grand societal challenges through innovation.

Christian Erik Pohlمشاهده پروفایل

استاد مدعو

• Environmental Science
• Sustainability Science
• Transdisciplinary Research
• +۳ مورد دیگر

Prof. Dr. Christian Erik Pohl is an Adjunct Professor at ETH Zurich's Department of Environmental Systems Science and Head of the Institute for Environmental Decisions (IED) and the Transdisciplinarity Lab (TdLab). His research focuses on advancing transdisciplinary approaches to address complex environmental and societal challenges, emphasizing sustainable development. He holds a doctorate and habilitation in environmental science from the University of Bern. Key roles include membership in the Swiss National Science Foundation’s NRP 82 steering committee and the scientific advisory board of td-net. His contributions include seminal works like Principles for Designing Transdisciplinary Research and the Handbook of Transdisciplinary Research , fostering global collaboration in sustainability science. Awards include the KITE Award (2020) for teaching excellence and the Swiss-academies award (2015). He co-leads the TdLab, advancing methods for integrating academic and societal knowledge to tackle issues like biodiversity, water management, and climate change. His courses include 'Tackling Environmental Problems' and 'Transdisciplinary Research: Challenges of Interdisciplinarity and Stakeholder Engagement,' reflecting his commitment to education in sustainability science.

Thomas Demeesterمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +۷ مورد دیگر

Thomas Demeester is an Associate Professor at the Internet Technology and Data Science Lab (IDLab), Ghent University - imec, Belgium. Appointed as Assistant Professor in 2019, he leads an AI research group focused on health applications and drug design, co-directing the Text-to-Knowledge research cluster with Prof. Chris Develder. His educational background includes: M.Sc. in Electrical Engineering from Ghent University (2005), completed with thesis work at ETH Zurich Ph.D. in Computational Electromagnetics from Ghent University (2009), funded by Research Foundation - Flanders (FWO) Demeester's research spans artificial intelligence with emphasis on deep learning and neuro-symbolic methods. Current tracks include energy-based models (Hopfield Networks, Deep Equilibrium Models), diffusion models for drug design, and clinical reasoning systems. His work bridges NLP, healthcare informatics, and generative AI with strong industry partnerships. Recent publications (2023-2025) reveal strategic expansion from NLP into health-centric AI: BioLORD biomedical encoders (2023), synthetic medical data frameworks (UAI/NeurIPS 2024), and novel diffusion model guidance (ICLR 2025). This evolution demonstrates convergence of generative modeling, clinical data analysis, and protein design. He actively mentors 24 PhD students across diverse AI domains: Current Research: Conversational agents, emotion analysis, clinical reasoning, antibody design, and diffusion model optimization Recent Graduates: Interpretable language models, biomedical semantics, task-oriented dialogue, and social media knowledge extraction Research is supported by imec funding and collaborations with Flemish biotech companies, building on his post-doctoral experience securing media-sector projects. Within IDLab, he co-leads the Text-to-Knowledge cluster driving NLP innovations for healthcare, legal, and economic applications.

Gianni Franchiمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Uncertainty Quantification
• +۲ مورد دیگر

Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.

Siyu Tangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Digital Humans
• +۵ مورد دیگر

Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.

El Mahdi Chaytiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Meta-learning
• +۴ مورد دیگر

El Mahdi Chayti is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Department of Computer Science. He works under the Machine Learning and Optimization (MLO) lab, focusing on advanced optimization techniques and machine learning theory. Contact: el-mahdi.chayti@epfl.ch . Current Roles: Doctoral Assistant and PhD Student in Computer and Communication Sciences Research Interests: Machine Learning, Optimization Algorithms, Meta-learning, Second-order Optimization, Energy Forecasting His publications highlight expertise in stochastic and cubic Newton methods, hybrid deep learning models, and personalized collaborative learning. Recent works include Improving Stochastic Cubic Newton with Momentum (2025) and Hybridization of Deep Learning with Physical Knowledge for Energy Forecasting (2019) . Key trends in his research span optimization efficiency , meta-learning frameworks , and integration of domain knowledge into AI models. He emphasizes theoretical guarantees and practical scalability in algorithm design.