جست‌وجوی استاد و پژوهشگر

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.

• Machine Learning
• Statistics
• Theoretical Machine Learning
• +۷ مورد دیگر

Fanny Yang is an Assistant Professor in the Computer Science Department at ETH Zurich . She previously held postdoctoral positions at Stanford University and a Junior Fellowship at the Institute for Theoretical Studies at ETH Zurich, advised by Nicolai Meinshausen . Her PhD was completed at the EECS Department of UC Berkeley , supervised by Martin Wainwright . Research Interests : Theoretical foundations of machine learning and statistics , particularly focusing on overparameterized models and high-dimensional data . Developing trustworthy ML models with emphasis on distributional robustness , domain generalization , and interpretability . Applications in medical diagnostics and treatment effect analysis , aiming to address reliability issues in real-world domains. Recent Work Trends : 2025 Articles : Theoretical analysis of semi-supervised multi-objective learning , test-time scaling with verifiers, and foundation models for efficient randomized experiments . 2024 Articles : Studies on robust mixture learning , privacy-preserving data synthesis via optimal transport , confounding quantification in causal inference , and semi-private learning frameworks. 2023 Articles : Investigations into active vs. passive learning in high dimensions, inductive bias in noisy interpolation , and semi-supervised novelty detection using model ensembles .

• Distributed Computing
• Wireless Networks
• Distributed Systems
• +۴ مورد دیگر

Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.

Maksym Andriushchenkoمشاهده پروفایل

پژوهشگر

• AI Safety
• Adversarial Robustness
• LLM Alignment
• +۳ مورد دیگر

Maksym Andriushchenko is an incoming faculty member at the ELLIS Institute Tübingen and Max Planck Institute for Intelligent Systems , where he will lead the AI Safety and Alignment group as a principal investigator. Currently, he is a Doctoral Assistant at the Theory of Machine Learning Laboratory (TML) within the Department of Computer Science (IINFCOM) at Swiss Federal Institute of Technology in Lausanne (EPFL) . His work focuses on AI safety, adversarial robustness, and alignment of large language models (LLMs) with societal values.

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.

Gonzalo Guillén Gosálbezمشاهده پروفایل

استاد

• Process Systems Engineering
• Sustainable Chemistry
• Environmental Impact Assessment
• +۷ مورد دیگر

Prof. Dr. Gonzalo Guillén Gosálbez is a Full Professor at the Department of Chemistry and Applied Biosciences , ETH Zürich. He holds a PhD in Process Systems Engineering (UPC, 2005) and has held academic positions at Imperial College London (Reader), University of Manchester (Senior Lecturer), and Universitat Rovira i Virgili (Assistant/Associate Professor). His research focuses on Sustainable Chemical Processes , integrating life cycle assessment, optimization techniques, and planetary boundary analysis to evaluate and design low-carbon technologies. Current position: Full Professor, ETH Zürich (2019–present) Prior roles: Imperial College London (2016–2019), University of Manchester (2014–2016), URV Spain (2008–2014) Education: PhD (UPC, 2005), MEng/BEng (University of Murcia) His research explores CO2 valorization , green methanol synthesis , circular marine fuels , and planetary boundary compliance in energy and chemical systems. Recent work emphasizes machine learning for process modeling, single-atom catalysis , and decentralized ammonia production . Scientific contributions include 15+ peer-reviewed articles (2023–2025) in journals like Nature Chemical Engineering , ACS Sustainable Chemistry & Engineering , and Energy & Environmental Science . Key themes: Optimization of hybrid fossil/renewable carbon systems Environmental impacts of energy transitions Catalyst design for sustainable chemistry Life cycle assessment of emerging technologies Awarded UPC Top Doctoral Student Award and Top National Student Award , he combines process systems engineering with sustainability metrics to address global challenges in chemical and energy systems.

Peter A. Flachمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Explainable AI (XAI)
• +۳ مورد دیگر

Peter A. Flach is Professor of Artificial Intelligence at the Intelligent Systems Laboratory, School of Computer Science, University of Bristol, where he has been faculty since 1997 and was promoted to Professor in 2003. He currently directs the UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence and serves as Vice-President of the European Association for Data Science (EuADS), having previously served as its President. His research centers on rigorous evaluation and improvement of machine learning systems, with seminal contributions to classifier calibration (including Beta Calibration and Precision-Recall-Gain curves), explainable AI frameworks, and data science methodology. He pioneered the extension of CRISP-DM to data science trajectories and developed Explainability Fact Sheets for systematic assessment of XAI approaches. His work bridges theoretical foundations with practical deployment in real-world systems. Analysis of his recent publications reveals a dominant focus on interpretable and reliable machine learning, with strong emphasis on performance evaluation metrics, model calibration techniques, and human-centered explainability. His research increasingly addresses healthcare applications through projects like SPHERE and clinical decision support systems, while maintaining core contributions to fundamental ML theory. His scientific recognition includes prestigious fellowships: Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) Fellow of the European Association for Artificial Intelligence (EurAI) Professor Flach has secured major research funding including UKRI Centre for Doctoral Training grants and EU network funding (TAILOR). He leads extensive collaborations across Engineering, Population Health Science, and international institutions (Monash University, Polytechnic University of Valencia), plus over 20 industry partners in the Practice-Oriented AI CDT including LV= and QinetiQ. His work with the SPHERE project demonstrates successful translation of AI research into residential healthcare settings. He leads the Intelligent Systems Laboratory at Bristol, which develops influential open-source tools including the FAT Forensics Python toolbox for algorithmic fairness and the Explainability Fact Sheets framework. His lab maintains strong connections with the European AI community through ELLIS and EurAI, positioning Bristol as a hub for human-centered and methodologically rigorous AI research.

• Ethical Implications of Artificial Intelligence
• Societal Implications of Artificial Intelligence
• Policy Implications of Artificial Intelligence
• +۴ مورد دیگر

Hoda Heidari is the K&L Gates Career Development Assistant Professor in Ethics and Computational Technologies at Carnegie Mellon University (CMU), with joint appointments in the Machine Learning Department and the Institute for Software, Systems, and Society. She is affiliated with the Human-Computer Interaction Institute and the Heinz College of Information Systems and Public Policy, and co-leads the university-wide Responsible AI Initiative and K&L Gates Initiative for Ethics and Computational Technologies. Education: PhD in Computer and Information Science (University of Pennsylvania), MSc in Statistics (Wharton School) Her research focuses on the Ethical, Societal, and Policy Implications of AI , particularly fairness and accountability in high-stakes domains. Her work includes evaluating risks/benefits of general-purpose AI, human-AI decision-making systems, and AI governance frameworks. She has received multiple awards, including best paper honors at AIES, FAccT, and SAT-ML. Her research is supported by the NSF Program on Fairness in AI, PwC, CyLab, Meta, and J. P. Morgan. Recent Publications examine generative AI safety, fairness measurement, AI incident documentation, and ethical governance. Her teaching includes courses on Responsible AI, ML Ethics, and Societal Decision-Making, with a focus on preparing students to critically analyze AI's societal impact. Scientific Awards: Best Paper (AIES 2024, FAccT 2021, SAT-ML 2023), Exemplary Track Award (EC 2021) Grants: NSF, PwC, CyLab, Meta, J. P. Morgan She advises doctoral students and postdocs across CMU departments and collaborates with interdisciplinary teams. Her service includes organizing AI safety workshops and advising on NIST guidelines for AI red-teaming.

Margarita Boenig-Liptsinمشاهده پروفایل

استادیار

• Social and political aspects of digital technologies
• Identity and citizenship in technological societies
• Ethics of Technology
• +۵ مورد دیگر

Margarita Boenig-Liptsin is a tenure-track Assistant Professor of Ethics, Technology and Society at ETH Zurich's Department of Humanities, Social and Political Sciences, a position she has held since September 2022. Trained in Science, Technology and Society with dual PhDs from Harvard University (History of Science) and Université Paris-Sorbonne (Philosophy), she brings interdisciplinary expertise to examining the relationship between technology, power, democracy, and ethics. Her research spans several interconnected areas focusing on transformations to human identity and citizenship in relation to information technologies across time and cultures. She investigates the social and political aspects of digital technologies, identity and selfhood in technological societies, ethics of technology, and comparative cultures of innovation. Her work uniquely bridges historical analysis with contemporary ethical concerns, examining how concepts like human dignity evolve alongside technological developments from punch cards to AI governance frameworks. Her recent publications (2023-2025) reveal a strong focus on AI ethics, data justice, and the philosophical implications of algorithmic systems. These works demonstrate her consistent interest in how technology shapes democratic processes, citizenship, and ethical frameworks. Her research shows progression from historical studies of computer literacy programs to contemporary analyses of generative AI's impact on knowledge production and everyday ethics. Her scientific recognition includes: NSF funding for her collaborative project on Silicon Valley's innovation imaginary Mozilla Foundation Responsible Computer Science Challenge grant (2018-2021) Academic Data Science Alliance support (2020-2022) Sorbonne Université's Paris IAS Chair on 'Major Changes' (2021-2022) At ETH Zurich, she leads research and teaching initiatives that build connections between engineering and social sciences. Previously at UC Berkeley, she directed the Human Contexts and Ethics Program, developing the Human Contexts and Ethics component for data science education. Her work consistently emphasizes translating social science theory for diverse audiences and connecting scholarly work to community needs in Zurich, Switzerland, and Europe. She teaches courses including 'Artificial Intelligence and Human Values' and 'Research in Ethics, Technology and Society' for the Fall 2025 semester.

Devis Tuiaمشاهده پروفایل

دانشیار

• Earth Observation
• Remote Sensing
• Machine Learning
• +۵ مورد دیگر

Devis Tuia serves as Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), holding appointments in the Institute of Environmental Engineering (IIE) within the School of Architecture, Civil and Environmental Engineering (ENAC). He leads the Environmental Computational Science and Earth Observation Laboratory (ECEO) since 2020 and contributes to EPFL's Doctoral Program in Civil and Environmental Engineering. His academic journey began in Lausanne with studies at UNIL and EPFL, culminating in a PhD in remote sensing from UNIL. Postdoctoral research followed at institutions in Valencia, Boulder, and EPFL, focusing on machine learning model adaptation. He progressed from Research Assistant Professor at University of Zurich to Associate and Full Professor at Wageningen University before joining EPFL. Tuia's research bridges Earth observation with artificial intelligence, specializing in interpretable deep learning for environmental applications. His lab develops algorithms for making remote sensing accessible, with particular emphasis on digital wildlife conservation through automated censuses using drone and satellite imagery. Current projects tackle the 'black box' problem in environmental modeling while advancing spatial intelligence for sustainable urban development. His 2023-2025 publication portfolio reveals three dominant trends: (1) species distribution modeling using incomplete observations, (2) multimodal fusion of satellite/drone data with textual descriptions, and (3) interpretable AI frameworks for environmental decision-making. This work consistently addresses real-world challenges like wildfire forecasting and biodiversity monitoring. As an educator, Tuia supervises 12 current PhD students and has graduated 4 former EPFL doctoral candidates. His teaching portfolio includes Frontiers of Deep Learning for Engineers , Sensing and Spatial Modeling for Earth Observation , and Image Processing for Earth Observation courses. The ECEO laboratory maintains active collaborations with ESA-NASA initiatives and conservation organizations globally.

Andrea Cavallaroمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Andrea Cavallaro is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and Director of the Idiap Research Institute. He holds dual appointments in the School of Engineering (STI) within the Institute of Electrical Engineering and Measurements (IEM) and the School of Engineering's Education Unit (SEL-ENS). His research focuses on machine learning for multimodal perception, privacy-preserving AI, and autonomous systems. Cavallaro earned his PhD in Electrical Engineering from EPFL in 2002 and has held leadership roles including Director of Research at Queen Mary University of London and Turing Fellow at The Alan Turing Institute. Education: PhD in Electrical Engineering (EPFL, 2002) Leadership: Idiap Director, Affiliate at ELLIS Society Editorial Roles: Editor-in-Chief of Signal Processing: Image Communication (2020–2023), Senior Area Editor for IEEE Transactions on Image Processing Research Interests: Machine learning for audio-visual sensing, privacy in AI, autonomous systems perception, and ethical AI frameworks. Key projects include AlignAI (trustworthy AI alignment) and CORSMAL (multimodal object manipulation). Recent articles explore privacy-aware AI models, adversarial attacks, and multimodal perception systems. His work bridges theoretical advancements with practical applications in robotics, healthcare, and education. Awards include the Royal Academy of Engineering Teaching Prize and IAPR Fellowship. Teaching: Leads courses on deep learning ethics and multimodal AI at EPFL. Advising: Supervises 11 PhD students in areas like privacy-preserving algorithms and autonomous systems. Labs/Teams: Coordinates Idiap’s Audiovisual Intelligence and Learning Lab (LIDIAP) and collaborates on projects like GraphNEx (explainable AI via graph neural networks).

Emilio Frazzoliمشاهده پروفایل

استاد

• Autonomous Robotics
• Control Systems
• Game Theory
• +۴ مورد دیگر

Emilio Frazzoli is a Full Professor at ETH Zurich’s Department of Mechanical and Process Engineering. He leads the Institute for Dynamic Systems and Control and the Center for Sustainable Future Mobility, focusing on autonomous systems, robotics, and socio-technical control frameworks. Current affiliations: ETH Zurich (Dynamic Systems and Control, Sustainable Future Mobility) Research: Autonomous mobility-on-demand, game theory for resource allocation, and safety verification in multi-agent systems His work bridges robotics, control theory, and economics, with projects like the open-source AMoDeus simulation framework for autonomous taxis and karma-based resource allocation systems. Recent publications emphasize trustworthy AI, reproducibility in autonomous vehicle control, and human-robot interaction challenges. Notable projects include nuReality (VR-based pedestrian interaction studies) and CARSI II (context-driven vehicle interfaces).

Tanja Käserمشاهده پروفایل

استادیار

• Machine Learning
• Educational Data Mining
• Learning Analytics
• +۳ مورد دیگر

Tanja Käser is a Tenure Track Assistant Professor at EPFL's School of Computer and Communication Sciences (IC), leading the Machine Learning for Education Laboratory (ML4ED). Her interdisciplinary research bridges machine learning, data mining, and educational technology, focusing on personalized learning systems and human behavior modeling. PhD in Computer Science (ETH Zurich, 2015) - honored with Fritz Kutter Award Former Senior Data Scientist at Swiss Data Science Center (ETH Zurich) Postdoctoral Researcher at Stanford University's Graduate School of Education Research Focus Explainable AI for education Adaptive learning environments Behavioral pattern recognition Generative AI applications in pedagogy User modeling and personalization Learning analytics in unstructured settings Recent Publication Trends Her 2024-2023 work demonstrates: Interpretable clustering of learners Transformer-based language learning prediction GAN applications for creative education Teacher-AI collaboration frameworks Explainability validation methods Modular network architectures Scientific Recognition Fritz Kutter Award for best Swiss computer science thesis (2015) Advising & Collaborations Currently supervises multiple PhD students including: Cock Jade Maï L Glandorf Dominik Güres Fatma-Betül Neshaei Seyed Parsa Radmehr Bahar Shibu Abhinand Shved Ekaterina Research Infrastructure Operates from EPFL's ML4ED laboratory with hybrid on-site and digital educational systems research capabilities.

Claudio J. Tessoneمشاهده پروفایل

استاد

• Blockchain Technology
• Cryptocurrency Systems
• Complex Socio-Economic Systems
• +۵ مورد دیگر

Prof. Claudio J. Tessone is a Professor of Blockchain and Distributed Ledger Technologies at the Department of Informatics, University of Zurich. He serves as Head of the Blockchain and Distributed Ledger Technologies group, Chairman of the UZH Blockchain Center, and is incharge of the NetSci Society. His academic background includes a PhD in Physics (Complex Systems) and an Habilitation in Complex Socio-Economic Systems from ETH Zurich. Education: PhD in Physics (2006): Thesis on synchronization in stochastic systems, Universitat de les Illes Balears, Spain Habilitation (2015): Thesis on agent-based modeling of socio-economic systems, ETH Zurich Master in Physics (1999): Thesis on stochastic resonance, Instituto Balseiro, Argentina Research Interests: Prof. Tessone specializes in modeling complex socio-economic and socio-technical systems, with a focus on blockchain-based systems. His work explores crypto-economics, blockchain scalability, decentralized finance (DeFi), and the interplay between micro-level agent behavior and macro-level emergent properties. Notable areas include transaction network analysis in Bitcoin/Ethereum, consensus mechanisms (Proof-of-Stake/Work), and blockchain governance models. Publications Trends: Recent articles emphasize empirical blockchain analysis (e.g., Ethereum microvelocity, Bitcoin mesoscopic structure), DeFi arbitrage strategies, and privacy-preserving blockchain applications in healthcare. His work bridges theoretical agent-based models with real-world blockchain datasets, addressing both technical and socio-economic dimensions of distributed ledger technologies. Grants & Labs: Director of the UZH Summer School on Blockchain and Certificate of Advanced Studies program. Active in interdisciplinary collaborations through the URPP Social Networks (2015–2021) and ETH Zurich’s Systems Design group (2007–2014). Labs/Initiatives: Leads the UZH Blockchain Center, a hub for academic-industry research on blockchain applications in finance, governance, and digital transformation.

Reinhard Heckelمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computational Imaging
• +۵ مورد دیگر

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.