جست‌وجوی استاد و پژوهشگر

• Systems Neuroscience
• Animal Behavior
• Computer Vision
• +۴ مورد دیگر

Mackenzie Mathis is a Tenure Track Assistant Professor and Bertarelli Foundation Chair of Integrative Neuroscience at the Brain Mind Institute (BMI) of EPFL. She leads the UPMWMATHIS Lab, focusing on understanding neural circuits underlying adaptive behavior and developing AI tools like DeepLabCut and CEBRA. Her work bridges machine learning and neuroscience, with expertise in systems neuroscience, animal behavior, and computer vision. Education: PhD in Neuroscience, Harvard University (2017) Research Interests: Her lab explores motor learning, sensorimotor control, and neural dynamics. Key tools include markerless pose estimation (DeepLabCut), latent embedding analysis (CEBRA), and generative AI for behavioral studies. Projects span from mouse behavior assays to ethical AI applications in conservation. Publications: Recent work includes advancements in robust ML systems, pre-trained pose models (SuperAnimal), and neural-latent dynamics analysis. Her lab’s tools are widely adopted in neuroscience and robotics. Awards: Swiss Science Latsis Prize (2024) Eric Kandel Young Neuroscientist Prize (2023) FENS EJN Young Investigator Prize (2022) Vallee Scholar & ELLIS Scholar Advising & Grants: Current PhD students include Célia Benquet, Hossein Mirzaei, and others. Grants include SNSF Starting Grant (1.5M CHF) and CZI funding for open-source tools. Collaborates with robotics and conservation initiatives. Labs & Teams: The UPMWMATHIS Lab at EPFL Biotech Campus integrates computational and experimental neuroscience. Tools like AmadeusGPT and CEBRA are developed here, supported by interdisciplinary teams.

Ricardo Chavarriagaمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Neurotechnology
• Brain-Machine Interfacing
• +۴ مورد دیگر

Ricardo Chavarriaga is a Research Professor and Group Leader of the Responsible AI Innovation Group at the ZHAW School of Engineering in Winterthur, Switzerland. He also serves as Head of the CLAIRE Office Zürich and chairs the IEEE Standards Association group on Neurotechnologies for Brain-Machine Interface. His roles include leadership in AI ethics, neurotechnology governance, and translational research. Education: Ph.D. in Computational Neuroscience from EPFL (2005), B.Sc. in Electronics Engineering from Pontificia Universidad Javeriana (1998). Additional certifications include a CAS in Biotech Venture Management (2019) and PMP certification (2019). Research focuses on neurotechnologies, brain-machine interfaces, and AI ethics. He leads projects like certAInty (AI certification), AI-BRIDGE (responsible AI think-tank), and brain data governance initiatives. His work bridges technical innovation with societal implications, emphasizing standards and global policy frameworks. Key contributions include over 40 peer-reviewed articles, IEEE standards development, and advisory roles at international organizations like the Geneva Centre for Security Policy. Awards include the 2021 DIZH Fellowship for digital innovation. Active in academia and industry collaborations, he teaches reinforcement learning and machine intelligence at both BSc and MSc levels. Engages globally through conferences, policy workshops, and public lectures on AI governance and neurotechnology risks.

Wanyun Xieمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Optimization
• +۵ مورد دیگر

Wanyun Xie is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the Institute of Electrical Engineering. They work within the Laboratory for Information and Inference Systems (LIONS), focusing on research areas such as Machine Learning , Deep Learning , Optimization , and Robust Learning . Their email address is wanyun.xie@epfl.ch . Recent research trends in their publications include advancements in gradient norm clipping , Sharpness-aware Minimization (SAM) , data-mixing frameworks , and game-theoretic approaches for Byzantine-robust federated learning . These works emphasize non-Euclidean smoothness , parallelized optimization , and stable nonconvex-nonconcave training .

• Machine Learning
• Computer Vision
• Statistical Mechanics
• +۳ مورد دیگر

Anastasia Remizova is a Doctoral Assistant at the Lab for Statistical Mechanics of Inference in Large Systems (SMILS) within the School of Computer and Communication Sciences (IC) at EPFL. She is concurrently enrolled in the Doctoral Program in Computer and Communication Sciences . Additionally, she serves as Treasurer of the Association des Étudiants Russophones . Her research interests revolve around Machine Learning , Computer Vision , and Statistical Mechanics , focusing on topics like stochastic gradient dynamics, image inpainting, and adversarial learning. Recent publications highlight contributions to test risk analysis in weak feature scenarios and resolution-robust image restoration techniques. While no scientific awards are explicitly mentioned, her work demonstrates engagement with cutting-edge interdisciplinary research at the intersection of inference algorithms and large-scale systems. She is affiliated with the SMILS lab, where her doctoral research likely intersects theoretical and applied aspects of statistical mechanics in modern AI systems.

Eklavya Sarkarمشاهده پروفایل

پژوهشگر

• Deep Learning
• Self-Supervised Learning
• Representation Learning
• +۷ مورد دیگر

Eklavya Sarkar is a PhD candidate in Machine Learning at EPFL and Research Assistant at Idiap Research Institute, working in the Speech and Audio Processing group under Dr. Mathew Magimai Doss. His research focuses on speech and audio methods for analyzing human and non-human vocal communication, particularly in the context of the Swiss NCCR Evolving Languages project. He holds a MSc in Data Science from the University of Bath (First Class) and a BSc in Computer Science from the University of Liverpool (First Class). Education: PhD in Machine Learning (EPFL, 2021–present), MSc Data Science (University of Bath, 2018–19), BSc Computer Science (University of Liverpool, 2015–18). Key research areas include self-supervised learning, bioacoustics, generative adversarial networks, and biometric security. Previously interned at Idiap (biometrics), CERN (CMS Experiment), and conducted exoplanet research with Nobel laureate Didier Queloz. Research interests span cross-domain transfer learning for bioacoustics, speaker identification in animal vocalizations, and vulnerability analysis of face recognition systems. His work bridges machine learning theory with applications in wildlife communication analysis and cybersecurity. Recent publications emphasize self-supervised learning models for marmoset vocalization analysis and face morphing attack detection using GANs. Awards include 3rd Prize in the International Create Challenge 2020 for adversarial attack detection. Organized the Perspectives on AI Symposium series and led student housing committees. Technical expertise spans Python (PyTorch, Hydr), signal processing, and multiple machine learning frameworks. Engaged in interdisciplinary projects at the intersection of physics, computer science, and linguistics.

• Artificial Intelligence
• Natural Language Processing
• Blockchain Technology
• +۲ مورد دیگر

Dr. Luca Mazzola is a Lecturer at Lucerne School of Computer Science and Information Technology (HSLU-I) and senior research associate in the Information Systems Research Lab. With a PhD in Technologies for Human Communication (USI, 2014) and MSc in Computer Engineering (Politecnico di Milano, 2004), his research spans artificial intelligence, natural language processing, blockchain applications, and machine learning in distributed systems. Mazzola leads multiple research projects including PrivacyGLUE (privacy policy analysis), blockchain-based healthcare management systems, and personalized event matching frameworks. His recent publications focus on NLP applications for privacy compliance, federated learning architectures, and explainable AI for concept recognition. Mazzola teaches courses in Data Science Fundamentals and Natural Language Processing, emphasizing practical applications of ML and AI technologies. Research collaborations include bilateral projects with South Africa's SARCHi NRF and EU Blockchain Observatory. His technical innovations include developing P2P data marketplaces, blockchain authentication systems, and educational recommendation engines using knowledge graphs.

Lena A. Jägerمشاهده پروفایل

دانشیار

• Computational Linguistics
• Eye Tracking
• Machine Learning
• +۵ مورد دیگر

Lena A. Jäger is an Associate Professor in Digital Linguistics at the School for Transdisciplinary Studies, University of Potsdam. Her research focuses on computational linguistics, eye tracking, and machine learning applications in language processing. She leads the Digital Linguistics group, supervising 11 PhD students and contributing to interdisciplinary projects like the MultiplEYE corpus initiative. Her work bridges cognitive science and AI, with key contributions to eye movement analysis, biometric identification, and LLM interpretability. Her research interests span eye tracking in reading, linguistic predictability modeling, and the alignment of language models with human cognition. Notable achievements include developing the pymovements toolkit for eye movement data processing and pioneering studies on detecting alcohol inebriation via gaze analysis. Awards include best paper honors at ETRA 2023 and 2024. Recent work explores generative models (ScanDL) for synthetic eye movement generation, cross-lingual reading behavior analysis, and fairness in biometric systems. She advises on AI ethics through the DSI initiative and collaborates internationally on projects like the MECO corpus.

Eric van Genderenمشاهده پروفایل

پژوهشگر

• Electron Diffraction
• Protein Structure
• MicroED
• +۲ مورد دیگر

Eric van Genderen is a Scientist at the Paul Scherrer Institute, specializing in electron diffraction and structural biology. His work focuses on developing methods for protein structure determination. Research Interests: His expertise includes 3D electron diffraction (3D-ED/MicroED), machine learning applications for diffraction data, and protein nanocrystal analysis. Current projects investigate hydrogen storage nanomaterials and computational refinement techniques. Publications: Recent work demonstrates advances in AI-driven diffraction phasing, detector technology, and peptide engineering. Publications emphasize interdisciplinary approaches to structural analysis. Laboratories: Leads hardware development for electron diffractometers at the Laboratory for Multiscale Bioimaging. Facilities include specialized TEMs with cryo-stages and hybrid detectors. Advising: Supervises Master's students in projects related to diffraction classification algorithms and nanocomposite characterization.

Nicolas Flammarionمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Nicolas Flammarion is a tenure-track Assistant Professor in Computer Science at the École Polytechnique Fédérale de Lausanne (EPFL). He holds positions in multiple departments including the Theory in Machine Learning (TML) lab under the School of Computer and Communication Sciences (IC). His roles include teaching and doctoral program leadership across disciplines like Communication Systems and Computer Science. Education: PhD in 2017 from École Normale Supérieure (Paris), advised by Alexandre d’Aspremont and Francis Bach. Postdoctoral fellowship at UC Berkeley under Michael I. Jordan. Research focuses on machine learning theory, optimization, and statistical methods. Key areas include adversarial robustness, algorithmic generalization, and optimization dynamics. Notable contributions include work on SGD/GD comparisons, adversarial benchmarking (RobustBench), and safety in AI systems. Awards include the 2018 Fondation Mathématique Jacques Hadamard PhD Prize, 2021 NeurIPS Outstanding Paper Award, and 2024 Trust & Safety Google Research Award. Supervises PhD students in topics like algorithm design and machine learning theory. Labs/Teams: Leads TML lab and contributes to EPFL's doctoral programs in Informatics and Communication Sciences. Active in curriculum development for advanced machine learning topics.

Jeffrey Huangمشاهده پروفایل

استاد

• Design Thinking
• Artificial Intelligence in Architecture
• Strategic and Experience Design
• +۳ مورد دیگر

Jeffrey Huang is a Full Professor at EPFL, holding joint appointments in the Faculty of Computer and Communication Sciences (IC) and the Faculty of Architecture, Civil and Environmental Engineering (ENAC). He directs the Institute of Architecture and leads the Media x Design Laboratory (MxD Lab). His areas of expertise include Design Thinking, Artificial Intelligence in Architecture, Urban Digital Twins, and Computational Urbanism. Educated at ETH Zurich (DiplArch), Harvard University (Masters and PhD), Huang has held academic roles at MIT, Harvard, Tsinghua University, and the Singapore University of Technology and Design (SUTD). He is a recipient of the Gerald McCue Medal for academic excellence and the 2023 ACSA Best Article Award for his work on GANs and architectural design. Recent research focuses on combining machine learning with architectural design, particularly exploring generative adversarial networks (GANs), spatial analysis, and urban digital twins. His projects include the Blue City Innosuisse Flagship Project and the Singapore Prototypologies initiative. Awards include the 2023 ACSA Best Article Award and the Best Presentation Award at CAAD Futures 2009. He supervises numerous PhD students and has led significant research grants such as the Innosuisse Flagship Project and the MIT-SUTD collaborations. Huang co-founded Convergeo, a strategic design firm, and has been involved in creating new architectural education programs, including the Architecture and Sustainable Design Pillar at SUTD.

• Product Management
• Supply Chain Management
• Artificial Intelligence
• +۳ مورد دیگر

Amin Kaboli is a Lecturer at the Swiss Federal Institute of Technology in Lausanne (EPFL), holding positions in the School of Mechanical and Process Engineering and the School of Computer and Communication Sciences . As Co-Director of the AI Product Management program , he specializes in teaching students to apply AI/ML principles to real-world product development. His academic roles include designing curricula that blend technical engineering with business leadership, such as courses on Sustainable Products and Production Management. He earned a Ph.D. in Manufacturing Systems & Robotics from EPFL and holds a graduate degree in Industrial Engineering. His leadership training at IMD Business School complements his engineering background, enabling him to advise Fortune 500 corporations like Philip Morris International, Nespresso, and Rolex, as well as Swiss startups. His professional experience includes roles as an executive and COO of a startup, alongside work as an executive coach (PCC-certified). Kaboli’s research interests span AI-driven innovation , supply chain optimization , and sustainability . He explores solutions for energy management in smart grids, waste reduction in construction, and fault detection in industrial machinery. His work integrates fuzzy logic, multi-criteria decision-making (MCDM), and hybrid machine learning frameworks to address complex challenges. He teaches courses such as AI Product Management (focus on practical AI/ML applications), Sustainable Products and Supply Chains (sustainability principles and logistics optimization), Production Management (cost-effective manufacturing strategies), and Continuous Improvement of Manufacturing Systems (process optimization and team leadership). No scientific awards or grants are explicitly listed. His contributions include developing frameworks for eco-city solutions and waste management strategies. Kaboli’s office is located at ME A2 408 , EPFL’s Lausanne campus, where he also engages in administrative and educational leadership roles.

• Quantum Computing
• Machine Learning
• Probabilistic Programming
• +۳ مورد دیگر

Timon Gehr is part of the Professorship for Computer Science at ETH Zurich's Department of Computer Science, affiliated with the Institute of Programming Languages and Systems. His research focuses on quantum computing, probabilistic programming, neural network robustness, and privacy enforcement. He has contributed to the development of quantum languages like Silq and frameworks for certifying adversarial robustness in machine learning systems. His research interests include formal methods for programming languages, scalable symbolic reasoning, and applying probabilistic techniques to security and privacy. Recent work emphasizes robustness certification of neural networks and symbolic integration in machine learning. Key projects involve exact inference for probabilistic programs and differential privacy violation detection. Notable publications include work on quantum uncomputation, adversarial examples, and integrating logic into neural networks. No specific advising or grant details are provided in the text, but his involvement with the Institute of Programming Languages and Systems indicates active participation in academic collaborations and research teams.

Florian Tramèrمشاهده پروفایل

استادیار

• Computer Security
• Machine Learning
• Privacy-Preserving Technologies
• +۲ مورد دیگر

Florian Tramèr is an Assistant Professor of Computer Science at ETH Zurich, leading the SPY Lab. His research focuses on computer security, privacy, and adversarial machine learning, particularly studying vulnerabilities in deep learning systems. He holds a PhD from Stanford University and previously worked at Google Brain. Education: Bachelor's and Master's from EPFL (Switzerland), PhD from Stanford University. Research Interests: Adversarial machine learning, privacy-preserving technologies, cryptography, and evaluating AI safety through frameworks like AgentDojo. His work addresses societal impacts of rapidly deployed machine learning systems. Awards: Two Best Paper Awards at ICML 2024, CAIS SafeBench competition win, Google/Amazon Research Awards. His research has been featured in Nature , Science , and The Economist . Labs/Teams: SPY Lab (ETH Zurich), part of the Information Security Institute and ZISC. Associated with ETHZ AI Center. Advises PhD students on AI safety and security topics.

• Machine Learning
• Control Systems
• Computer Vision
• +۳ مورد دیگر

Hansjörg Gisler is affiliated with the Department of Integrated Systems at ETH Zürich, contributing to the Professorship for Digital Integrated Circuits and Systems. His research focuses on interdisciplinary applications of machine learning, control systems, and medical informatics. Key areas include 3D object detection using advanced neural networks, optimization algorithms for convex-concave problems, and IoT-driven medical diagnostic systems. He has collaborated extensively with researchers on projects involving deep learning frameworks for adverse condition detection, semantic segmentation, and adaptive control systems. Publications span topics like parameter-separable optimization methods, proximal Lagrangian techniques, and weakly-supervised learning for 3D point cloud processing. His work bridges theoretical advancements in optimization with practical applications in healthcare monitoring and autonomous systems. Gisler's contributions to real-time sleep apnea diagnosis and muscle fatigue detection systems highlight his commitment to biomedical engineering innovations. Collaborations with institutions like ETH Zürich's Institute for Integrated Systems underscore his role in fostering cross-disciplinary research.

• Cybersecurity
• Blockchain Technology
• Privacy-Preserving Systems
• +۵ مورد دیگر

Dr. Kari Kostiainen is a Lecturer at the Department of Computer Science, ETH Zürich. His affiliation includes the Institute for Information Security (Institut f. Informationssicherheit) and he is associated with the Security of Wireless Networks course in the Autumn Semester 2025. His research focuses on cybersecurity, blockchain technology, privacy-preserving systems, and trusted execution environments. Kari's work addresses challenges in phishing detection, cryptocurrency regulation, and secure communication protocols. Key research interests include: Phishing prevention and organizational cybersecurity Blockchain scalability and privacy in digital currencies Trusted execution environments (TEEs) and enclave security Privacy-preserving technologies for decentralized systems Secure access control mechanisms and user-friendly security setups His recent publications emphasize advancements in CBDC design, censorship-resistant payment systems, and lightweight blockchain client privacy. Notable contributions include Platypus (privacy-preserving CBDC framework) and Tee-based mining pool optimizations. Dr. Kari Kostiainen can be contacted at kari.kostiainen@inf.ethz.ch . His office is located at CNB F 103.2, ETH Zürich.