Jean-Claude Besse is a Lecturer in the Department of Physics at ETH Zürich, specializing in superconducting circuits and quantum optics. His research focuses on quantum computing, microwave photonics, and artificial atoms. Research Interests: Besse works on the fabrication of superconducting circuits, modular quantum computing processors, and microwave quantum optics using artificial atoms. His work includes single-photon detection, parity measurements, entanglement stabilization, and quantum networking. He has developed technologies like high-fidelity multiplexed readout and tunable ZZ gates. Key Contributions: Besse led breakthroughs in non-destructive single-photon detection, deterministic remote entanglement, and loophole-free Bell inequality violations. His research enables error-corrected quantum communication protocols and scalable microwave quantum systems. Publications Trends: Recent articles emphasize modular quantum architectures, entanglement stabilization, and microwave photon engineering. Topics include cluster state generation, defect mode mitigation, and reinforcement learning for quantum feedback systems. Labs & Teams: Affiliated with the Laboratorium für Festkörperphysik at ETH Zürich, Besse contributes to advancing superconducting quantum technologies and microwave quantum optics.
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نمایش ۱-۱۵ از ۲۳ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Georg Fantnerمشاهده پروفایل
دانشیار
Georg Fantner is an Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) with dual appointments in the School of Engineering (STI) within the Institute of Bioengineering and the School of Life Sciences (SV) for teaching. He directs the Laboratory for Bio- and Nano-Instrumentation (LBNI) and holds leadership roles including President of the Open Science Strategic Committee and the Association des Professeurs de l'EPFL. Research Focus: Bioinstrumentation, Nanotechnology, Scanning Probe Microscopy, and Metrology Teaching: Structural Mechanics for Life Sciences, Metrology, and Metrology Practicals His research pioneers advanced instrumentation for nanoscale characterization, emphasizing data-driven approaches to enhance microscopy techniques. Recent work integrates deep learning with scanning probe microscopy for real-time biological imaging and develops novel MEMS devices for fluid-compatible nanoscale manipulation. Key innovations include hermetically sealed sample chambers for pathogen studies and deterministic nanotopography engineering. Professor Fantner actively mentors 7 current PhD students and has supervised 14 graduates. His laboratory fosters interdisciplinary collaboration across engineering, physics, and life sciences to advance nanoscale measurement technologies and instrumentation development.
Yuning Jiangمشاهده پروفایل
استاد مهمان
Yuning Jiang is a Visiting Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Automatic Control Laboratory (LA3) within the School of Engineering (STI). He teaches the doctoral course Optimal Control for Dynamic Systems and contributes to research in distributed optimization, model predictive control (MPC), and smart grid technologies. His work bridges theoretical advancements in control systems with practical applications in power networks and autonomous systems. Current research emphasizes scalable solutions for AC optimal power flow, real-time MPC for embedded systems, and robust optimization under uncertainty. His research interests span Optimal Control , Power Systems , Smart Grids , and Federated Learning . Notable contributions include distributed algorithms for large-scale power systems and privacy-preserving co-simulation frameworks. Recent publications focus on microservice deployment in satellite-terrestrial networks and real-time pricing mechanisms for vehicle-to-grid (V2G) integration. Yuning holds a position in the EDEE-ENS unit under EPFL’s Academic Affairs division (VPA-AVP-DLE), reflecting his role in academic administration and teaching infrastructure. His lab, the Automatic Control Laboratory, focuses on cutting-edge research in control theory and its interdisciplinary applications.
Laurent Condatمشاهده پروفایل
پژوهشگر
Laurent Condat is a Senior Research Scientist at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, where he conducts research in optimization algorithms and their applications. He is affiliated with the College of Engineering, Department of Computer Science, and has previously held research positions at CNRS in France, working at GREYC in Caen and GIPSA-Lab in Grenoble. Dr. Condat received his PhD in 2006 from Grenoble Institute of Technology, followed by a 2-year postdoc in Munich, Germany. He was recruited as a permanent researcher by CNRS in 2008 and has been on leave from CNRS since November 2019 to work at KAUST. In February 2025, he was promoted to 'chargé de recherche hors classe' (senior research scientist) by CNRS. His research focuses on deterministic and stochastic optimization algorithms, convex relaxations, and applications to machine learning, signal and image processing. His work spans theoretical foundations of optimization methods to practical implementations for distributed and federated learning systems. He has developed several influential algorithms including RandProx, TAMUNA, and LoCoDL that address communication efficiency in distributed optimization. His recent publications demonstrate strong trends in communication-efficient distributed optimization, with particular emphasis on federated learning, compression techniques, and local training methods. His work bridges theoretical optimization with practical machine learning applications, showing consistent innovation in algorithmic design for large-scale problems. Best reviewer award at AISTATS 2025 Meritorious Service Award from Mathematical Programming Stanford's list of world's top 2% most influential scientists Dr. Condat has co-supervised PhD students including Daniele Picone and Julien Baderot. He serves as an Associate Editor for IEEE Transactions on Signal Processing and has presented his work at numerous international conferences including plenary talks at major optimization workshops. His research is supported through KAUST funding and collaborative projects with researchers worldwide.
Debabrota Basuمشاهده پروفایل
استادیار
Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.
Jean-Yves Le Boudec is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Institute of Electrical Engineering. He has been a key figure in advancing the theory and application of network calculus and deterministic networking, contributing significantly to standards such as IEEE Time-Sensitive Networking (TSN) and IETF DetNet. His research focuses on network calculus , time-sensitive and deterministic networking , traffic regulation , worst-case delay analysis , and cyber-physical systems , with cross-cutting applications in smart grids , real-time communication , and network security . He has co-authored foundational texts on network calculus and developed theoretical frameworks for traffic regulators, service curves, and delay bounds in complex networked systems. The recent publications highlight a strong trend in analyzing and improving performance guarantees in deterministic networks, including scheduling mechanisms like Deficit Round-Robin and Cyclic Queuing and Forwarding, traffic shaping via interleaved regulators, and security against time-synchronization attacks in power systems. The work spans theoretical modeling using stochastic and min-plus/max-plus algebra, practical algorithm design, and application to critical infrastructure. IEEE Fellow Le Boudec has advised numerous researchers and PhD students, including Ehsan Mohammadpour, Ludovic Thomas, and Seyed Mohammadhossein Tabatabaee. His collaborative projects often involve grants related to European and Swiss research initiatives in networking and smart grid technologies. He leads a research group focused on networked systems at EPFL, contributing to both theoretical advances and real-world implementations in industrial and energy-critical networks. His lab work centers on modeling and verification of time-sensitive network behaviors, integrating formal methods with practical experimentation. The team investigates regulators, shapers, and synchronization mechanisms, aiming to ensure robustness, predictability, and security in next-generation communication infrastructures. Future work continues to explore the interplay between communication, control, and energy systems in highly reliable environments.
- Applied Analysis
- Numerical Analysis
- Data Science
- +۶ مورد دیگر
Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.
Christoph Grunau is a Researcher at ETH Zürich's Theoretical Computer Science department, affiliated with the Professorship for Computer Science. His work focuses on distributed computing, parallel algorithms, graph theory, and network decomposition. He has contributed to advancements in scalable MPC (Massively Parallel Computing) algorithms, efficient parallel derandomization techniques, and deterministic network decomposition methods. His research emphasizes algorithmic efficiency, theoretical guarantees, and applications in distributed systems, quantum computing, and dynamic graph problems. Key contributions include work on graph orientation, dynamic coloring algorithms, and clustering techniques such as k-center and k-means++. His publications span topics like shortest path algorithms with negative edge weights, probabilistic methods for algorithm analysis, and distributed symmetry breaking in sparse graphs. Grunau's research bridges foundational theory with practical distributed computing challenges, addressing scalability and efficiency in both classical and emerging computational frameworks.
Michel C Milinkovitch is a Full Professor in the Department of Genetics and Evolution at the University of Geneva, leading the Laboratory of Artificial & Natural Evolution (LANE) . His research merges biology, physics, and computational modeling to unravel the developmental and evolutionary mechanisms behind life's complexity, focusing on reptiles and mammals. Institution: University of Geneva Contact: Sc3 4024B | +41 22 379 33 38 Research Interests span evolutionary developmental biology, biomechanics of skin appendages, reaction-diffusion systems, mechanical instabilities in morphogenesis, and computational modeling. He investigates how physical constraints interact with genetic networks to generate patterns in scales, feathers, and pigmentation. Scientific Contributions include groundbreaking work on mechanical vs. chemical patterning in crocodile scales, snake scale organization via somitic cues, and the role of the sonic hedgehog pathway in avian feather development. His team employs advanced imaging, CRISPR-Cas9, and 3D simulations to bridge micro- and macro-scale biological phenomena. Collaborators & Alumni include Senior Lecturer Athanasia Tzika and researchers like Pierre-Yves Helleboid and Gabriel N. Santos-Durán. The lab maintains strong ties with interdisciplinary institutions and contributes to open-access protocols in reptilian genome assembly.
Patrick Eugster is a full professor at the Università della Svizzera italiana (USI) and leads the Software Systems (SWYSTEMS) group within the Computer Systems Institute . He has held academic positions at Purdue University, TU Darmstadt, and MIT as a visiting faculty. His research focuses on distributed software systems , addressing challenges in consistency, efficiency, and security in decentralized environments. Current affiliation: USI Faculty of Informatics Prior affiliations: Purdue University (2005-2016), TU Darmstadt (2014-2017), MIT (2012/2013) Research Interests Distributed Systems : Investigating reliable failure detection, deterministic data paths for 6G, and hybrid synchronous/asynchronous architectures. Network Innovation : Optimizing TCAM encoding, analyzing microbursts, and developing quantum network protocols. Security & Verification : Creating confidentiality-preserving mechanisms and formal verification approaches for distributed software. Scientific Contributions 2025: Best paper at TACAS on Horn clause validation 2025: IFIP Jean-Claude Laprie Award for dependable computing 2024: Key papers at IEEE Network, ACM PLDI, and IEEE INFOCOM Funding & Collaborations Swiss National Science Foundation grants #200021_197353 and #200021_192121 EU Horizon Europe MSCA staff exchange project CloudStars Industry partners: Cisco, Amazon AWS, Facebook Research, SAP, Northrop Grumman Cybersecurity Research Consortium Labs & Teams Directs the SWYSTEMS group, which includes postdocs like Sina Darabi and Shamiek Mangipudi, PhD students such as Anita Buckley and Jérôme Graf, and alumni who have become faculty at institutions like Télécom Paris and University of Southern California.
- Distributed Systems
- Networks
- Security
- +۵ مورد دیگر
Patrick Thomas Eugster is a Full Professor of Computer Science at the Università della Svizzera italiana (USI), leading the Software Systems (SWYSTEMS) group within the Computer Systems Institute, which he co-founded. Previously, he held faculty positions at Purdue University (2005-2016) and TU Darmstadt (2014-2017), with a visiting role at MIT (2012/2013). His research focuses on distributed systems, networking, security, and programming languages, with over 160 publications and significant industry collaborations with companies like Amazon, Google, and Facebook. Education: He holds an M.S. (1998) and Ph.D. (2001) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL). Research Interests: His work addresses distributed systems challenges such as fault-tolerance, security, and efficient resource management. Recent topics include datacenter reliability, quantum network verification, and confidential computing. His team explores intersections between systems, languages, and networks to build robust and secure distributed applications. Publications: Recent work spans topics like failure detection in datacenters, formal verification of systems, and network congestion control. Key venues include USENIX ATC, ACM SIGMETRICS, IEEE Network, and TACAS. His 2025 work on failure detection and TCAM encoding exemplifies contributions to system resilience and hardware optimization. Awards: Jean-Claude Laprie Award (2025), TACAS Best Paper (2025), ERC Consolidator Grant (2014), NSF CAREER Award (2007). Advising & Grants: Supervised over 20 PhD and postdoctoral researchers. Active grants include EU Horizon Europe CloudStars, Swiss National Science Foundation, and industry partnerships with Cisco and SAP. Labs & Teams: Directs the SWYSTEMS group, collaborating on projects like secure cloud analytics, quantum network verification, and datacenter monitoring. Former students hold roles at universities, tech firms, and startups.
Dr. Manuela Fischer is a Lecturer at the Department of Computer Science at ETH Zürich. Her research focuses on distributed computing, algorithms, and parallel processing, with a particular emphasis on graph theory and massively parallel computation. She has contributed to advancements in algorithms for sparse graphs, approximation techniques in semi-streaming models, and distributed systems optimization. Her work often explores theoretical computer science challenges, including algorithm design and computational complexity. Dr. Fischer’s publications span topics such as exponential speedups in MPC frameworks, deterministic symmetry breaking in graphs, and efficient connectivity algorithms in distributed environments. These contributions highlight her expertise in leveraging parallel and distributed computing paradigms to solve complex computational problems. No scientific awards or grants are explicitly mentioned in the provided information. She is affiliated with ETH Zürich and contributes to teaching and research in computer science, though specific educational qualifications or lab affiliations are not detailed in the text.
Filip Ilievskiمشاهده پروفایل
استادیار
Filip Ilievski serves as an Assistant Professor of Commonsense AI (Sr.) at Vrije Universiteit Amsterdam, where he leads the Learning and Reasoning Group. He holds additional affiliations as an Affiliated Scientist at USC Information Sciences Institute and Amsterdam Sustainability Institute, and serves as Scientific Coordinator of the Digital Sustainability Institute (DiSC). His academic journey includes a PhD from Vrije Universiteit Amsterdam (2015-2019), research positions at USC (2019-2023), and a research visit to Carnegie Mellon University (2017). Dr. Ilievski's research focuses on advancing human-centric AI with common sense for social good applications. His work spans three interconnected areas: commonsense reasoning (including situational awareness, numeracy, and modeling of other agents), analogy and abstraction (studying cognitive generalization mechanisms through narratives and lateral thinking puzzles), and AI for social good (interpreting complex online media like internet memes and developing knowledge-based solutions for sustainable policies). He employs neuro-symbolic methods including prototype-based networks, combining LLMs with deterministic engines, and reasoning with scene knowledge graphs to create robust, interpretable AI systems. His publication record shows a strong trend toward multimodal reasoning and practical applications of commonsense AI, particularly in understanding internet memes, developing sustainable AI solutions, and creating robust evaluation frameworks like the MARVEL benchmark. Recent work increasingly integrates cognitive theories with neural architectures to address limitations in current foundation models, with particular emphasis on explainability and cultural context awareness. Best Student Paper Award at K-CAP for 'Knowledge-enhanced Agents for Interactive Text Games' Best Paper and Best Presentation Award at Workshop on Multimodal Content Analysis for Social Good (MM4SG) Book 'Human-Centric AI with Common Sense' published in Springer Nature Synthesis series Dr. Ilievski actively supervises a growing team of PhD students and postdocs, with current projects focusing on visual commonsense reasoning, meme semantics, analogical abstraction, multimodal alignment, and architectures for human-centric AI. His Digital Sustainability Institute (DiSC) coordinates research on knowledge-driven AI for sustainable policies, while his Situated AI minor program develops next-generation AI education. He maintains active collaborations with institutions including USC, CMU, RPI, University of Lyon, UvA, University of Bielefeld, and industry partners like Bosch Research, NEC Labs, Merit Technologies, and Tencent.
Radu Vintanمشاهده پروفایل
پژوهشگر
Radu Vintan is a Doctoral Assistant and PhD student at École polytechnique fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, affiliated with the Institute of Computer Science and the Theory of Computation Laboratory 2 (THL2). He is part of the Doctoral Program in Computer and Communication Sciences (EDIC). Education: Bachelor's and Master's in Computer Science, Technical University of Munich (TUM), Germany PhD in Computer Science, École polytechnique fédérale de Lausanne (EPFL), Switzerland (ongoing) His primary research interests lie in theoretical computer science , particularly online algorithms and approximation algorithms . He also has experience in machine learning and software engineering . His work often involves algorithmic design and analysis for graph and network problems. The recent publications reflect a strong focus on online edge coloring and network update algorithms , with contributions to top-tier conferences such as FOCS, STOC, SODA, and INFOCOM. These works explore both theoretical limits and practical algorithmic solutions, often bridging deterministic and randomized approaches. Scientific Awards: No awards mentioned. Advising and Grants: Radu Vintan is advised by Professor Ola Svensson. There is no mention of him advising students or receiving independent grants. His research is conducted within the Theory group at EPFL, supported through his Doctoral Assistant position. Laboratories and Teams: He is an active member of the Theory of Computation Laboratory 2 (THL2) at EPFL, contributing to foundational algorithmic research in collaboration with leading experts in theoretical computer science.
Joanna Wengمشاهده پروفایل
مدرس ارشد
- Artificial Intelligence in Safety-Critical Systems
- Nuclear Power Plant Safety Analysis
- Functional Safety Standards
- +۳ مورد دیگر
Joanna Weng is a Senior Lecturer in Mathematics, Physics, and Sensor Technology at the ZHAW School of Engineering, affiliated with the Institute of Applied Mathematics and Physics (IAMP) and the Centre for Artificial Intelligence (CAI). She holds roles as Project Leader at the Safety-Critical Systems Research Lab and serves as an Associate Diversity Officer. Her expertise spans AI in safety-critical systems, functional safety (IEC 61508), nuclear power plant safety analysis, and particle physics. Education: CAS in Project Management, Functional Safety Engineer (TÜV Rheinland), and IEEE CertifAIEd Assessor. Research focuses on AI certification frameworks, deterministic/probabilistic safety analysis, and autonomous system safety. She leads projects like the Personnel Safety System (PSS) at the European Spallation Source (ESS) and co-leads the certAInty certification scheme for AI systems. Notable contributions include work on trustworthy AI assessment and MLOps-enabled safety. Key Projects: Personnel Safety System (PSS) at ESS IEEE CertifAIEd Assessor Training Machine Protection and Autonomous Predictive Interlock Systems certAInty AI Certification Scheme Awards/Certifications: CAS in Safe and Secure AI, TÜV Functional Safety Engineer, IEEE CertifAIEd Assessor. Grants/Advising: Innovationsuisse-funded certAInty project leader, overseeing interdisciplinary teams for AI safety research and implementation. Labs/Teams: Safety-Critical Systems Research Lab (IAMP), Centre for Artificial Intelligence (CAI), and collaborations with CERN Alumni and Women in Tech networks.