Find Academic Supervisors

• Decentralized Systems
• Distributed Systems
• Privacy and Anonymous Communication
• +7 more

Prof. Bryan Alexander Ford is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Decentralized and Distributed Systems (DEDIS) lab within the School of Computer and Communication Sciences, Department of Computer Science. He also holds teaching positions in SIN (Systems and Networking) and SSC (Security and Software Composition) at EPFL, and serves on the Open Science Strategic Committee. His academic journey includes faculty positions at Yale University following his Ph.D. at MIT. Dr. Ford's research spans multiple domains with a primary focus on building secure decentralized systems. His work encompasses privacy and anonymous communication systems like Dissent, systems security, blockchain technology, and novel approaches to operating systems for deterministic parallel computing such as Determinator. He has made seminal contributions to parsing theory through his development of Parsing Expression Grammars (PEGs) and packrat parsing algorithms, which provide linear-time parsing with backtracking capabilities. His research also extends to networking protocols including the Unmanaged Internet Architecture and Structured Stream Transport, as well as virtualization technologies like VX32. Ford's publication record demonstrates a remarkable evolution from foundational work in parsing and programming languages to cutting-edge research in decentralized systems and security. His early career focused on operating systems theory, parsing algorithms, and language design, culminating in influential papers on packrat parsing and PEGs. More recently, his work has shifted toward practical decentralized systems, blockchain technology, and security architectures, while maintaining connections to programming language theory through projects like Matchertext and MinML. This trajectory reflects both continuity in his interest in system architecture and a strategic pivot toward emerging challenges in decentralized computing. As an educator and mentor, Ford advises multiple PhD students at EPFL through the EDIC program and welcomes prospective students and researchers to join his lab. His teaching includes courses on decentralized systems engineering and technologies for democratic society, reflecting his commitment to both technical rigor and societal impact of computing technologies.

Stefan Michael MoserView profile

Researcher

• Information Theory
• Communication Systems
• LaTeX
• +3 more

Stefan M. Moser is a Senior Scientist at the Institute for Signal and Information Processing (ISI) at ETH Zurich and an Adjunct Professor at the Institute of Communications Engineering of National Yang Ming Chiao Tung University (NYCU) . He also serves as a Math Teacher at Kantonsschule Uster (KUS) . Teaching: Courses include Information Theory I/II , Communication Systems , and LaTeX tutorials. Research: Focuses on information theory, communication systems, and mathematical aspects of noncentral chi-square distributions. Software Development: Created financial tools like buha and grade management system grad , along with utilities for LaTeX, PDF processing, and secure communication via GnuPG. Publications: Authored lecture notes Information Theory (2025) and Advanced Topics in Information Theory (2025), plus co-authored textbook A Student's Guide to Coding and Information Theory (2012).

Rishabh IyerView profile

Assistant Professor

• Computer Systems
• Performance Analysis and Optimization
• Operating Systems
• +5 more

Rishabh Iyer is an Assistant Professor in the Electrical Engineering and Computer Sciences department at the University of California, Berkeley. His research bridges computer systems, networking, and formal methods with a focus on performance predictability and reliability. Prior to Berkeley, he completed his PhD at EPFL and undergraduate studies at IIT Bombay. His educational background includes: PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL) Bachelor's degree from Indian Institute of Technology Bombay (IIT Bombay) Iyer's research centers on performance interfaces for computer systems – creating succinct abstractions that allow engineers to reason about system performance without understanding implementation details. His work spans three major directions: (1) Extracting performance interfaces from implementations, (2) Designing systems with predictable performance, and (3) Formally verifying performance claims. This research combines operating systems, networking, computer architecture, and formal verification techniques to address performance unpredictability in modern systems. His group develops tools like PIX for network functions, KFlex for kernel extensions, and Software LPN for hardware accelerators. His publications reveal strong focus on performance modeling for network functions, kernel extensions, and hardware accelerators, with recent work expanding into real-time systems and SD-WAN validation. The research consistently targets practical impact, with deployments at companies like Meta and Alibaba. Scientific recognition includes: ACM SIGOPS Dennis M. Ritchie Award Eurosys Roger Needham PhD Award Dimitris N. Chorafas Award Best Paper at VDAT 2019 Iyer actively advises students at UC Berkeley and teaches courses including CS 294-262 (Performance Analysis) and CS 168 (Internet Architecture). Previously at EPFL, he taught Principles of Computer Systems, Software Engineering, and Vector Calculus. His group seeks talented students interested in building reliable, high-performance systems. Current research directions include performance interfaces for distributed applications, next-generation kernel extensions, and formally verified network systems. He leads research within UC Berkeley's systems community and maintains strong connections with EPFL's Dependable Systems Lab where he completed his PhD.

• Computational Game Theory
• Multi-Agent Systems
• Artificial Intelligence
• +7 more

Jiarui Gan is a Lecturer in the Department of Computer Science at the University of Oxford, where they conduct research at the intersection of computational game theory, multi-agent systems, and artificial intelligence. Their work focuses on understanding and shaping interactions among intelligent agents in complex real-world scenarios, with applications spanning transportation systems, digital platforms, and societal ecosystems. Dr. Gan's academic journey includes: PhD in Computer Science from the University of Oxford, supervised by Edith Elkind and Michael Wooldridge Postdoctoral research at the Max Planck Institute for Software Systems (MPI-SWS) with Rupak Majumdar Dr. Gan's research program centers on computational approaches to game theory and multi-agent systems. They investigate how to design incentive mechanisms that effectively coordinate autonomous agents toward organizational objectives, while addressing critical issues of fairness, security, and sustainability. Their work spans several interconnected themes: Principal-agency problems and dynamic mechanism design Stackelberg games and robust equilibrium concepts Fair resource allocation and envy-freeness in multi-agent settings Bayesian persuasion and information design Applications to security, transportation, and societal challenges Analysis of Dr. Gan's publication record reveals a consistent pattern of bridging theoretical insights with practical applications. Their work demonstrates sophisticated mathematical modeling combined with algorithmic innovations, resulting in computationally tractable solutions for complex multi-agent problems. The research shows particular strength in developing frameworks that unify previously disparate problem domains, such as their generalized principal-agency model that encompasses contract design, information design, and Bayesian Stackelberg games. Dr. Gan is actively involved in the academic community, mentoring students and collaborating with researchers across institutions. They are currently seeking motivated PhD students interested in computational game theory and multi-agent systems, with opportunities to work on both theoretical foundations and practical applications that address societal challenges.

Alexander HeinleinView profile

Assistant Professor

• Numerical methods for partial differential equations
• Scientific computing
• Domain decomposition methods
• +6 more

Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.

Víctor ElviraView profile

Senior Lecturer

• Bayesian Inference
• Sequential Monte Carlo Methods
• Importance Sampling
• +5 more

Víctor Elvira is a Senior Lecturer at the University of Edinburgh, specializing in Bayesian inference, sequential Monte Carlo methods, and computational statistics. He serves as an Associate Editor for IEEE Transactions on Signal Processing and organizes major academic events like the Sequential Monte Carlo Workshop (SMC 2024) and Summer School in Bayesian Filtering (SSBF 2024) . Key research areas: Bayesian filtering, importance sampling, state-space modeling, and machine learning applications Recent work focuses on sparse graphical models, differentiable particle filters, and Monte Carlo optimization Scientific Awards : 2025 EURASIP Early Career Award 2024 ELLIS Fellow 2023 Leverhulme Research Fellow 2021 Alan Turing Fellow 2018 Fulbright & Marie Curie Fellow 2018 IEEE Senior Member He has delivered PhD courses on Monte Carlo methods and Bayesian filtering in Madrid (2025) and Barcelona (2025), and maintains open-access software implementations for publications like Sparse Graphical Linear Dynamical Systems and Graphical Inference in Linear-Gaussian State-Space Models .

Cyril CayronView profile

Lecturer

• Metallurgy
• Crystallography
• Phase Transformations
• +3 more

Cyril Cayron is a Scientific Collaborator at the Laboratory of Thermomechanical Metallurgy (LMTM) at École Polytechnique Fédérale de Lausanne (EPFL) in Neuchâtel, Switzerland, where he has worked since 2014. He also serves as a Lecturer in the School of Engineering at EPFL, teaching courses on phase transformations. His work focuses on the intersection of metallurgy, crystallography, and phase transformations, with applications across multiple industries including nuclear energy, aeronautics, and microelectronics. PhD in electron microscopy study of aluminum matrix composites (1996-2000), EPFL, Lausanne Habilitation à Diriger des Recherches (HDR) (2012) École Nationale Supérieure des Mines de Nancy, France (1992-1995) Cyril Cayron's research spans both fundamental and applied aspects of materials science. His primary focus is on phase transformations in metallic materials, where he has developed the groupoid theory of crystallographic variants. His work on martensitic transformations in steels has led to a new hard-sphere crystallographic model that serves as an alternative to traditional phenomenological theories. He has also developed the ARPGE software for reconstructing parent grains from EBSD data, which is distributed in more than 20 countries. His recent publications reveal a strong focus on advanced manufacturing techniques, particularly additive manufacturing (3D printing), where he investigates microstructure evolution, phase transformations, and mechanical properties in materials like stainless steel, titanium alloys, and high-entropy alloys. His work bridges fundamental crystallography with practical applications in aerospace, energy, and medical devices (particularly shape memory alloys). 2018: Haenny Prize - For the connection between crystallography and phase transformations Cyril Cayron co-supervises multiple PhD students including Annick Baur, Margaux Larcher, and Céline Guidoux. He serves as a reviewer for prestigious journals including Acta Materialia, Scripta Materialia, and Journal of Applied Crystallography. His research has been supported by various institutions including CEA/LITEN in Grenoble and EPFL. At EPFL's Laboratory of Thermomechanical Metallurgy, Cayron manages the metallography laboratory and is responsible for SEM, EDS, EBSD, TEM, and HRTEM characterizations. His team investigates the relationships between strong deformations, textures, grain sizes, and phase transformations in various metallic systems.

Beate SickView profile

Professor

• Machine Learning
• Medical Imaging
• Deep Learning
• +3 more

Beate Sick is a Professor at the Zurich University of Applied Sciences School of Engineering, specializing in Data Analysis and Statistics. Her work bridges machine learning, probabilistic modeling, and medical applications, particularly in stroke outcome prediction, uncertainty quantification, and statistical methods for healthcare.

• Species Distribution Modeling
• Hydrological Modeling
• Ecosystem Services Assessment
• +6 more

Prof. Anthony Lehmann is a leading researcher at the University of Geneva , affiliated with the EnviroSPACE group and the Department of Environmental and Water Sciences . His career spans over three decades, pioneering innovations like the GRASP software for Species Distribution Modeling (SDM) and coordinating large-scale projects such as enviroGRIDS , ACQWA , and ValPar.CH . He serves as Associate Editor for Environmental Sciences and Policy and co-led a MOOC on Ecosystem Services.

Georg von ArxView profile

Researcher

• Dendroecology
• Dendroclimatology
• Plant Physiology
• +3 more

Georg von Arx is a Group Leader at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) in Birmensdorf, Switzerland. His research focuses on tree growth dynamics, xylem anatomy, and their responses to environmental changes. He leads the Dendrosciences Research Group, investigating anatomical properties of woody tissues and their links to tree physiology, particularly in relation to water and carbon relations. He is also affiliated with ETH Zurich, where he teaches courses on dendroecology and wood structure. Key research interests include dendroecology (tree growth analysis over centuries), dendroclimatology (reconstructing past climates), and plant community responses to climate variability. He develops tools like ROXAS for quantifying xylem cell features, enabling high-throughput anatomical data collection. His work bridges ecology, climatology, and applied forest science, addressing topics such as climate sensitivity of European beech, impacts of extreme droughts, and isotopic tracing of water sources in trees. Notable projects include the ROXAS-X software upgrade, continental-scale climate studies on beech provenances, and assessments of tree-water relations through laser-ablation isotope analysis. He collaborates widely, with contributions to initiatives like GenTree (forest genetic resources) and Caldera (volcanic climate impacts). Education includes a Habilitation (2021) from the University of Bern and a PhD (2005) from ETH Zurich. He has held postdoctoral and research roles since 2006, including at the University of Arizona (2007–2008). Teaching roles at ETH Zurich include courses on wood anatomy and climate sciences.

Mirela Beloiu SchwenkeView profile

Lecturer

• Forest Ecology
• Remote Sensing
• Climate Change
• +4 more

Dr. Mirela Beloiu Schwenke is a Lecturer at ETH Zurich's Department of Environmental Systems Science, specializing in forest ecology, remote sensing, and climate change impacts. Her research focuses on understanding forest resilience to climate-driven disturbances like droughts and developing advanced monitoring techniques using remote sensing and machine learning. She contributes to projects such as the SNSF COST Action for tree species identification and the UPSCALE initiative for forest vitality monitoring. Education & Research Interests PhD in Environmental Sciences (implied from professional context) Focus areas: Tree mortality dynamics, drought tolerance mechanisms, and integrating ground-based and remote sensing data Key Projects Leading development of deep learning models for tree species identification in mixed forests Contributing to global forest monitoring frameworks through Sentinel satellite integration Investigating drought impacts on sapling recovery and forest succession patterns Awards & Recognition Recipient of the 2023 Young Scientist Award for her presentation on tree mortality mechanisms at the EARSel Symposium. Teaching Teaches courses on forest ecosystems and practical field methodologies for environmental science students. Grants & Collaborations Swiss National Science Foundation (SNSF) funded projects International collaborations with institutions like the Africa CDC and ICOS Switzerland Labs & Teams Part of ETH Zurich's forest ecology and remote sensing research groups, actively contributing to open-access databases like 'deadtrees.earth'.

Martin ScheringerView profile

Professor

• Environmental Chemistry
• Chemical Risk Assessment
• Persistent Organic Pollutants
• +2 more

Prof. Dr. Martin Scheringer is Professor at ETH Zurich's Department of Environmental Systems Science and holds a position at Leuphana Universität Lüneburg. His research focuses on environmental chemistry and chemical risk assessment, with particular emphasis on persistent organic pollutants, chemical alternatives assessment, and pollution management strategies. Recent work examines global chemical pollution challenges including the interplay between chemical contamination and climate change, human exposure pathways through food systems, and developing frameworks for safer chemical alternatives. His publications demonstrate sustained focus on improving chemical regulation, exposure assessment methodologies, and computational modeling approaches for environmental toxicology. Scholarly contributions include developing the Essential-Use Concept for chemical regulation, analyzing global threats from accumulating pollutants like trifluoroacetic acid, and creating computational tools for reproducible toxicokinetic modeling.

Laura GrigoriView profile

Professor

• Numerical Linear Algebra
• High Performance Computing
• Deterministic and Randomized Algorithms
• +4 more

Laura Grigori is a Full Professor and Chair of High Performance Numerical Algorithms and Simulations at EPFL's School of Basic Sciences (SB) Department of Mathematics (MATH). Her research focuses on numerical linear algebra, high performance computing, and tensor computations, with applications in astrophysics and molecular simulations. She leads the HPNalgs lab and teaches courses in numerical analysis and HPC. Her awards include the SIAM Supercomputing Career Prize (2024) and SIAM Fellow distinction (2020). She advises four PhD students and has authored numerous papers on communication-avoiding algorithms, randomized methods, and parallel linear algebra techniques. Her work addresses scalability challenges in scientific computing and large-scale data analysis. Labs/Teams: HPNalgs Lab (https://www.epfl.ch/labs/hpnalgs/) Grants: ERC Synergy Grant (2019) for Extreme-scale Computational Chemistry

Michalis KokologiannakisView profile

Assistant Professor

• Programming Languages
• Software Verification
• Automated Reasoning
• +4 more

Michalis Kokologiannakis is a Tenure Track Assistant Professor of Computer Science at ETH Zurich's Department of Computer Science (D-INFK). Previously, he was a postdoctoral researcher at the Max Planck Institute for Software Systems (MPI-SWS), where he completed his doctorate. His research focuses on programming languages, formal software verification, and automated reasoning, with a particular emphasis on concurrency, weak memory models, and algorithmic techniques for scalable verification of concurrent data structures. Education: PhD in Computer Science, Max Planck Institute for Software Systems (MPI-SWS) MEng in Electrical and Computer Engineering, National Technical University of Athens (NTUA) Research Interests: Developing algorithms for analyzing large-scale concurrent program executions Formal verification of software correctness using mathematically rigorous techniques Tools like GenMC and Kater for weak memory model analysis Practical applications of research in non-academic contexts Teaching Philosophy: Advocates for interactive, dialogue-based education that connects students with cutting-edge research tools and methodologies. Envisions integrating his group's research software into classroom experiences. Labs/Teams: Collaborates with the Institute for Programming Languages and Systems at ETH Zurich to address challenges in verification and concurrency research.

• Machine Learning
• DevOps
• Distributed Computing
• +5 more

Roland Christen is a Senior Research Associate at the Lucerne School of Computer Science and Information Technology, part of the Lucerne School of Applied Sciences and Arts (HSLU). He specializes in software architecture, DevOps, distributed systems, and machine learning applications across healthcare and commerce. Christen actively contributes to STEM outreach initiatives like RobertaRegioZentrum Luzern and YoungTech@hslu, emphasizing education and technology accessibility. His professional expertise spans software design, microservices, agile methodologies, and database technologies. He leads research projects in quantum cryptography, medical image analysis (e.g., psoriasis detection), AI-driven e-commerce, and predictive modeling for tourism markets. Notable outputs include peer-reviewed work on machine learning for dermatology and conference presentations on quantum privacy amplification and GPU computing. Christen holds a Roberta® Teacher Certification from the Fraunhofer IAIS and has contributed to interdisciplinary projects like Deep Neural Yodeling and Skin-App: Medical Severity Grading of Hand Eczema. He also maintains strong ties to applied fields through lab collaborations and industry partnerships, bridging academia and practical innovation.