Find Academic Supervisors

• Machine Learning
• High-Dimensional Statistics
• Data Science
• +3 more

Professor Marco Mondelli is a faculty member at the Institute of Science and Technology Austria (ISTA), where he has been employed since 2019. He was promoted from Assistant Professor to full Professor in 2025. His research focuses on machine learning, high-dimensional statistics, data science, information theory, and modern coding theory. He maintains affiliations with the ELLIS network as an ELLIS Member and collaborates extensively across European institutions. His research interests span theoretical foundations of machine learning with particular emphasis on high-dimensional phenomena, neural network theory, information limits in statistical inference, and applications in quantitative genetics. He has made significant contributions to understanding neural collapse phenomena, spectral methods for high-dimensional estimation, and privacy-preserving machine learning in overparameterized regimes. His work combines rigorous mathematical analysis with practical applications in modern AI systems. Mondelli's recent publications demonstrate strong trends in theoretical machine learning, particularly analyzing the behavior of deep neural networks, developing optimal estimation algorithms for high-dimensional problems, and establishing fundamental limits for various learning tasks. His research bridges statistical physics approaches with modern machine learning theory. ERC Starting Grant recipient for project "Inference in High Dimensions: Light-speed Algorithms and Information Limits" Co-organizer of the "Youth in High Dimensions" conference series Editor for IEEE BITS special issue on Generative Models Associate Editor for IEEE Transactions on Information Theory Professor Mondelli actively mentors PhD students and postdoctoral researchers, currently supervising seven PhD students and one postdoc. His group has secured significant funding through the ERC Starting Grant, supporting multiple positions at all levels. He serves on program committees for top machine learning conferences including NeurIPS, ICML, and ICLR, and has organized workshops on high-dimensional learning dynamics.

• High-Performance Computing
• Performance Modeling
• Parallel Computing
• +3 more

Alexandru Calotoiu is a Researcher in the Department of Computer Science at ETH Zürich, affiliated with the Professorship for Scalable Parallel Computing. His work focuses on performance modeling, high-performance computing (HPC), serverless systems, and cloud computing. He leads research in empirical performance modeling for complex applications, optimization of parallel algorithms, and scalable cloud architectures. Key research areas include noise-resilient performance models, serverless computing frameworks, and compositional parallel programming. He has contributed to benchmarking tools like SeBS and developed techniques for loop scheduling, static analysis, and resource disaggregation in HPC environments. His publications from 2023–2025 emphasize serverless systems (e.g., FaaSKeeper, Cppless), performance embeddings for optimization, and specialized supercomputing for climate science. These studies address scalability, reproducibility, and cross-platform performance portability in data-centric workloads. No scientific awards are explicitly listed, but his work has been presented at leading conferences such as ISCA and IEEE/ACM events. He collaborates on projects like rFaaS (RDMA-enabled serverless platforms) and Process-as-a-Service frameworks. His research bridges theoretical models with practical implementations in distributed systems and cloud infrastructure.

• Distributed Computing
• Algorithms
• Fault-Tolerance
• +4 more

Soma Chaudhuri is an Associate Professor of Computer Science at Iowa State University. She earned her B.S. in Mathematics and Computer Science from MIT (1984), and M.S. and Ph.D. in Computer Science and Engineering from the University of Washington (1987, 1990) under Professor Richard E. Ladner. Her research specializes in theoretical distributed computing, including algorithm design, lower bounds, and impossibility results for asynchronous/semi-synchronous systems. Current investigations focus on leveraging inexact timing information to solve canonical distributed computing problems. She develops techniques for simulating synchronous fault-tolerant algorithms in timing-uncertain environments and establishing complexity hierarchies for shared objects. Dr. Chaudhuri teaches undergraduate and graduate courses in algorithm design, theory of computation, operating systems, and specialized topics in distributed systems. She has served on program committees for major conferences including PODC and ICDCS, and reviews for leading journals in computer science. Her publications establish foundational results in consensus problems, shared memory consistency, and register implementations, contributing to theoretical frameworks for fault-tolerant distributed computing.

• Distributed Systems
• Data Management
• Dependability
• +4 more

Rui Oliveira is an Associate Professor at Universidade do Minho in Portugal, affiliated with the Distributed Systems Group. His research focuses on dependable distributed systems, fault-tolerant database systems, and large-scale distributed systems. He teaches Distributed Systems across undergraduate, master's, and doctoral programs. Research Interests His work spans epidemic communication protocols, data management in cloud environments, and high-performance transactional middleware. Recent contributions include frameworks for privacy-preserving machine learning (SOTERIA), distributed tracing (CAT), and fault-diagnosis tools for I/O behavior analysis. Key Contributions Developed the Loom disaggregated database system, the TADA distributed agreement toolkit, and the DATAFLASKS epidemic storage substrate. His work emphasizes practical implementations of scalable and resilient distributed systems. Labs/Teams Affiliated with HASLab (Hardware and Software Systems Laboratory), focusing on distributed systems research. Collaborates with industry on cloud computing and cybersecurity challenges.

• Computer Vision
• Image Processing
• Lighting Technology
• +4 more

Géza Várady is an Associate Professor at the University of Pécs (PTE), holding roles such as Vice Dean for Scientific Affairs at the Faculty of Engineering and Information Technology. He has served as Head of the Department of Technical Informatics and has held various academic leadership positions. His career spans over two decades, with research focusing on computer vision, image processing, and lighting technology. He holds a PhD from the Doctoral School of Informatics at the University of Pannonia. Education includes a Master’s degree in Computer Engineering and a PhD in Informatics. He has also held research positions internationally, such as a Leonardo Fellowship at Schefenacker GmbH in Stuttgart, Germany. His research interests include mesopic vision models, color correction systems, 3D depth sensing using monocular cameras, and drone-based applications. He leads the drone research team and has supervised doctoral students in areas like image data correction and 3D modeling. Notable achievements include the 2024 Publication Excellence Award, IBM Faculty Awards for adaptive lighting systems, and the Walsh Weston Award for contributions to lighting science. He actively participates in academic governance, serving on national committees like the Hungarian Academy of Sciences’ Engineering Sciences Committee and the John von Neumann Computer Society. Publications span over 100 articles in journals like Lighting Research and Technology and Technical Gazette , focusing on topics ranging from photogrammetry to autonomous drone control. He has authored textbooks on MPI programming and computer architecture.

• Numerical Optimization
• Mixed-Integer Nonlinear Programming
• Global Optimization
• +14 more

Ruth Misener is a Professor in the Department of Computing at Imperial College London, where she leads the Computational Optimization Group and holds the BASF/RAEng Research Chair in Data-Driven Optimization (2022–2027). She is affiliated with the Faculty of Engineering and contributes to interdisciplinary research institutes including the Data Science Institute, the Institute for Molecular Science and Engineering, and the Sargent Centre for Process Systems Engineering. Her research lies at the intersection of numerical optimization, operations research, and machine learning, with applications in chemical engineering, bioprocess optimization, energy systems, and industrial scheduling. She develops global optimization algorithms for mixed-integer nonlinear programs (MINLP), focusing on real-world challenges such as heat recovery network design, petrochemical process optimization, and robust bioreactor operation. A key innovation is her work on optimizing over machine learning surrogates, including tree ensembles and neural networks, enabling data-driven decision-making under uncertainty. Her recent publications demonstrate a strong trend toward integrating Bayesian optimization with active learning, explainable AI, and industrial applications, particularly in collaboration with BASF, Royal Mail, and Eli Lilly. She develops and maintains open-source optimization tools such as ROmodel, OMLT, and ENTMOOT, which are publicly available on GitHub. STEM for Britain acceptance Runner-Up Presentation Award at PSE@ResearchDayUK Best Quality Poster to Simon Olofsson 1st Poster Prize at UK/Ireland Annual Meeting of the Society for Industrial & Applied Mathematics (2018) 2nd Poster Prize at Centre for Process Systems Engineering Industrial Consortium Meeting (2017) 1st Poster Prize at 2nd PSE@ResearchDayUK (2017) 2nd Presentation Prize at Department of Computing Research Associate Symposium (2017) Runner-Up for May Hicks Award (via student Natasha Page) Ruth supervises a dynamic research team and has examined and mentored numerous PhD students, including Jean Kossaifi, Robert Walecki, Alexander Thebelt, and Toby Boyne. She leads major research grants, including the BASF/RAEng Research Chair and the IConIC Prosperity Partnership, and collaborates with industry partners to advance continuous manufacturing and data-driven process optimization. Her team actively disseminates work through open-access publications, video presentations, and social media.

Jose Manuel Claver IborraView profile

Professor

• Parallel and Distributed Computing
• Cloud Computing
• Video Coding
• +7 more

Dr. José Manuel Claver Iborra is a Full Professor at the Department of Computer Science, School of Engineering (ETSE-UV), University of Valencia. He holds a PhD in Computer Science (Parallel and Distributed Computing program) from the Technical University of Valencia and an MSc in Physics (specialized in Electronics and Computer Science) from the University of Valencia. As an IEEE Senior Member, he focuses on cloud computing, video coding, parallel/distributed systems, reconfigurable computing, and network protocols for real-time applications. Current Research: Cloud-based video encoding, GPU acceleration for DNA analysis, FPGA-based network protocols, and indoor localization systems Academic Leadership: Coordinator of the UV-Tirant node in the Spanish Supercomputing Network (RES) His recent publications analyze GPU-based motion estimation, heterogeneous computing for video standards (H.264/AV1), and QoS scheduling algorithms. He supervises PhD and Master's theses on sensor networks, FPGA programming platforms, and parallel applications. Scientific Awards IEEE Senior Member He has directed funded projects on cloud infrastructure, distributed video processing, and reconfigurable systems since the 1990s.

Elsa L GunterView profile

Research Professor

• Formal Methods
• Programming Languages
• Software Engineering
• +7 more

Elsa L Gunter is a Research Professor and Senior Lecturer at the University of Illinois at Urbana-Champaign's Department of Computer Science. Her academic background includes a Ph.D. in Mathematics from the University of Wisconsin, Madison. She leads research in formal methods, programming languages, and human-computer systems. Research Interests: Her work spans formal verification, programming language semantics, automated theorem proving, and security. She develops tools for compiler optimization verification, human-automation system safety, and concurrent program analysis. Key projects include the VeriF-OPT framework for parallel program transformations and Tutela for human-computer system protection analysis. Publications Focus: Her recent research emphasizes compiler verification, concurrency models, and human-system interaction. Work includes symbolic analysis for CSP, dependently-typed session systems, and robustness verification for safety-critical interfaces. Awards: Most Influential 10 Year Paper award at Requirements Engineering (RE 2010) EASST Best Software Science Paper at ETAPS 2001 Best Paper award at Fourth International Conference on Requirements Engineering (2000) Funding and Labs: Secured NSF grants for projects on parallel program verification ($450K) and human task analysis ($500K). Leads the Formal Methods and Verification Lab, collaborating with NASA on NextGen aviation systems. Student Advising: Mentored 7 PhD graduates and 12+ Master's students. Current PhD candidates work on secure distributed programming (Dennis Griffith) and formal methods for concurrent systems (Liyi Li, Susannah Johnson).

Nobuko YoshidaView profile

Professor

• Theory of Computing
• Concurrency
• Type Theory
• +2 more

Nobuko Yoshida is a Professor in the Department of Computer Science at the University of Oxford. Her research centers on theoretical foundations of concurrent and distributed systems, with a focus on type theory and programming languages. Affiliation: University of Oxford Research Interests: Theory of Computing, Concurrency, Type Theory, Programming Languages, Distributed Systems Conference Involvement: Active in POPL, ECOOP, ICFP, OOPSLA, PPDP, and ST30 tracks Her recent work analyzes multiparty session types, asynchronous/synchronous communication models, timed protocols, and type refinements for distributed systems. Key contributions include advancements in protocol specification, endpoint projection, and formal verification of concurrent systems. Yoshida has authored or co-authored publications at leading venues including ECOOP, ST30, and OOPSLA. Her articles emphasize compositional reasoning for multiparty sessions, hybrid protocol designs, and robustness in distributed environments with crash-stop failures. These works intersect with subfields such as session type theory, formal verification, and specification-agnostic implementation techniques.

Cedric LibertView profile

Researcher

• Concurrent Programming
• Software Visualization
• Student Preconceptions
• +3 more

Cedric Libert is a researcher in the Faculty of Computer Science, focusing on concurrent programming education and student preconceptions. His work bridges computer science pedagogy with practical curriculum design, particularly in secondary school and university-level programming courses. Research Interests : Concurrent programming, message-passing systems, computational thinking, and student-centric learning models. Key Themes : Addresses challenges in introducing concurrency early in education, analyzing student misconceptions, and developing visualization tools for complex programming concepts. Scientific Contributions : Published 6 research outputs between 2017-2020, including conference papers and book chapters on concurrency education and continuous assessment. Active in academic presentations, participating in workshops and seminars on programming pedagogy in Belgium.

Mohsen BayatiView profile

Professor

• Machine Learning
• Healthcare
• Data-Driven Decision Making
• +5 more

Mohsen Bayati is The Carl and Marilynn Thoma Professor of Operations, Information & Technology at Stanford Graduate School of Business. He holds courtesy appointments in Electrical Engineering and Radiation Oncology at Stanford University. His research focuses on applying machine learning, statistical physics, and decision models to healthcare and experimental design. Key contributions include reducing hospital readmissions via predictive analytics, developing human-in-the-loop AI systems like MEDIC for medication directions, and advancing causal inference methods for experiments with network interference. Education: PhD (Stanford University, Electrical Engineering), MSc (Stanford University, Operations Research), BSc (Sharif University of Technology, Mathematics). Research Interests: Machine learning in healthcare, causal inference, data-driven decision-making, and algorithmic efficiency. Current projects include optimizing experimental designs for staggered rollouts and improving prostate cancer recurrence predictions through clinical expertise integration with ML. Awards: 2024 PhD Faculty Distinguished Service Award, Younger Family Scholarships (2019–21), Spence Scholar (2015–16). Notable collaborations include work with Amazon, Microsoft Research, and Stanford Medical School. Advising: Mentored PhD students like Sadegh Shirani (causal message-passing) and Ruoxuan Xiong (optimal experimental design). Taught courses on business intelligence, data analysis, and operations research. Labs/Teams: Active in Stanford’s Golub Capital Social Impact Lab and collaborates with the FDA on drug interaction analysis using EHR data.

Armando Solar-LezamaView profile

Professor

• Program Synthesis
• Neurosymbolic Programming
• Machine Learning
• +5 more

Armando Solar-Lezama is a Professor at the MIT Schwarzman College of Computing , Associate Director and COO of MIT CSAIL, and leads the Computer-Aided Programming Group . He earned his BS in Computer Science and Mathematics from Texas A&M University and PhD (2008) from UC Berkeley under Rastislav Bodik. His research focuses on program synthesis at the intersection of Programming Systems and Artificial Intelligence, with recent work on neurosymbolic programming. Education: BS (Texas A&M), PhD (UC Berkeley) Labs: CSAIL, Center for Deployable Machine Learning His research explores automated reasoning and learning to reduce programming effort, including the development of the Sketch programming language. Current neurosymbolic work combines deep learning with logical reasoning for applications in multi-agent systems, RNA splicing, and interpretable policy generation. Recent projects include VLMaterial for procedural asset generation and CRUXEval for code evaluation benchmarks. Scientific awards include: Robin Milner Young Researcher Award (2024) Best Paper (PLDI 2005, PPoPP 2016) Outstanding Paper (EMNLP 2023) He advises graduate students through MIT's EECS PhD program and has developed courses like 6.820 Foundations of Program Analysis and Program Synthesis . His work impacts software synthesis automation, programming language design, and deployable machine learning.

• Program Specification and Verification
• Concurrent Separation Logics
• Symbolic Execution
• +5 more

Philippa Gardner is a Professor in the Department of Computing at Imperial College London, holding a UK Research and Innovation Established Fellowship (2018–2023). She specializes in program specification, verification, and concurrent separation logics. Her Gillian platform unifies symbolic execution, verification, and testing for C and JavaScript. She was elected a Fellow of the Royal Academy of Engineering (2020) and directed the Research Institute on Verified Trustworthy Software Systems (VeTSS, 2017–2023). Her work includes foundational contributions to separation logic, concurrent program reasoning, and mechanised language specifications. Education: PhD in Computer Science from Edinburgh (1992), supervised by Gordon Plotkin. Key roles: General Chair of POPL 2024, organiser of the Isaac Newton Institute's 'Verified Software' programme (2022). Research interests span formal methods, concurrency, and tool development. Notable grants include £1.5M EPSRC Established Career Fellowship (2018–2023) and £500K Facebook Research gift (2020). Awards include Imperial’s President and Rector’s Teaching Award (2013).

Christian BorgsView profile

Professor

• Artificial Intelligence
• Network Science
• Graph Theory
• +5 more

Christian Borgs is a Professor in the Electrical Engineering and Computer Sciences (EECS) department at the University of California, Berkeley. He is affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab and directs the Bakar Institute of Digital Materials for the Planet (BIDMaP). Previously, he co-founded and led Microsoft Research New England and the Theory Group at Microsoft Research for over 22 years. Borgs holds a Ph.D. and Habilitation in Mathematical Physics from the University of Munich and Free University Berlin, respectively, and served as a C4 Chair of Statistical Mechanics at the University of Leipzig. His research spans network science, graph theory, AI ethics, and applications in materials science. Notable contributions include the theory of graph limits, network modeling, and foundational work in statistical physics applied to computer science. He has authored ~150 papers and holds 30 patents. Awards include the Karl-Scheel Prize and Fellowships from the American Mathematical Society and AAAS. Borgs collaborates extensively with his spouse, Jennifer Chayes, on interdisciplinary projects. His recent work focuses on responsible AI, differential privacy, and AI-driven materials discovery for climate change mitigation.

• Algorithms and Computational Complexity
• Temporal Graphs
• Algorithmic Graph Theory
• +6 more

George B. Mertzios is an Associate Professor in the Department of Computer Science at Durham University, affiliated with the Algorithms and Complexity Research Group (ACiD) within the School of Engineering and Computing Sciences. He has held academic positions at Durham since 2011, progressing from Lecturer to Senior Lecturer and then to Associate Professor since 2017. He has also held visiting positions at institutions including the University of Bordeaux/CNRS and the University of Haifa. His research interests lie at the intersection of theoretical computer science and network science, with a strong focus on temporal graphs , algorithmic graph theory , parameterized complexity , and combinatorial optimization . He investigates efficient algorithms for dynamic and evolving networks, geometric graph models, and computational problems in network evolution and connectivity. His work often bridges foundational theory with applications in network design and distributed systems. The recent publications and ongoing activities of George B. Mertzios demonstrate a consistent and impactful research trajectory centered on the algorithmic foundations of temporal and dynamic networks. His work spans complexity analysis, algorithm design, and structural graph theory, with a notable emphasis on temporal vertex cover, sliding window models, and connectivity in time-varying graphs. He frequently publishes in top-tier conferences such as ICALP, MFCS, AAAI, and STACS, as well as leading journals including the Journal of Computer and System Sciences and Algorithmica . Gold Medal, Balkan Mathematical Olympiad, 1998 Distinguish Diploma, Bulgarian National Mathematical Competition 'Chernorizets Hrabar', 1998 Certificate of Merit, Mediterranean Mathematics Competition, 1999 Best paper award of Track C, ICALP 2010 Best student paper award, SAND 2024 George B. Mertzios has been actively involved in research supervision and leadership. He has supervised multiple PhD students to completion and currently advises ongoing doctoral research. He has served as Principal Investigator for EPSRC grants on Algorithmic Aspects of Temporal Graphs and Algorithmic Aspects of Intersection Graph Models , and as a Co-Investigator on projects related to graph coloring. He is a frequent organizer of scientific workshops, including the Algorithmic Aspects of Temporal Graphs series at ICALP and Dagstuhl seminars, and serves on the program committees of numerous international conferences such as MFCS, IWOCA, and SAND. He is a key member of the Network Engineering Science and Theory in Durham (NESTiD) research group, where he coordinates seminar series and fosters collaborative research in network algorithms and theory.