Nate Foster is a Professor of Computer Science at Cornell University and Associate Dean for Research since 2024. He works in the intersection of programming languages and network science , focusing on formal methods for network verification , language design , and type systems . Research Focus: Ethics, Law, and Policy in Information Science Human-Computer Interaction Network Science and Software-Defined Networking Formal Verification and Type Systems Provenance and Security Scientific Awards: ACM Fellow (2025) ACM SIGPLAN Robin Milner Award (2023) ACM SIGCOMM Rising Star Award (2018) National Science Foundation CAREER Award (2013) Current Projects include Reinforcement Learning for Network Security , Formal Foundations for Programmable Data Planes , and (Co)-Algebraic Foundations for Programmable Networks . His recent publications focus on network verification (NetKAT, StacKAT), data plane programming (Petr4, SafeP4), and security (Ancile, Proof-Carrying Code).
Nathanaël Fijalkow is a senior researcher at CNRS in LaBRI, Bordeaux, where he leads the Synthesis team. His work bridges program synthesis, games on graphs, automata theory, and applications in machine learning and formal verification. Research interests include: Program synthesis and code generation Game theory for algorithmic verification Linear Temporal Logic learning Probabilistic automata and dynamical systems Boolean network synthesis for biological modeling Recent publications focus on GPU-accelerated program synthesis, decidable classes of POMDPs, and optimal transformations in automata theory. He received the AAAI 2025 Outstanding Paper Award. He supervises PhD students and collaborators in the Synthesis team, working on projects like ANR ZADyG, ANR Shannon meets Cray, and PEPR IA SAIF. The team develops tools such as Scarlet and BoNesis for LTL learning and Boolean network analysis.
Dr. Oleksii Bychkov serves as Head of the Department of Software Systems and Technologies at Taras Shevchenko National University of Kyiv's Faculty of Information Technology, a position he has held since 2014. His extensive career includes roles as Deputy Dean of the Faculty (2013-2016), Assistant Professor at the Faculty of Cybernetics (2001-2013), and Director of the Information Technology Research Center (2006-2013). A State Prize winner and member of the Ukrainian Academy of Science, he bridges theoretical cybernetics with practical information systems development. Education: Doctor of Sciences (DSc) in Information Technology PhD in Physics and Mathematics Dissertation: "Construction of optimal qualitative characteristics of stochastic neutral equations of neutral type" (Specialty 01.05.01) Dissertation: "Information technologies for analysis and synthesis of continuous-discrete information systems and processes based on fuzzy hybrid automata" (Specialty 05.13.06) Research Focus: Dr. Bychkov pioneers cyber-physical systems and fuzzy modeling through groundbreaking work in possibility theory and hybrid automata stability. His research integrates mathematical modeling with data science to solve complex problems in aerospace, healthcare, and economic systems. The development of perceptive elements and Lyapunov optimal functions represents significant theoretical contributions with practical software implementation. Publication Trends: Analysis of his 15 most recent works reveals a strong trajectory toward applying possibility theory to real-world cyber-physical systems. Key themes include stability analysis of hybrid automata (40% of publications), fuzzy differential equations (30%), and data-driven solutions for information systems (30%). His work increasingly intersects with AI applications in real estate valuation, aircraft dynamics, and educational technology. Scientific Recognition: State Prize of Ukraine in Science and Technology Membership in Ukrainian Academy of Science Research Leadership: Dr. Bychkov directs the Erasmus+ iBIGworld initiative for Big Data innovations while securing grants for mathematical modeling of economic systems. His mentorship program develops next-generation researchers in cybernetics, with recent projects focusing on continuous-discrete process optimization and social information retrieval systems. Technical Infrastructure: As former Director of the Information Technology Research Center, he established specialized labs for hybrid automata simulation and fuzzy system development. Current teams focus on cyber-physical testbeds for aircraft dynamics and real-time social search algorithms within university information systems.
Martin Kruliš is an associate professor in the Department of Distributed and Dependable Systems at the Faculty of Mathematics and Physics, Charles University , Prague, Czech Republic. His primary roles include research, teaching, advising, and leading projects that bridge high-performance computing, GPU programming, and self-adaptive systems. Education: While exact details of his own degrees are not provided, Dr. Kruliš’s extensive publication record and faculty position at Charles University indicate advanced training in computer science with specialization in parallel and distributed computing. Research Interests: High-Performance GPU Computing: Deep investigation into CUDA kernel optimization, memory bandwidth utilization, and workload dispatching for massively parallel accelerators. Self-Adaptive & Self-Optimizing Systems: Designing ensemble-based abstractions that integrate machine-learning estimators to enable runtime adaptation in component architectures. Parallel Algorithms & Data Structures: Development of cache-friendly, SIMD-aware, and GPU-accelerated algorithms for clustering, dimensionality reduction, and similarity search. Software Engineering for Parallelism: Creation of C++ libraries, DSLs (e.g., Bobolang), and educational tools (ReCodEx) that simplify parallel programming and automated evaluation. Publication Trends: Across 2011–2025, Kruliš’s articles reveal a clear trajectory from foundational GPU-accelerated indexing and multimedia retrieval toward sophisticated self-adaptive systems that leverage machine learning. Recent works (2023–2025) increasingly focus on integrating LLMs and neural networks into compiler and runtime optimization loops, reflecting a convergence of AI and systems research. Scientific Awards & Recognition: No specific awards or fellowships are mentioned in the provided text; however, sustained publication in top-tier venues (JPDC, IPDPS, Euro-Par, SEAMS) and active involvement in program committees and tool development indicate strong peer recognition. Teaching & Student Supervision: Teaches Programming in Parallel Environment (NPRG042) , Advanced Programming in Parallel Environment (NPRG058) , Computer Systems (NSWI170) , and Software Projects . Supervises numerous bachelor’s and master’s theses; exact student names are not listed in the text. Labs, Projects & Tools: ReCodEx: A widely used platform for semi-automated evaluation of programming assignments at Charles University. Simdex: A modular simulator of the ReCodEx backend that enables realistic experimentation with self-adaptive job dispatching and machine-learning controllers. Active contributor to open-source repositories on GitHub, focusing on GPU kernels, benchmarking frameworks, and educational tooling.
Damien Mondou is a contractual research teacher (ATER) at the University of La Rochelle, holding a PhD in Computer Science. He works in the intersection of interactive systems, adaptive content management, and formal modeling. Developed the CIT framework (based on timed automata) for interactive experience modeling Created software platforms: CELTIC (modeling editor) and EDAIN (AI-driven execution driver) Specializes in machine learning (Q-Learning), behavioral modeling, and supervisory systems His research focuses on adaptive digital content, combining formal methods with reinforcement learning to enable dynamic re-scripting of interactive experiences. Key applications include serious games and museum robotics. Recent publications address hybrid supervision approaches, remote scenario adaptation, and formal modeling of interactive systems using automaton networks. Teaching experience includes logic programming (Prolog), constraint programming, software engineering, and advanced data structures across multiple universities and programs.
Anca Muscholl is a Professor at the University of Bordeaux, affiliated with LaBRI (Laboratoire Bordelais de Recherche en Informatique), a prominent computer science research laboratory in France. Her research focuses on theoretical aspects of computer science, particularly in automata theory, logic, games, verification and synthesis, distributed systems, and foundations of databases. Her research interests span across several key areas in theoretical computer science: Automata theory, with particular focus on string transducers and weighted automata Formal verification and synthesis of distributed systems Logic in computer science, including two-variable logic on data words Communication models and message passing systems Web services composition and negotiation protocols Professor Muscholl has been actively involved in the theoretical computer science community through numerous editorial and leadership roles. She currently serves on the editorial boards of TheoretiCS, Logical Methods in Computer Science, and LIPIcs. She is also a member of the Council of the European Association for Theoretical Computer Science (EATCS) and the Comité national de la recherche scientifique (section 06). Her recent publications demonstrate continued productivity and influence in her field, with significant contributions to string transducers, distributed systems verification, and automata theory. Her work shows a consistent focus on theoretical foundations with practical applications in verification and distributed computing. Her professional service includes significant leadership roles such as: Steering committee chair of the EATCS International Colloquium on Automata, Languages and Programming (2018-2024) Editorial board member of Discrete Mathematics & Theoretical Computer Science (2001-2023) Editorial board member of Information Processing Letters (2006-2015) Steering committee member of the Symposium on Theoretical Aspects of Computer Science (STACS) (2008-2014) Executive committee member of GDR Informatique-Mathématique (2017-2023) Professor Muscholl has made substantial contributions to the theoretical computer science community through her research, service, and mentorship. Her work bridges theoretical foundations with practical applications in verification and distributed systems, contributing to the advancement of formal methods and their applications.
Vijay Bhattiprolu serves as an Assistant Professor in the Department of Combinatorics & Optimization at the University of Waterloo, where his research bridges theoretical computer science and advanced mathematical disciplines. His work focuses on fundamental questions in computational complexity and optimization theory, with significant implications for algorithm design and analysis. Dr. Bhattiprolu's academic journey includes a Ph.D. from Carnegie Mellon University (2019) under Venkat Guruswami, a postdoctoral fellowship at Princeton University and the Institute for Advanced Study (2019-2022) as part of the Simons Collaboration on Algorithms and Geometry, and undergraduate studies at the University of Illinois at Urbana-Champaign (2014) with research mentorship from Sariel Har-Peled and Mahesh Viswanathan. Ph.D. in Computer Science, Carnegie Mellon University (2014-2019) Postdoc, Princeton University / Institute for Advanced Study (2019-2022) B.Sc., University of Illinois at Urbana-Champaign (2011-2014) His research program centers on approximation algorithms, hardness of approximation, and their deep connections to functional analysis and convex geometry. Key themes include polynomial maximization over convex sets, spectral theory, asymptotic convex geometry, and sum of squares methods, with applications to fundamental optimization problems. This interdisciplinary approach leverages mathematical tools to establish tight bounds on computational efficiency and develop novel algorithmic frameworks. Dr. Bhattiprolu's publication record reveals consistent contributions to theoretical foundations of optimization, particularly in matrix norm approximation, quadratic form maximization, and geometric methods in algorithm design. His work frequently appears in premier venues including STOC, FOCS, and SICOMP, demonstrating sustained impact on understanding computational limits and advancing approximation techniques. He currently mentors three graduate students: Yang Xiao (Ph.D., co-advised with Chaitanya Swamy), Jacob Skitsko (Ph.D., co-advised with Kostya Pashkovich), and Martin Liu (Masters). His teaching portfolio includes advanced courses in approximation algorithms (CO759) and core optimization theory (CO250, CO370), reflecting his commitment to training the next generation of theoretical computer scientists.
Jonas Kantic is a Ph.D. student and Researcher at the Chair of Integrated Systems within the Faculty of Electrical Engineering and Information Technology at Technical University of Munich . Holding a Master of Science in Technical Informatics from Leibniz University Hannover, his work focuses on AI acceleration architectures and embedded systems design. Education Master's in Technical Informatics (2017-2020), Leibniz University Hannover Chinese Language Studies (2018-2019), Beijing Foreign Studies University Bachelor's in Technical Informatics (2013-2017), Leibniz University Hannover Research Focus Specializes in reservoir computing architectures Expertise in FPGA-based AI acceleration Investigates temporal/spatial compression techniques Develops efficient edge AI inference systems Applies machine learning to motorcycle control systems Works on hyperdimensional computing models Supervision Mentored 5+ students in RNN accelerators, CNN optimization, and stochastic computing Collaborates with industry partners (BMW Motorrad, NXP Semiconductors) Publications 2024 - Complex & Intelligent Systems: Cellular Automata for Reservoir Computing 2024 - IEEE NorCAS: FPGA Implementation for High-Speed Reservoir Models 2021 - Current Directions in Biomedical Engineering: Hearing Aid CNN Optimization
Professor Alexandra Silva is a faculty member in the Department of Computer Science at University College London . Her academic rank is Professor of Algebra, Semantics, and Computation . Her research spans Theory of Computation , Pure Mathematics , Artificial Intelligence , and Software Engineering , with a focus on formal methods and symbolic computation. She has contributed extensively to probabilistic programming, network verification, and automata theory. In recent years (2025 and 2024), her work has centered on formal verification , probabilistic systems , and symbolic execution . Key trends include applications of algebraic structures , logic in computer science , and automata theory . While no explicit scientific awards are listed in the provided text, her collaborative work with researchers like Kozen, Jacobs, and Hsu underscores her influence in theoretical computer science.
Alexander Artikis is an Associate Professor at the Department of Maritime Studies, University of Piraeus. His academic work bridges artificial intelligence, distributed systems, and maritime informatics, with a focus on complex event recognition and temporal reasoning. PhD in Multi-Agent Systems from Imperial College London Research areas include: Artificial Intelligence and Machine Learning Distributed and Real-Time Systems Event Calculus and Temporal Reasoning Maritime Data Analytics Recent publications highlight advancements in: Knowledge Graph Inconsistency Detection and Resolution Tensor-Based and Symbolic Register Transducer Formalisms Human-in-the-Loop Visual Analytics Temporal Specification Optimization Large Language Model Applications in Event Recognition Scalable Event Processing for Maritime Mobility He contributes to the academic community through program committee roles at premier conferences like AAMAS, IJCAI, and AAAI.
Sastry P S is a Professor at the Indian Institute of Science , specializing in interdisciplinary research in machine learning and its applications. His work bridges theoretical foundations with practical implementations in pattern recognition, data mining, and learning automata. Educated at IIT Kharagpur and IISc Bangalore Recognized with prestigious awards including the C.V. Raman Young Scientist Award and Indo-US Science Fellowship Current research focuses on stochastic models, neural networks, and statistical challenges in AI His editorial roles include Associate Editor for IEEE Transactions and affiliations with institutions like IEEE and Indian National Academy of Engineering.
Dr Xu Xu serves as Senior Lecturer in Complex Systems Modelling at the University of Sheffield's Department of Computer Science and is affiliated with the INSIGNEO Institute for in silico Medicine. She holds the role of Admissions Tutor and leads research in computational haemodynamics and multi-scale modeling for personalised cardiovascular healthcare, bridging computer science with biomedical applications. Her academic foundation includes advanced engineering degrees from prestigious institutions: BEng in Automation, Xidian University, China MSc in Control Systems Engineering (with Distinction), University of Sheffield PhD in Nonlinear Systems and Cellular Maps, University of Sheffield Dr Xu's research program centers on multi-scale lattice Boltzmann simulations of blood flow, compartmental cardiovascular modeling for personalised medicine, uncertainty quantification, and nonlinear control systems. Her work extends to cellular automata applications in swarm robotics and collective behavior, demonstrating interdisciplinary innovation across computational science, biomedical engineering, and control theory to advance in silico healthcare solutions. Analysis of her recent publications reveals a cohesive trajectory toward integrating sensitivity analysis, data assimilation, and parameter identification for cardiovascular personalisation. Her scholarship spans computational fluid dynamics (particularly lattice Boltzmann methods), automotive control systems, and swarm robotics, with increasing emphasis on clinical translation through the INSIGNEO Institute's in silico medicine framework. She has earned eight institutional awards recognizing excellence across multiple dimensions of academic practice: Inspirational teaching Research supervision Academic advising Dr Xu has successfully supervised six PhD candidates to completion and secured significant research funding as Principal Investigator. Her grant portfolio demonstrates versatility across biomedical and engineering domains: EPSRC-funded cardiovascular assessment project (£451k) Electric discharge machining optimization Multi-robot path planning systems As an active member of the Complex Systems Modelling Research Group and INSIGNEO Institute, she collaborates on cutting-edge in silico healthcare initiatives. Her leadership extends to academic governance through roles including Interim Deputy Head of Department and MSc Course Leader, where she achieved top-tier student satisfaction rankings in engineering education.
Afsaneh Rahbar is an Assistant Professor at Duke University's Department of Electrical and Computer Engineering within the College of Engineering. Her research focuses on compilers, high-performance computing, and distributed systems, with particular emphasis on mobile ad hoc networks and data communication protocols. M.S., Rice University (2018) Ph.D., Rice University (2021) Rahbar's work spans both theoretical and applied aspects of computing systems, including contributions to MANET protocols, distributed learning automata, and fault-tolerant circuit design. Her publications from 2009–2010 reflect early-career expertise in system architecture and network optimization. While no scientific awards are highlighted in available records, her research has been presented at major international conferences like IEEE ACS and ITNG. Rahbar actively contributes to academic training through courses in software engineering and systems programming.
James Woodcock is a Professor in the Department of Electrical and Computer Engineering at Aarhus University, specializing in Software Engineering and Computing Systems. With over 200 research outputs and 11 completed projects, he focuses on Formal Verification, Cyber-Physical Systems (CPS), and Robotics. His work integrates Digital Twins, Model Checking, and Process Algebra to ensure system reliability. Key research areas: Formal Verification, Robotics, CPS, Digital Twins, Security Engineering Active collaborations: University of York, University of Liverpool, University of Bremen Recent projects include: UK Trustworthy Autonomous Systems Verifiability Node (2020-2024) RoboTest: Model-Based Testing of Autonomous Robots (2018-2023) His 2025 paper on BDI Agent Verification and 2023 State-of-the-Art Report on Verified Computation highlight his leadership in applying Formal Methods to Robotics and Security. Over 2005-2025, he has published extensively in CPS , Formal Verification , and Model-Based Design . Awarded prestigious titles including Chartered Engineer (2011) and Distinguished Researcher (2019), he has served as Editor, PhD Examiner, and Visiting Lecturer globally.
Clément Hongler is an Associate Professor in the Chair of Statistical Field Theory at the School of Basic Sciences , École Polytechnique Fédérale de Lausanne (EPFL). He leads research at the intersection of statistical mechanics, quantum field theory, and deep learning theory. Education : B.Sc. (2006), M.Sc. (2008) in Mathematics from EPFL, and Ph.D. (2010) in Mathematics from Université de Genève. Career : Ritt Assistant Professor at Columbia University (2010-2014), Tenure-Track Assistant Professor at EPFL (2014-2018), and Associate Professor at EPFL since 2019. His research focuses on: Connections between lattice models and conformal field theories Dynamics of learning in neural networks Artificial life via continuous cellular automata Decentralized systems He actively teaches courses such as Probabilistic models of modern AI and participates in EPFL's doctoral programs, including Mathematics and EDMA (Environmental Data Science and Engineering). His lab ( CSFT ) collaborates on interdisciplinary projects bridging theoretical physics and machine learning.
