Dr. Friedrich Slivovsky is a computer scientist specializing in computational complexity, logic in computer science, and algorithm design. His research focuses on quantified Boolean formulas (QBF), SAT solving, and circuit minimization, with recent contributions to fine-grained complexity analysis and structure-aware lower bounds. He serves as a module co-ordinator for postgraduate and undergraduate courses in optimization at his institution. 2025: Fine-Grained Complexity Analysis of Dependency Quantified Boolean Formulas (QBF complexity, dependency schemes) 2024: Strategy Extraction by Interpolation (proof complexity), eSLIM: Circuit Minimization with SAT (logic synthesis), Hardness of Random Parity Encodings (CDCL solver analysis) 2023: Circuit Minimization with QBF-Based Synthesis (exact circuit optimization), Structure-Aware QBF Lower Bounds (tractability expansion) His work bridges theoretical analysis with practical applications in automated reasoning and formal verification. Teaching roles include coordinating optimization modules, emphasizing algorithmic efficiency and computational problem-solving.
Carlo Ciliberto is an Associate Professor in Machine Learning at University College London. His research focuses on theoretical and applied machine learning, with particular emphasis on structured prediction, meta-learning, optimal transport, and quantum computing. He has contributed to advancements in kernel methods, reinforcement learning, and robotics perception systems, notably through work with humanoid robots like the iCub. Key research interests include: Developing algorithms for distribution regression and Wasserstein-based learning. Exploring meta-learning frameworks for few-shot and incremental learning tasks. Designing robust systems for robotics applications, such as object recognition and tactile sensing. Investigating statistical foundations of quantum machine learning. Notable contributions to the field include the Manifold Structured Prediction framework, Sliced Wasserstein Kernels for distribution regression, and methodologies for conditional meta-learning. His work bridges theory and practice, with applications ranging from civil infrastructure analysis to humanoid robot perception.
Divesh Aggarwal is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS) and a Principal Investigator at the Centre for Quantum Technologies (CQT). He leads an active research group with multiple PhD students, postdocs, and collaborators focusing on theoretical computer science and cryptography. His research spans two primary themes: (1) fine-grained complexity and exponential algorithms for hard problems, particularly lattice problems, where he works on both finding faster algorithms and proving lower bounds; and (2) randomness extractors and their applications in cryptography, privacy amplification, and tamper-resilient systems. His work intersects cryptography, computational complexity, and quantum computing. Aggarwal's recent publications (2022-2025) demonstrate continued productivity in lattice-based cryptography, with significant contributions to non-malleable codes, quantum security, and hardness assumptions. His work appears consistently in top venues including STOC, FOCS, CRYPTO, and IEEE Transactions. The research shows a strong trend toward quantum-resistant cryptography and exploring the boundaries between classical and quantum computational hardness. He actively mentors students and researchers, with current PhD students including Zeyong Li, Rishav Gupta, Saswata Mukherjee, Aditya Morolia, and Ananta Mukherjee. His alumni have secured positions at institutions including NUS, IIT Delhi, and XJTLU. Aggarwal also organizes a weekly research seminar where his group discusses theoretical computer science topics. His teaching portfolio includes advanced courses such as Computational Complexity, Pseudorandomness, Design and Analysis of Algorithms, and Introduction to Quantum Computing. He serves on program committees for major conferences including CRYPTO, EUROCRYPT, and STOC, and on editorial boards for journals like Information Processing Letters.
Herman Geuvers is a Professor of Computer Science (Theoretical Computer Science) at Radboud University Nijmegen, where he leads the Foundations group within the Institute for Computing and Information Science (ICIS), part of the Faculty of Science. He also holds a part-time professorship at the Technical University of Eindhoven's Faculty of Mathematics and Computer Science. His roles include chairing the Examination Board for Computing Science at Radboud and the board of the Institute for Programming Research and Algorithmics (IPA). He chairs the Steering Committee of the FSCD conference and is a member of the COST Action CA20111 on Formal Proofs. Geuvers holds a Master's and PhD in Mathematics from Radboud University (1988 and 1993, respectively). He has led significant projects like the ARPA initiative to advance Proof Assistant usage and contributed to the EUTypes network. His teaching includes advanced courses on Type Theory, Semantics, Complexity, and Proving with Computer Assistance. His research focuses on logic in computer science, type theory, lambda calculus, and formal methods. He emphasizes integrating proof assistants like Coq into mathematics and software verification. He has supervised numerous PhD students and remains active in organizing international conferences and workshops.
Alexander Golovnev is an Assistant Professor at Georgetown University's Computer Science Department. His research focuses on computational complexity, algorithms, pseudorandomness, learning theory, and cryptography. He received his PhD from New York University in 2017, advised by Oded Regev and Yevgeniy Dodis, with postdoctoral positions at Columbia University, Yahoo Research, and Harvard University. His current teaching includes Matrix Rigidity (Spring 2025) and Introduction to Algorithms. He has developed a 5-Course Specialization on Discrete Mathematics and organized workshops on Matrix Rigidity and Fine-Grained Cryptography. Service includes program committees for CSR'22, FOCS'22, STOC'24, CCC'24, and ITC'25. Research interests span quantum computing reductions, lattice problems, circuit complexity, and fine-grained cryptography. His recent publications explore worst-case to average-case reductions, function inversion tradeoffs, and SNARK constructions. Awards include NSF CAREER Award (2024), Rabin Postdoctoral Fellowship (2018-2020), and IPEC excellent student paper award (2012). Current PhD/Master's advisees: Sidhant Saraogi (joint with Justin Thaler), Karthik Gajulapalli, Samuel King, and Satyajeet Nagargoje.
Xi Niu is an Associate Professor at the College of Computing and Informatics, University of North Carolina at Charlotte. His research focuses on data and text analytics, knowledge discovery, search behavior, and interactive information retrieval with a strong emphasis on computational serendipity and cybersecurity applications. His work bridges theoretical advancements with practical applications, particularly in developing machine learning models for cross-domain recommendations, improving user experience through serendipity enhancement, and automating cybersecurity threat analysis. Notable contributions include frameworks for modeling user curiosity in recommender systems and methodologies for contradiction detection in text. Recent research trends show a focus on leveraging deep learning techniques for extreme multi-label classification, contrastive learning in recommendation systems, and integrating topological analysis for understanding complex text patterns. His work also explores human-centered design principles in crowdsourcing and active learning systems.
Virginia Vassilevska Williams is Professor of Computer Science and Artificial Intelligence + Decision-making at MIT EECS. Her research focuses on theoretical computer science with emphasis on algorithms, computational complexity, and graph theory. She has made significant contributions to matrix multiplication complexity and fine-grained hardness results. Recent publications explore fundamental problems in graph algorithms including cycle detection, shortest paths, and clique enumeration. Her work demonstrates consistent advancement in understanding computational limits for graph problems and matrix operations. Key research themes include: Breaking barriers in matrix multiplication exponents Establishing hardness thresholds for approximation algorithms Developing efficient graph traversal methods for sparse structures Her 2024 publications continue this trajectory with refinements to the laser method for matrix multiplication and improved clique listing techniques. The research consistently pushes boundaries in algorithm optimality proofs and computational complexity theory.
Peter Jonsson is a Professor of Computer Science and Head of Unit at Linköping University's Department of Computer and Information Science (IDA), within the Artificial Intelligence and Integrated Computer Systems (AIICS) division. He holds a PhD in Computer Science from Linköping University (1996) and has been a Professor since 2004. His research focuses on computational complexity, constraint satisfaction problems (CSP), algorithms, and planning. He has advised over 15 PhD students and supervised multiple postdoctoral researchers, contributing significantly to theoretical computer science. His work bridges algorithm design and complexity analysis, with applications in AI and planning systems. Current research interests include parameterized complexity, infinite-domain CSPs, and structural restrictions in planning. Education: PhD (1996), MSc (1993), all from Linköping University. Key publications span CSP theory, planning algorithms, and complexity classification. His advising includes notable students like Biman Roy and Victor Lagerkvist. He has collaborated on projects funded through Swedish research programs and international grants. Labs/teams: Active in the AIICS division, focusing on foundational AI and algorithmic research.
Andrea Lincoln is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. Her research focuses on theoretical computer science, particularly average-case and fine-grained complexity. She holds a PhD from MIT (2020) and completed a postdoctoral fellowship at UC Berkeley with Barna Saha (2020-2021). Her work explores algorithmic complexity through reduction networks and dynamic systems analysis. Research interests include computational problem complexity, algorithm design for dynamic graphs, and average-case scenario analysis. She has contributed to understanding hardness proofs for problems like k-SUM and Orthogonal Vectors, as well as developing techniques for evaluating Boolean formulas and subgraph counting. Her recent work (2022-2025) emphasizes algorithmic efficiency under predictions, compression impacts on string distance measures, and cache-adaptive performance analysis. Notable publications address hypercycle database problems and delegation protocols for search problems. While no specific awards are listed, her prolific publication record reflects sustained contributions to theoretical computer science. Dr. Lincoln advises no listed students and has no documented grants in the provided text. Her work is associated with foundational algorithmic research rather than applied lab settings.
Dr. Michael Tautschnig is a Lecturer in Theoretical Computer Science at Queen Mary University of London, School of Electronic Engineering and Computer Science. He holds a PhD from Vienna University of Technology (2011) and a Master's from TU Munich (2006). His academic roles include Director of Undergraduate Admissions and teaching modules like Programming for Artificial Intelligence and Data Science. He has extensive industry experience as a Senior Software Development Engineer at Amazon Web Services (AWS), focusing on security and verification. Research interests include software verification, concurrency, decision procedures, formal methods, and embedded systems. He has contributed to projects like CBMC (C Bounded Model Checker) and FShell, emphasizing tools for program analysis and testing. His work spans formal verification of low-level software, weak memory models, and automated testing frameworks. Publications highlight contributions to model checking, concurrency analysis, and verification competitions. Awards include a patent for API optimization and a best paper award. He has secured grants such as the Google Faculty Research Award (2014-2015) and GCHQ Small Grant (2014-2015). Active in conference organization (e.g., CAV, TACAS) and program committees, he promotes academic-industrial collaboration in software verification. Labs/Teams: Collaborations include AWS Security, University of Oxford, and TU Wien. His work bridges academia and industry, focusing on scalable verification tools for large software systems.
Parinya Chalermsook is a Professor of Algorithms at the University of Sheffield, affiliated with the Foundations of Computation Group in the Department of Computer Science, Faculty of Engineering. He also holds a visiting associate professor position at Aalto University, Finland. His research focuses on theoretical computer science, particularly the interplay between algorithms and mathematical optimization, with strong interests in extremal combinatorics and their applications in TCS. His work spans parameterized complexity, approximation algorithms, computational complexity, and discrete optimization. The recent articles and talks highlight a strong trend in fine-grained and parameterized computational geometry, graph algorithms, and the synergy between continuous and discrete optimization. His research is deeply theoretical, often bridging mathematical disciplines with algorithmic challenges. Simons-Berkeley Research Fellowship (2017) ERC Starting Grant (~1.4M Euro, 2017–2024) Academy of Finland Research Fellowship (~900K EUR, 2017–2022) He has supervised numerous PhD students and hosted postdoctoral fellows, fostering a vibrant research group. His research has been supported by major grants from the European Research Council and the Academy of Finland. He actively contributes to the academic community through program committees (e.g., STOC, SODA, ICALP) and organizing workshops at Dagstuhl and Hausdorff Institute. He is a key member of the Foundations of Computation Group at Sheffield and has previously contributed to the TCS communities at Aalto University and Max Planck Institute for Informatics.
Chris Brzuska is an Associate Professor at Aalto University's Department of Mathematics and Systems Analysis, part of the School of Science. His research focuses on cryptography, security protocols, and formal verification of cryptographic systems. He has contributed to key areas including post-quantum cryptography, white-box security, obfuscation, and game-based security models. His work often involves rigorous analysis of cryptographic primitives and protocols, emphasizing practical security and formal proofs. Brzuska's recent research includes studies on LWE assumptions, garbling schemes, TLS security, and resistance against side-channel attacks. He has authored or co-authored over 30 publications in top-tier conferences and journals, such as CRYPTO, EUROCRYPT, and ASIACRYPT. His work bridges theoretical foundations and applied cryptography, addressing real-world security challenges in protocols like TLS and messaging frameworks.
Bundit Laekhanukit is an Associate Professor at the Institute for Theoretical Computer Science of Shanghai University of Finance and Economics. Born in Songkhla, Thailand, he earned his PhD from McGill University in 2014 under the supervision of Professor Adrian Vetta. His research focuses on: Approximation algorithms Hardness of approximation Parameterized complexity Fine-grained complexity He has held prestigious research positions at institutions including: Simons Institute for the Theory of Computing (UC Berkeley, Fall 2014) Swiss AI Lab IDSIA Weizmann Institute of Science (postdoctoral fellow) Scientific honors include: National-Youth-1000-Talent Program Simons Institute Research Fellowships
Marc Roth is a Lecturer in Theoretical Computer Science at the School of Electronic Engineering and Computer Science, Queen Mary University of London, and an Associate Member of the Department of Computer Science at the University of Oxford. He previously held research positions at Oxford, including Senior Research Associate in Algorithms and Complexity Theory and Junior Research Fellow at Merton College. His research focuses on computational counting problems, particularly the multivariate and exact complexity of infeasible counting problems, with applications in network analysis, bioinformatics, and graph neural networks. Key areas include motif counting in higher-order networks, parameterized and fine-grained complexity, approximation algorithms, and descriptive complexity theory. His recent work aims to extend motif counting beyond graphs to higher-arity relational structures, with implications for the expressive power of hypergraph neural networks. He is currently recruiting a PhD student to work on these topics. Marc Roth earned his PhD in Computer Science from Saarland University and the Cluster of Excellence MMCI under the supervision of Holger Dell. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: Marc Roth is actively involved in academic supervision, currently advertising a fully funded PhD studentship on motif counting in higher-order networks. The position includes tuition coverage and a UKRI-level stipend, indicating active grant support. He welcomes prospective PhD applicants and is engaged in mentoring and research guidance. Labs and Teams: Marc Roth is affiliated with the theoretical computer science group at Queen Mary University of London. He was previously part of the algorithms and complexity theory group at the University of Oxford under Leslie Ann Goldberg and associated with Merton College. His research is collaborative and theory-driven, focusing on algorithmic foundations of network analysis.
Delia Kesner is a Full Professor in Computer Science at UFR d'Informatique, Université Paris Cité, and a member of the Institut de Recherche en Informatique Fondamentale (IRIF), a joint CNRS unit. She holds significant leadership roles as Deputy Director of the École Doctorale 386 de Sciences Mathématiques de Paris Centre, Director (France) of the IRP SINFIN, and In charge of the ERC Unit at CNRS Informatics. She is a Senior Member of the Institut Universitaire de France (IUF) and a Corresponding Member of the Academy of Sciences of Torino, reflecting her high standing in the academic community. Her research is centered on the theoretical foundations of computation, with primary interests in programming languages, lambda calculus, type theory, proof theory, linear logic, and rewriting theory. Her work delves into specific areas such as evaluation strategies (call-by-need, call-by-push-value), intersection type theory, the Curry-Howard isomorphism, resource calculi, and explicit substitutions. This research provides deep insights into the semantics and behavior of programming languages and logical systems. The trends in her recent publications, which appear in top venues like Logical Methods in Computer Science (LMCS) and PACMPL, show a consistent focus on the interplay between logic and computation. Her work often involves developing fine-grained, quantitative models for lambda calculi, investigating inhabitation problems, creating strong bisimulations for classical calculi, and revisiting foundational systems like the Bang Calculus. A unifying theme is the use of type systems and resource-aware semantics to analyze and understand computational processes with precision. Senior Member of Institut Universitaire de France (IUF) Corresponding Member of the Academy of Sciences of Torino RAICES International Cooperation Award (2016) Delia Kesner has a distinguished record of mentoring, having advised numerous PhD students, including well-known researchers like Beniamino Accattoli, Pierre Vial, and Pablo Barenbaum. Her leadership extends to major collaborative research projects and working groups, such as the LIA INFINIS (co-directed with Argentina), the GDRI Linear Logic, and the French working groups SCALP and LHC. She has coordinated international projects like ECOS-Sud and STIC-Amsud. Her service to the community is extensive, having served on the steering committees of FSCD, ETAPS, and IFIP WG 1.6, and on the program committees of countless top conferences including POPL, LICS, and ICFP. She is a central figure in several international research communities, organizing and co-organizing workshops like HOR, LSFA, and the Workshop on Intersection Types. Her work in the LIA INFINIS and IRP SINFIN highlights her commitment to fostering long-term international cooperation, particularly between France and Argentina.
