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Laurent DoyenView profile

Researcher

• Game Theory
• Automata Theory
• Formal Verification
• +7 more

Laurent Doyen is a CNRS Researcher at the Laboratoire Méthodes Formelles (LMF), ENS Paris-Saclay. He holds a PhD from the Université Libre de Bruxelles (2006) and an HDR from ENS Cachan (2012). His research focuses on formal methods, game theory, automata, and verification of quantitative and probabilistic systems. PhD: Université Libre de Bruxelles, 2006 HDR: ENS Cachan, 2012 Research Interests: Laurent's work centers on algorithms and tools for the verification and synthesis of reliable software, hardware, and embedded systems. His primary areas include game and automata theory, with a focus on discrete quantitative and probabilistic systems, timed and hybrid systems. He investigates synchronization, mean-payoff objectives, and imperfect information in games, contributing significantly to theoretical foundations and practical tools. The recent publications highlight a strong trend in stochastic games, synchronization in Markov decision processes, and quantitative verification. His work spans theoretical computer science, formal methods, and practical applications in system design, often appearing in top venues like LICS, ICALP, and CONCUR. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Laurent has advised several PhD students including Mahsa Shirmohammadi, Julien Reichert, Thomas Soullard, and Pranshu Gaba. He leads and participates in multiple research projects such as QuaVerif (PI), IFCPAR SMILeS (coPI), Cassting, ARiSE, and Quasimodo. He has been a Rutherford Visiting Fellow at the University of Warwick and is involved in various academic communities like GAMES, CFV, and GDR-IM. Labs and Teams: He is a key member of the Laboratoire Méthodes Formelles (LMF) at ENS Paris-Saclay and has been associated with the LSV (Laboratoire Spécification et Vérification) in the past. He contributes to the development of tools like Alaska and Alpaga for automata analysis and model checking.

• Computer Science
• Cryptography
• Blockchain
• +6 more

Giulia Fanti is an academic researcher affiliated with Carnegie Mellon University in the Computer Science Department . Her research focuses on privacy-preserving technologies, blockchain systems, and machine learning mechanisms, with significant contributions to federated learning, differential privacy, and cryptocurrency network design. Key Research Areas : Privacy in blockchain, Generative Adversarial Networks (GANs), Federated Learning, Game Theory applications to decentralized systems. Recent Publications : Her work explores liquidity provisioning in decentralized finance, truncated consistency models for image generation, and private data valuation frameworks. She has contributed to venues like NeurIPS, ICLR, and SIGMETRICS, often addressing privacy-utility tradeoffs.

• Trustworthy machine learning
• Explainable AI
• Interpretability
• +5 more

Damien Garreau is Professor for the Theory of Machine Learning at Julius-Maximilians-Universität Würzburg , Germany. Until March 2024 he served as Associate Professor in the Probability and Statistics team of the J. A. Dieudonné laboratory at Université Côte d'Azur and was a member of the Inria Maasai team in Sophia-Antipolis. Earlier positions include post-doctoral research at the Max Planck Institute for Intelligent Systems in Tübingen and PhD studies in the Inria Sierra team in Paris. Education & Career Path PhD, Inria Sierra team, Paris – advisors Sylvain Arlot & Gérard Biau Post-doc, Max Planck Institute for Intelligent Systems, Tübingen – mentor Ulrike von Luxburg Associate Professor, Université Côte d’Azur / Inria Maasai (until March 2024) Professor for Theory of Machine Learning, Julius-Maximilians-Universität Würzburg (since 2024) Research Focus Garreau’s research centers on trustworthy machine learning . He investigates how to explain, audit, and robustify modern AI systems, with particular emphasis on post-hoc interpretability , statistical guarantees of explanation methods, fairness , and causality . Representative contributions include theoretical analyses of LIME and Anchors, novel explanation methods such as SMACE and GLEAMS, and practical tools for vision and NLP that remain faithful under adversarial or out-of-distribution settings. Across computer vision, natural-language processing, and healthcare applications, his work bridges rigorous theory with impactful algorithms, advancing the societal goal of deploying AI systems whose decisions can be trusted and understood by humans. Scientific Awards & Recognition Best Paper Award , ECML 2024 Area Chair , ICML 2025 ANR JCJC Grant NIM-ML (2021–2025) Université franco-allemande support for Winter School on Causality and Explainable AI Advising, Grants & Collaborative Projects Garreau has successfully supervised or co-supervised a growing cohort of doctoral and master’s students, including Gianluigi Lopardo, Kensuke Mitsuzawa, Martin Charachon, Jonas Wacker, Samuel, Antonio, Magamed, Arthur Assad, Charbel Yahchouchi, and Mariana Chaves. He is the PI of the ANR JCJC project NIM-ML , whose goal is to develop next-generation interpretability methods endowed with statistical guarantees. He co-organizes the annual Winter School on Causality and Explainable AI , fostering Franco-German academic exchange. Labs & Teams Since 2024 he leads the Professorship for the Theory of Machine Learning at Julius-Maximilians-Universität Würzburg. Previously he was a core member of the Maasai Inria team on the Sophia-Antipolis campus, and an active collaborator of the J. A. Dieudonné mathematics laboratory. He maintains strong ties with the TML group at the Max Planck Institute for Intelligent Systems and regularly hosts joint visitors and workshops.

Cameron FreerView profile

Research Professor

• Probabilistic computing
• Foundations of probabilistic programming
• Random structures
• +5 more

Cameron Freer is a Research Scientist in the MIT Probabilistic Computing Project , with prior roles including Instructor in Pure Mathematics at MIT, Postdoctoral Fellow at CSAIL, and Project Associate Professor at Keio University. His work bridges probabilistic computing, logic, and theoretical computer science. Education PhD in Mathematics, Harvard University, 2008 (Thesis: Models with High Scott Rank ) Research Interests Freer's research explores the deep interplay between randomness and computation , focusing on: Foundations of probabilistic programming languages and systems Efficient samplers for discrete and continuous distributions Mathematics of random structures like graphons and exchangeable processes Computability in measure theory and probabilistic inference Publications Overview His recent work (2020–2024) advances probabilistic programming systems (e.g., GenSQL), theoretical frameworks for random graphs via Markov categories, and computable approaches to PAC learning. Earlier contributions include exact sampling algorithms, computable exchangeability, and algorithmic barriers in conditional probability. Academic Service Steering Committee Member, LAFI (formerly PPS) workshop series (2017–2025) Program Committee Chair/Co-chair, PPS 2017–2018 Session Chair, POPL 2017 (PPS track) Industry & Visiting Roles Chief Scientist, Remine (2017–2018) Research Scientist, Gamalon Labs (2013–2016) Lyric Labs Visiting Fellow, Analog Devices (2013–2014) Project Associate Professor, Keio University (2021–2024) Labs & Collaborations Freer collaborates extensively with the MIT Probabilistic Computing Project, Harvard Logic Group, and international partners in Oxford, CMU, and Keio University. His work integrates theoretical insights with practical systems in AI and probabilistic inference.

• string algorithms
• data structures
• graph algorithms
• +5 more

Shay Golan is a Senior Lecturer in the Computer Science department at Ariel University, where he teaches core algorithms and data structures courses. Previously, he held postdoctoral positions at Reichman University, Haifa University (hosted by Shay Mozes and Oren Weimann), and was a Fulbright Postdoctoral Fellow at UC Berkeley's EECS department (hosted by Jelani Nelson). He earned his Ph.D. in Computer Science from Bar-Ilan University under the supervision of Ely Porat and Tsvi Kopelowitz. Dr. Golan's primary research focuses on string algorithms with emphasis on streaming, small space, and dynamic settings. His work spans multiple subfields including data structures, graph algorithms, and discrete algorithms. He has made significant contributions to pattern matching, edit distance problems, and string compression techniques, with applications extending to bioinformatics for DNA sequence analysis. His recent publication trajectory (2022-2026) reveals a strong focus on string algorithmic challenges across multiple dimensions: theoretical complexity analysis (LZ77 compression, string covers), specialized string problems (2D string matching, hairpin structures), graph algorithm applications (planar graph distance oracles), and bioinformatics implementations (DNA minimizers). His work consistently appears in top theoretical computer science venues including STOC, SODA, and CPM, demonstrating both theoretical depth and practical relevance. Fulbright Postdoctoral Fellow at UC Berkeley As an educator, Dr. Golan has consistently taught foundational computer science courses including Algorithms 1, Algorithms 2, Data Structures, and Computability since 2016. His research is supported through collaborations with leading institutions including Reichman University, Haifa University, and UC Berkeley. He maintains active research partnerships with prominent computer scientists including Itai Boneh, Ely Porat, and Tsvi Kopelowitz. Dr. Golan participates in the broader theoretical computer science community through conference presentations and workshop participation, with recorded talks available for several of his publications. His work bridges theoretical computer science with practical applications, particularly in computational biology and genomic sequence analysis.

Colin CooperView profile

Professor

• Computer Science
• Theoretical Computer Science
• Algorithms
• +3 more

Prof. Colin Cooper holds the position of Professor at the University of Hamburg, affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences and the Department of Computer Science. He is a Mercator Fellow supported by the German Research Foundation (DFG). His research interests include theoretical computer science, algorithms, and network analysis. He is located in office G-214 at Vogt-Kölln-Str. 30, Hamburg. Key recognitions include the Mercator Fellowship (DFG), reflecting his contributions to academic research. While specific grants or advising records are not detailed here, his affiliation and research focus suggest active engagement in computational and algorithmic studies. Contact details include fax +49 40 42883-2246 and email colin.cooper@kcl.ac.uk.

Anita WinterView profile

Professor

• Probability Theory
• Stochastic Processes
• Mathematical Biology
• +3 more

Prof. Dr. Anita Winter is a Professor at the University of Duisburg-Essen within the Faculty of Mathematics. Her research focuses on Probability Theory, Stochastic Processes, and their applications in areas like Mathematical Biology and Statistical Physics. She holds a prominent position in the field, with extensive contributions to branching processes, measure-valued processes, and tree-valued stochastic dynamics. Her work integrates algebraic and geometric structures with probabilistic methods, such as algebraic measure trees and pruning processes. She has published extensively in leading journals like *Stochastic Processes and their Applications* and *The Annals of Probability*. Her research often explores the interplay between population dynamics and spatial structures, including studies on coalescent processes, random graphs, and evolutionary models. Anita Winter has taught advanced courses in Probability Theory, Levy processes, and stochastic analysis across multiple semesters, reflecting her deep engagement with both research and education. Her team assistance, led by Dagmar Goetz, supports her academic activities and administrative coordination.

• Computational Social Science
• Data Science
• Multivariate Social Networks
• +10 more

Termeh Shafie is a full-time Professor in the Department of Politics and Public Administration at the University of Konstanz, specializing in Computational Social Science and Data Science. With a strong statistical foundation, she develops advanced methodologies for analyzing multivariate social networks while bridging archaeological network reconstruction with modern data science techniques through projects like NEXUS 1492. Key Research Areas: Multigraph modeling, network entropy analysis, isotope geoprovenance, and hypergraph representations Projects: NEXUS 1492 archaeological network reconstruction Her 15 most recent publications demonstrate significant contributions to network methodology (random multigraph models, centrality index analysis), archaeological applications (Caribbean attack networks, Iroquoian settlement patterns), and data privacy frameworks. Articles span 2012–2025 with interdisciplinary focus on statistical sociology, archaeological theory, and computational modeling. Current teaching includes courses on social network analysis, data science, and statistical learning. Office hours available via ILIAS booking system. Contact details provided for both academic and administrative correspondence.

• Mathematical Optimization
• Algorithmic Game Theory
• Multi-Agent Systems
• +3 more

Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with the Department of Mathematics within Faculty II – Mathematics and Natural Sciences. He holds a PhD in Mathematics from TU Berlin (2012) and previously served as an Assistant Professor for Operations Research at Humboldt-Universität zu Berlin (2014–2016) and led the Junior Research Group for Optimization under Uncertainty at the Einstein-Center for Mathematics (2014–2016). His research focuses on algorithmic game theory, mathematical optimization of multi-agent systems in traffic, telecommunications, and economics. Key areas include network flows, congestion games, mechanism design, and infrastructure networks. He actively contributes to academic activities as an associate editor for journals like International Journal of Game Theory and Operations Research Forum , and has served on program committees for conferences such as AAAI, EC, and WINE. Recent work includes studies on evolutionary road network reconstruction, robustness of potential-based flows, and algorithmic solutions for knapsack and assignment problems. His research bridges theoretical foundations with practical applications in transportation and resource allocation.

• Biodiversity
• Evolutionary Biology
• Zoology
• +3 more

Gabriele Gerlach is a Professor at the Carl von Ossietzky University of Oldenburg since 2007, affiliated with the Institute of Biology and Environmental Sciences and leading the Department of Biodiversity and Evolution of Animals. She also serves as an Adjunct Senior Scientist at the Marine Biological Laboratory (USA) and an Adjunct Professor at the Boston University Marine Program. Education : Ph.D. (1990) and Habilitation (2000) in Zoology and Ecology at the University of Constance, Germany. Research Interests : Biodiversity, evolutionary biology, and ecology combined with mathematical approaches. Her work focuses on operator theory, spectral analysis, and dynamical systems applied to biological and environmental questions. Publications : Recent contributions include mathematical frameworks for kernel operators, transition semigroups, and spectral convergence, bridging abstract analysis with computational biology.

Michael GentnerView profile

Researcher

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +2 more

Dr. Michael Gentner is a researcher at the Institute of Optimization and Operations Research, Ulm University, where he teaches courses including Angewandte Diskrete Mathematik (Combinatorics) and Advanced Discrete Algorithms. His institutional contact details include office Helmholtzstr. 18, Room 1.47, with Thursday office hours (14:00-16:00) and phone number +49 731 50-23637. His primary research focuses on structural graph theory and combinatorics, with expertise in domination theory, independence number, zero forcing sets, and extremal graph problems. Gentner investigates properties of graphs through degree sequences, clustering coefficients, and dynamic monopolies, particularly in specialized graph classes like forests and graphs without short cycles. Analysis of his 2015-2018 publications reveals consistent emphasis on establishing bounds and extremal values for graph invariants. His work bridges theoretical combinatorics with applications in network science, frequently collaborating with Dieter Rautenbach and other researchers in the field. Scientific Awards No scientific awards or honors were mentioned in the provided information. Advising and Grants No details regarding student supervision, PhD advisees, or research grants were found in the source material. Labs and Teams Gentner operates within Ulm University's Institute of Optimization and Operations Research, though specific laboratory structures or research team compositions are not detailed in the available text.

Andre PittigView profile

Professor

• Approach-Avoidance Decision Conflicts
• Fear and Anxiety Mechanisms
• Experimental Psychopathology
• +5 more

Andre Pittig is a Professor for Translational Psychotherapy at the Georg-Elias-Mueller-Institut of Psychology, Georg-August-University of Goettingen, Germany, where he serves as Head of the Department of Translational Psychotherapy since April 2023. Previously, he held professorship positions at FAU Erlangen-Nürnberg (2021-2023) and led an independent research group at the University of Würzburg (2018-2022). Dr. Pittig's educational background includes: Diploma in Psychology (Master equivalent) with Honors from Bielefeld University (2008) PhD from the University of Mannheim (2009-2013) Visiting Researcher at UCLA (2009-2011) Licensed psychological psychotherapist (Approbation) since 2017 Dr. Pittig's research focuses on the mechanisms, psychopathology, and modification of approach-avoidance-decision conflicts, with particular emphasis on learning and unlearning of fear, anxiety, and defensive behaviors. His work bridges experimental psychopathology and psychotherapy, translating basic research findings into practical psychological interventions. He is particularly interested in optimizing and disseminating evidence-based psychotherapy approaches, with a strong commitment to open science practices. His recent publications demonstrate a consistent focus on understanding the neural and behavioral mechanisms underlying anxiety disorders and developing more effective exposure-based treatments. His work employs diverse methodologies including network analysis, fMRI, computational modeling, and large-scale clinical trials. A significant portion of his research examines how individuals process threats, make approach-avoidance decisions, and how these processes can be modified through therapeutic interventions. Dr. Pittig has received numerous scientific awards recognizing his contributions: Supervisor Award (1st place) for outstanding doctoral supervision (2024) Excellence in Research Award (2020) Golden FIPSI for "Best seminar" (2018, 2019) Associate Editorial Board Membership Award for Behaviour Research and Therapy (2016) Advancement Award for Outstanding Young Researchers (2014) As a supervisor and mentor , Dr. Pittig has demonstrated exceptional commitment to doctoral training, as evidenced by his 2024 Supervisor Award. He coordinates research consortiums including the "Providing Tools for Effective Care and Treatment of Anxiety Disorders" (PROTECT-AD). His work has been supported by prestigious funding bodies including the German National Academic Foundation and DAAD. Dr. Pittig leads the Translational Psychotherapy research team at Georg-August-University of Goettingen, which includes researchers such as Sarah Aschoff, Ece Atik, Thomas Borchert, Katharina Hutterer, Caroline im Brahm, Roxana Pittig, Sophie Thaon de Saint Andre, Lena Waltemate, and Hendrika Wiedemann. The team focuses on bridging basic research on fear and anxiety with clinical applications in psychotherapy.

Stefan KratschView profile

Professor

• Parameterized complexity
• Efficient preprocessing
• Computational complexity

Stefan Kratsch is a Professor of Theoretical Computer Science at the Institute of Computer Science, Humboldt University of Berlin. He holds this position since September 2017 and has previously held academic roles at the University of Bonn (2015–2017) and Technical University Berlin (2012–2014). His research focuses on parameterized complexity, efficient preprocessing, and computational complexity, with a strong emphasis on theoretical foundations and algorithm design. University: Humboldt University of Berlin Department: Institute of Computer Science (Algorithm Engineering group) Academic Rank: Professor Email: stefan.kratsch@hu-berlin.de, kratsch@informatik.hu-berlin.de Kratsch’s recent publications highlight his work on advanced algorithmic techniques for graph problems. Key areas include kernelization methods, flow-augmentation for connectivity problems, and tight complexity bounds for classical problems parameterized by structural measures like clique-width and cutwidth. His theoretical contributions aim to bridge preprocessing efficiency with computational hardness. Stefan actively engages in academic service, including organizing workshops and serving on program committees for leading conferences such as IPEC, LATIN, and SWAT. He has reviewed for numerous journals and grant panels, including the European Research Council and German Research Foundation. Advising: He supervised PhD candidate Michael Piechotta, whose defense is scheduled for September 2025.

Sharat IbrahimpurView profile

Researcher

• Combinatorial Optimization
• Approximation Algorithms
• Stochastic Optimization
• +4 more

Sharat Ibrahimpur is a Postdoctoral Researcher at the Research Institute for Discrete Mathematics (University of Bonn, Germany). His research focuses on approximation algorithms for combinatorial optimization problems, with recent emphasis on stochastic optimization in network design, scheduling, and caching systems. Previously, he held postdoctoral positions at the London School of Economics (Mathematics Department) and Google Research (Discrete Algorithms Group). Education: PhD and M.Math in Combinatorics and Optimization (University of Waterloo), B.Sc. in Applied Mathematics (IIT Roorkee) Advisor: Chaitanya Swamy (University of Waterloo) Research interests span network design , load balancing , caching , and stochastic optimization . His publications in venues like IPCO , ICALP , and MathProg demonstrate expertise in primal-dual methods, independent rounding, and uncrossable functions. Key collaborations include Vera Traub, László Végh, and Manish Purohit.

Pascal MaillardView profile

Professor

• Probability Theory
• Mathematical Physics
• Statistical Mechanics
• +3 more

Pascal Maillard is a Professor at the Department of Mathematics at Université Toulouse III - Paul Sabatier, affiliated with the Institut de Mathématiques de Toulouse (CNRS UMR5219). He has been a Junior member of the Institut Universitaire de France since October 2021. His research focuses on probability theory, particularly branching random walks, multiplicative cascades, and random energy models, with applications in statistical mechanics and mathematical physics. Maillard has coordinated the ANR-DFG funded project REMECO (2021-2024), investigating extreme value distributions, partition functions at complex temperatures, and optimization algorithms in random energy models. He has also co-organized the annual 'Les probabilités de demain' conference (2016–2019) to support early-career researchers in probability. His teaching spans advanced modules in stochastic modeling, probability theory, and mathematical statistics at both undergraduate and graduate levels. He is currently developing lecture notes on branching random walks and multiplicative cascades based on his Master's course at Université Paris-Sud. Research Interests: Extremal processes in branching systems, random energy landscapes, stochastic optimization, and applications to statistical physics. Awards: Junior member of Institut Universitaire de France (2021–present). Advising: Supervised three PhD students and multiple research projects in probability theory, including studies on SLE, Markov chain mixing times, and Erdős-Rényi graphs. Labs/Teams: Co-lead of REMECO project with Lisa Hartung, involving institutions in Toulouse and Mainz.