Padakandla Arun is a Professor in the Communication Systems department at EURECOM. His research focuses on quantum information theory, network communication, and coding theory, with recent work on multi-terminal quantum channels and machine learning applications in quantum measurement systems. Affiliation: EURECOM - Communication Systems Academic Rank: Professor Research Interests Prof. Padakandla investigates quantum communication protocols, classical-quantum hybrid systems, and structured code design for multi-user channels. His work bridges information theory with quantum mechanics, emphasizing achievable rate regions and measurement simulation techniques. Scientific Contributions Key trends in his publications include: Quantum MAC and interference channel analysis PAC learning frameworks for POVM hypothesis classes Algebraic structures in network information theory Contact Email: Arun.Padakandla@eurecom.fr
Ronald Ortner is a Professor and Chair of Information Technology, leading research in reinforcement learning, Markov decision processes, and computational learning theory. His work emphasizes theoretical foundations and practical applications in autonomous systems and optimization. He has published extensively since 2004, with notable contributions to bandit algorithms, regret analysis, and exploration strategies in dynamic environments. Research Focus: Reinforcement Learning, Markov Processes, Optimization Key Contributions: Regret bounds in MDPs, adaptive algorithms, transfer learning quantification Ortner engages in academic activities such as conference presentations and peer reviews, focusing on advancing algorithmic approaches in AI and machine learning. His research spans interdisciplinary areas including robotics, energy systems, and probabilistic modeling.
Shibashis Guha is an Associate Professor (Reader) at the School of Technology and Computer Science, Tata Institute of Fundamental Research (TIFR), where he conducts research and teaches in formal methods, logic, automata theory, and verification. His work focuses on reactive controller synthesis, probabilistic systems, timed automata, and the integration of machine learning with formal verification. Institution: Tata Institute of Fundamental Research School: School of Technology and Computer Science Department: Department of Computer Science Academic Rank: Associate Professor His research interests span formal methods, logic in computer science, automata theory, probabilistic systems, algorithmic game theory, and reinforcement learning. He investigates the synthesis of reactive controllers, behavioral equivalences, and the application of learning in verification. His work often intersects with infinite games and descriptive complexity. The recent publications highlight a strong trend in stochastic games, mean-payoff objectives, window properties, probabilistic model checking, and the synthesis of controllers under logical specifications. There is a clear emphasis on bridging formal methods with learning, especially in continuous-time and probabilistic settings. Scientific Awards: Best paper award at MFCS 2021 Guha advises several students and collaborates widely across institutions. His research is funded by the DST-SERB project on zero-sum and nonzero-sum games for controller synthesis. He has served on program committees for major conferences including CAV, ATVA, CONCUR, LICS, and VMCAI, and has organized workshops such as iVerif. He teaches advanced graduate courses like Automata and Computability, Descriptive Complexity, and Automata, Verification, and Infinite Games. He is actively involved in the research community, delivering invited talks at institutions like IST Austria, ENS Paris-Saclay, and Indian Statistical Institute. He also leads seminar series and participates in panels on AI and verification.
Madhav Marathe is a tenured Professor of Computer Science and the Distinguished Professor in Biocomplexity at the University of Virginia, where he also serves as Executive Director of the Biocomplexity Institute. He has held leadership roles at Virginia Tech and Los Alamos National Laboratory, and his work is deeply rooted in transdisciplinary team science. His research spans a wide range of domains including network science, artificial intelligence, computational epidemiology, high-performance computing, and complex systems. He develops foundational methods to model, analyze, and control large-scale biological, information, social, and technical (BIST) systems. His work integrates theoretical computer science with practical applications in public health, disaster response, and infrastructure resilience. The recent publications reflect a strong trend toward data-driven modeling of societal challenges—especially in pandemic response, forced migration, and energy systems. His team leverages agent-based simulations, machine learning, and high-performance computing to create scalable, policy-relevant models that support real-world decision-making. Fellow, American Association for the Advancement of Science (AAAS) Fellow, Association for Computing Machinery (ACM) Fellow, Institute of Electrical and Electronics Engineers (IEEE) Fellow, Society for Industrial and Applied Mathematics (SIAM) Distinguished Researcher Award, University of Virginia (2023) Honorary Doctoral Degree, Chalmers University (2023) Best Paper Award, SIGKDD 2021 (Applied Data Science) Endowed Distinguished Professor of Biocomplexity (2019) Dean’s Award for Excellence in Research, Virginia Tech (2018) Constellation Group’s Supernova Award (2016) Dr. Marathe has mentored over 30 doctoral students, 20+ MS students, and 15 postdoctoral fellows, and has led major federally funded projects including those related to computational epidemiology and national security. His lab, the Biocomplexity Institute, develops high-performance computing services and data analytics platforms for policymakers and emergency planners. He is also involved in initiatives such as the National Security Data and Policy Institute and the Expeditions in Global Pervasive Computational Epidemiology.
Dr. John G. Park is a Research Fellow at the Mayo Clinic College of Medicine and Science, specializing in Pulmonary Medicine , Sleep Medicine , and Critical Care . His work bridges clinical practice and research at Mayo Clinic's Rochester, Minnesota campus. Education : MD from Loyola University (1994), Residency at Cedars-Sinai (1997), Fellowship in Pulmonary/Critical Care (2002) and Sleep Medicine (2003) Board Certifications : Pulmonary Disease, Critical Care Medicine, Sleep Medicine (2009) Research spans critical care outcomes , ventilator-induced lung injury , and Pneumocystis carinii lifecycle . Publications focus on cardiogenic shock epidemiology , PAC utility , and congenital heart disease in ICU . Awards include DOM Humanitarian Award (2024), Internal Medicine Teaching Recognition (2023), and multiple Mayo Clinic Education Honors (2010-2023). Professional leadership roles: Chair of Safety IPE Workgroup , Executive Member of Global Critical Care Collaboration , and Education Committee member at American Academy of Sleep Medicine .
Ann Skinner is a Research Scientist at Duke University, affiliated with the Parenting Across Cultures (PAC) and C-StARR programs. Her research examines how stressful community, familial, and interpersonal events affect parent-child relationships and youth behavioral development, including aggression and internalizing symptoms. She has extensive experience in data management for multisite projects and supervising school- and community-based interventions. Ph.D. in Developmental Psychology, University of Gothenburg, Sweden M.A. in Education, College of William and Mary B.A. in Psychology, College of William and Mary, with focus on special education Skinner’s research spans cross-cultural longitudinal studies on topics like COVID-19’s impact on adolescent development , parenting behaviors , and mental health outcomes . She currently serves as a 2022-23 fellow with the ICDSS COVID-19 Global Scholars Program and has led grants focused on adolescent adjustment and AI-related developmental factors . Her recent publications analyze primal world beliefs , executive function development , and pandemic-related behavioral disruptions across 9–12 nations. She previously worked as a special education teacher and supervisor in North Carolina and Rhode Island.
Christian Igel is a Professor at the Department of Computer Science (DIKU) and Director of the SCIENCE AI Centre at the University of Copenhagen. His academic journey includes a Computer Science degree from the Technical University of Dortmund, a Doctoral degree from Bielefeld University, and a Habilitation degree from Ruhr-University Bochum. He has held academic positions since 2003, including a W1 Professorship at Ruhr-University Bochum before joining DIKU in 2010 as a Professor with Special Duties in Machine Learning and becoming a Full Professor in 2014. Research Interests: Deep learning, kernel methods, evolutionary optimization, reinforcement learning, PAC-Bayesian analysis, and ML applications for sustainability. Affiliations: SCIENCE AI Centre (Director), European Lab for Learning and Intelligent Systems (ELLIS Fellow). Editorial Roles: Editor of KI – Künstliche Intelligenz , Associate Editor of Evolutionary Computation Journal and Artificial Intelligence Journal . Education: Technical University of Dortmund (Computer Science), Bielefeld University (Doctorate), Ruhr-University Bochum (Habilitation).
Tom Hanika is a Visiting Professor (W2) at the University of Hildesheim since April 2023, having previously been affiliated with the University of Kassel in the Department of Electrical Engineering/Computer Science. He also held a visiting professorship at Humboldt University of Berlin until February 2024 and has taught at the University of Würzburg. His academic journey includes interim professorships and lectureships across multiple German institutions. Dr. Hanika's research centers on the theoretical foundations of knowledge discovery in graph data structures with particular emphasis on formal concept analysis. He applies advanced mathematical techniques from algebra, geometric measure theory, topology, and logic to address fundamental challenges in artificial intelligence and machine learning. His work bridges pure mathematics with practical applications in data science, especially focusing on the 'curse of dimensionality' in high-dimensional data spaces. His publication record reveals a strong trajectory in intrinsic dimension analysis, conceptual measurement theory, and knowledge representation in formal contexts. The most recent works demonstrate increasing focus on large-scale geometric learning and the mathematical underpinnings of dimensionality effects in machine learning systems. His research consistently connects theoretical mathematics with practical data science applications. Dr. Hanika serves actively in the academic community as an Editorial Board Member and has chaired program committees for the International Conference on Formal Concept Analysis (ICFCA 2021) and the International Conference on Conceptual Structures (ICCS 2021). He regularly reviews for top journals including Discrete Applied Mathematics and Scientometrics. He leads the 'Dimension Curse Detector' project funded by the LOEWE Exploration program of the State of Hesse, and maintains significant open-source contributions including conexp-clj (a Formal Concept Analysis research tool) and BibSonomy (a scholarly social bookmarking system). These projects serve as both research platforms and community resources for the formal methods and data science communities.
Yair Zick is an Assistant Professor at the Manning College of Information Systems and Computer Sciences (CICS), University of Massachusetts Amherst. Previously, he held positions at the National University of Singapore (NUS School of Computing) and Carnegie Mellon University as a postdoc. His research focuses on algorithmic fairness, game theory, fair division, and explainable AI. He earned his PhD in Mathematics from Nanyang Technological University (2014) and a BSc (Amirim Honors Program) from the Hebrew University of Jerusalem. Educations: PhD in Mathematics, Nanyang Technological University, Singapore (2014) BSc in Mathematics (Amirim Honors Program), Hebrew University of Jerusalem, Israel (2009) His research interests include computational aspects of game theory, fair division, algorithmic transparency, and applying machine learning to economic problems. He teaches courses such as Algorithms, Game Theory, and Fairness, Artificial Intelligence, and Algorithmic Fairness. Key Awards: 2021 IJCAI Early Career Spotlight Award 2020 AAAI Outstanding Senior Program Committee Member Award 2017 Singapore NRF Fellowship 2016 ACM EC Best Paper Award 2014 Victor Lesser IFAAMAS Distinguished Dissertation Award His work bridges theoretical foundations (e.g., Nash equilibria, mechanism design) with practical applications in fair resource allocation and transparent AI systems. He actively contributes to conferences like AAAI, AAMAS, and NeurIPS, and collaborates with institutions globally.
Hessam Mahdavifar is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University's College of Engineering. His research focuses on coding theory, wireless communications, data privacy, and privacy-preserving computing. He holds a PhD from the University of California San Diego (2012), an MS from UCSD (2009), and a BS from Sharif University of Technology (2007). Key research areas include analog subspace coding for non-coherent networks, polar coding for low-capacity channels, and privacy-preserving protocols for distributed learning. His work bridges information theory with practical applications in secure communication and federated learning systems. Notable contributions include analog secret sharing frameworks and coded computing techniques for resilient distributed algorithms. Awards: NSF CAREER Award (2020), Qualcomm Innovation Fellowship (2020), IEEE RFID Best Paper (2015), and International Mathematical Olympiad Silver Medal (2002/2003) Grants: NSF CAREER grant for coding subspace research, collaborative projects on polar coding and coded computing Labs/Teams: Active in Northeastern's Information Theory and Communications group, leading research on secure machine learning and wireless security His recent work emphasizes federated learning security (e.g., LightVeriFL protocols), privacy-preserving distributed algorithms, and code design for emerging communication paradigms like millimeter-wave covert systems.
Hamish Flynn is a Researcher in the Department of Engineering Artificial Intelligence and Machine Learning. His work focuses on developing advanced algorithms for sequential decision-making systems, with a strong emphasis on theoretical guarantees and practical implementations. His research interests span several core areas of machine learning: Bandit algorithms (contextual, linear, and multi-armed variants) PAC-Bayesian theory and applications Reinforcement learning systems Online optimization under uncertainty Statistical learning theory Flynn's recent publications (2022-2025) demonstrate consistent focus on improving sequential learning methods. Key research themes include: non-iid noise modeling in bandits, confidence bound optimization for sequential regression, sparse nonparametric methods, and hardware-efficient machine learning implementations. His work frequently combines theoretical frameworks (PAC-Bayes, martingale theory) with practical applications in reinforcement learning and adaptive systems. No scientific awards, students advised, grant activities, or lab affiliations are mentioned in the available information.
Olivier CATONI serves as a CREST Permanent Member and CNRS Research Fellow in the Statistics department at ENSAE Paris, part of Institut Polytechnique de Paris. His academic profile centers on theoretical statistical research with applications across multiple domains. His research focuses on Statistical Learning Theory , particularly PAC-Bayes bounds which provide probabilistic guarantees for machine learning algorithms, and Statistical models for linguistics which apply quantitative methods to language analysis. His work bridges theoretical statistics with practical machine learning applications. As a CNRS Research Fellow, Dr. Catoni participates in CREST's research activities, contributing to France's national research infrastructure. His position involves conducting independent research, supervising graduate students (though specific advisees aren't listed), and collaborating with the broader academic community through seminars and publications.
Julianne Walsh is a researcher at the University of Hawai‘i at Mānoa 's Center for Pacific Islands Studies. Holding a PhD in Cultural Anthropology (2003), she develops digital educational resources while working with Marshallese communities in Hawaii. Her roles include undergraduate advisor , service-learning coordinator , and curriculum committee chair . PhD, University of Hawai‘i at Mānoa (2003) MA, Louisiana State University (1995) Research focus includes: Marshallese leadership models Compact of Free Association migration Indigenizing education Public anthropology US-Marshall Islands adoptions Recent publications show emphasis on: Pacific Islander digital storytelling Historical narrative construction Gender and environmental policy in Oceania Indigenous education frameworks Teaching roles include: PACS core courses in public policy High-impact pedagogy development Writing-intensive curriculum design
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.
George Deligiannidis is a Professor of Statistics and Director of the MSc in Statistical Science at the University of Oxford's Department of Statistics. He is also a Hugh Price Fellow in Statistics at Jesus College. His academic journey includes degrees from the University of Warwick (MMath), Heriot-Watt University/Edinburgh (MSc in Financial Mathematics), and a PhD from the University of Nottingham. He has held roles at the University of Leicester and King's College London before returning to Oxford in 2017 as Associate Professor, promoted to full Professor in 2024. His research focuses on probability theory, statistical methodology, and their applications in computational statistics and machine learning. Key interests include Monte Carlo methods (especially MCMC), random walks, optimal transport, and diffusion models. Notable recent work explores the theoretical foundations of diffusion models under manifold hypotheses, generalization bounds in machine learning, and convergence analysis of sampling algorithms. Deligiannidis has authored influential papers in top conferences (NeurIPS, ICML, COLT) and journals (Annals of Statistics, JRSSB). He is actively involved in teaching, including Advanced Simulation Methods and Modern Statistical Theory. His work bridges theoretical probability with practical computational challenges, contributing to both methodological advances and foundational understanding in statistical inference.
