Nicolas Chopin is a Professor of Data Sciences at ENSAE, Institut Polytechnique de Paris. He joined ENSAE in 2006 after serving as a lecturer at the University of Bristol (2003-2006). He holds a PhD from Université Paris VI (2003) and an HDR (habilitation) earned in 2010. His research centers on Bayesian computation methodologies, including: Sequential Monte Carlo (particle filters) Markov chain Monte Carlo Variational inference Probabilistic Machine Learning He develops computational frameworks for complex statistical inference problems. Analysis of his recent publications (2022-2025) reveals strong emphasis on: Monte Carlo innovations (e.g., waste-free SMC, quasi-Monte Carlo), scalable Bayesian modeling, debiasing techniques for sequential inference, and applications in optimization/bandit problems. Theoretical rigor combined with computational efficiency is a consistent theme. Awards: Savage Award for Best Doctoral Dissertation in Bayesian Statistics (2002)
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.
Woody Powell is Jacks Family Professor of Education and Professor (by courtesy) of Sociology, Organizational Behavior, Management Science and Engineering, and Communication at Stanford University . He serves as Faculty Co-Director of the Stanford Center on Philanthropy and Civil Society (PACS) since 2006 and leads its Civic Life of Cities Lab, studying civil society organizations across global cities. He has been recognized with the 2019 School of Humanities and Sciences Dean's Award for Excellence in Graduate Teaching and is a member of prestigious academies like the American Academy of Arts and Sciences and The British Academy. Education : PhD in Sociology (SUNY-Stony Brook, 1978), MA in Sociology (SUNY-Stony Brook, 1975), BA in Sociology (Florida State University, 1971) His research focuses on organizational theory and network analysis , particularly how ideas and practices transfer across organizations. He investigates institutional isomorphism , knowledge networks , and civil society dynamics , with a strong emphasis on the role of networks in enabling or constraining innovation. Recent publications explore themes like the evolution from bureaucratic to open organizational forms, the legacy of institutional theory, and the impact of precarious labor. His seminal works, such as the 1990 article on network forms of organization and the 1983 paper on institutional isomorphism, remain foundational in organizational sociology. Scientific Awards : Max Weber Award (1990), Viviana Zelizer Prize (2005), American Sociological Review's most cited article (1983), and honorary degrees from Uppsala University and Copenhagen Business School. He mentors doctoral students and postdoctoral fellows, including Priyam Saraf, Nick Sherefkin, and Tuomas Vesterinen. As director of SCANCOR and co-founder of PACS, he has shaped academic networks and civic research initiatives.
John M. Wallace, Jr. serves as Vice Provost for Faculty Diversity and Development, Interim Chair of the Department of Africana Studies, and David E. Epperson Endowed Chair and Professor at the University of Pittsburgh. He holds joint appointments in the School of Social Work, Katz Graduate School of Business, and Dietrich School of Arts and Sciences (Sociology), focusing on African American community well-being through community-based participatory research and social entrepreneurship. Dr. Wallace earned his AB in Sociology from the University of Chicago and MA/PhD in Sociology from the University of Michigan. His academic journey spans 30 years of bridging scholarly research with community action in Pittsburgh's Homewood neighborhood. His research centers on adolescent problem behaviors, comprehensive community initiatives, faith-based revitalization, and ESTREAM (entrepreneurship, science, technology, engineering, agriculture, math) education. He pioneers social enterprises addressing food insecurity and youth unemployment through community-academic partnerships, emphasizing culturally responsive interventions for economically disadvantaged populations. Recent publications reveal consistent focus on health disparities (asthma, obesity, tobacco use), neighborhood effects on health, and community-driven solutions. His work employs mixed-methods approaches to evaluate interventions while centering African American experiences and community voice in research design. Dr. Wallace's accolades include: Fellow of the American Academy of Social Work and Social Welfare (2018) BMe Leadership Award (2017) Martin Luther King Distinguished Individual Leadership Award (2017) Racial Justice Award from YWCA Pittsburgh (2016) Chancellor’s Distinguished Public Service Award (2015) Marilyn J. Gittell Activist Scholar Award (2012) As principal investigator for major grants including Comm-Univer-City of Pittsburgh (University of Pittsburgh), Healthy Living Healthy Learning Healthy Lives (NIMHD), and PACS (Richard King Mellon Foundation), he mobilizes university resources for two-generation interventions. Funded by NIH, Heinz Endowments, and Pittsburgh Foundation, his work integrates research, teaching, and community service through Homewood Children's Village and Operation Better Block. He co-founded and leads Homewood Children's Village (community development), serves as board president of Operation Better Block (community organizing), and founded The Oasis Project (economic development division of Bible Center Church, where he is senior pastor). These interconnected initiatives form an ecosystem addressing youth development, food security, and economic empowerment through faith-based community partnerships.
Jaron Skovsted Gundersen is a Research Assistant at the Department of Electronic Systems, within The Technical Faculty of IT and Design at Aalborg University, Denmark. He is actively involved in the Automation & Control group and the Learning and Decisions Lab, focusing on privacy-preserving distributed systems, quantum coding, and decentralized control for infrastructure resilience. His research centers on advanced topics in secure computation and machine learning, including privacy-preserving distributed consensus , secure multi-party computation using Shamir secret sharing , federated learning , and quantum stabilizer codes . His work integrates theoretical foundations with practical applications in critical systems such as water and power distribution networks. The trend in his publications shows a strong emphasis on data privacy in distributed machine learning , leveraging techniques like subspace perturbation and differential quantization. His recent articles span high-impact journals such as IEEE Transactions on Information Forensics and Security and IEEE Journal on Selected Areas in Information Theory, reflecting contributions to both theoretical and applied aspects of information security and control systems. He has been a project participant in the SWIFT research initiative (2019–2024), which investigates decentralized control solutions for electric and water distribution systems. His activities include multiple conference presentations, participation in academic workshops, and public engagement through events like the PDJF Grundfos Prize 'The Stars of Tomorrow' EXPO. He also delivered a lecture on technological solutions in water technology at a national climate meeting in 2022. PhD graduate (March 2021) Active researcher in privacy-preserving machine learning and quantum coding Contributor to resilient infrastructure control systems Regular participant in international conferences and workshops Gundersen is affiliated with the Learning and Decisions Lab at Aalborg University, where he collaborates on cutting-edge research in distributed intelligence, secure computation, and adaptive control systems. The lab fosters interdisciplinary work combining control theory, information theory, and machine learning for real-world applications.
Peter Grünwald is a Full Professor of Statistical Learning at Leiden University's Mathematical Institute and heads the Machine Learning group at CWI (Centrum Wiskunde & Informatica) in Amsterdam. He serves as President of the Association for Computational Learning and has held editorial roles at Foundations and Trends in Machine Learning. His research bridges machine learning theory and statistical methodology. Research Interests Safe Learning: Ensuring robustness in statistical inference and machine learning Safe Probability: Developing probability frameworks for partial domain modeling Replicability Crisis Mitigation: Addressing statistical misinterpretations in applied sciences Learning Bounds: Quantifying data requirements for reliable conclusions MDL Principle: Advancing Minimum Description Length methodology PAC-Bayesian Methods: Bridging frequentist and Bayesian paradigms Scientific Awards Van Dantzig Prize (2010) - Highest Dutch award in statistics IMS Fellow - Recognition from the Institute of Mathematical Statistics Grants NWO TOP-1 Grant (2016) NWO VICI Grant (2010) NWO VIDI Grant (2005)
Dr. Barbara Piškur is a senior researcher at the Research Center Autonomy and Participation, and a Senior Lecturer at the Occupational Therapy Department at Zuyd University of Applied Sciences in the Netherlands. She focuses on enabling participation for children and youth with developmental conditions, particularly through environmental assessment tools like CAPE, PAC, PEM-CY, and COPM. Her work emphasizes user involvement in research and international collaboration through NetChild and CanChild. She is contactable at barbara.piskur@zuyd.nl.
M.H.M. Winands is a Professor in Machine Reasoning and Chair of the Department of Advanced Computing Sciences at Maastricht University's Faculty of Science and Engineering. He received his PhD in Artificial Intelligence from Maastricht University in 2004. His research focuses on heuristic search algorithms, Monte-Carlo Tree Search (MCTS), and artificial intelligence in games, with significant contributions to General Game Playing and adaptive search methods. Winands leads research on: Monte-Carlo Tree Search variants and hybridization Online learning of search-control knowledge Game-solving algorithms (Proof-Number Search) Self-adaptive AI systems for games His work has been applied to diverse domains including board games (Lines of Action, Clobber), video games (StarCraft, Ms. Pac-Man), and structural engineering. Analysis of his publications shows strong emphasis on: Enhancing MCTS robustness through parameter randomization Developing hybrid algorithms combining MCTS with classical search Creating adaptive systems for general game playing Hardware acceleration of game AI components Awards include: ChessBase Best-Publication Award (2004, 2008) Multiple Computer Olympiad wins for game-playing programs He has supervised 3 PhD students and 22 MSc students, with research supported by NWO grants (GoGeneral, Go4Nature). Leads the DKE Games and AI group and serves as Editor-in-Chief of the ICGA Journal.
Melih Kandemir is an Associate Professor of Machine Learning at the University of Southern Denmark, Department of Mathematics and Computer Science. He earned his PhD in 2013 from Aalto University under Prof. Samuel Kaski, followed by postdoctoral work at Heidelberg University (with Prof. Fred Hamprecht) and an assistant professorship at Ozyegin University, Turkey. Prior to joining SDU, he led research at Bosch Center for Artificial Intelligence. Education: PhD (Aalto University, 2013), Postdoc (Heidelberg University). Previous Roles: Assistant Professor (Ozyegin University), Research Group Leader (Bosch CAI). His research focuses on Bayesian inference, stochastic process modeling with deep neural networks, and applications to reinforcement learning and continual learning. He leads the SDU Adaptive Intelligence (ADIN) Lab and is an ELLIS Member, reflecting his standing as a top European AI researcher. His work addresses critical challenges in uncertainty quantification, exploration strategies, and theoretically grounded algorithms for decision-making systems. Recent publications highlight expertise in model-based reinforcement learning (e.g., MOMBO for offline RL), PAC-Bayesian bandits, evidential learning for robust classification, and neural stochastic differential equations. His scientific awards include a Best Paper Award (2017) and ELLIS Membership. He also explores interdisciplinary applications of natural sciences to sustainable technology development.
Mario Marchand is a retired Professor in the Department of Computer Science and Software Engineering at Université Laval, Canada. His research focuses on machine learning theory, explainable AI, and computational biology. He has contributed to foundational work in PAC-Bayesian analysis, generalization bounds, and kernel methods. Marchand is affiliated with the Computer Vision and Systems Laboratory and the GRAAL research group. Though no longer supervising graduate students, his recent work addresses challenges in algorithmic stability, meta-learning, and feature attribution consensus. His publications span topics from neural network optimization to applications in bioinformatics and drug discovery. Research Interests: Machine Learning Theory, Explainable AI, Bioinformatics, Kernel Methods, Domain Adaptation, and Statistical Learning. Key Contributions: PAC-Bayesian risk bounds, decision tree analysis, and algorithms for mixture models. His work bridges theoretical guarantees and practical applications in healthcare and computational biology. Professional Activities: Taught courses including IFT-7002 (Foundations of Machine Learning) and contributed to international conferences. His methods are used in biomarker discovery and peptide design for drug development. Recent trends in his articles emphasize explainable AI (XAI) and resolving feature attribution disagreements, alongside foundational studies on generalization in meta-learning and multi-source domain adaptation. His collaborative projects include predicting molecular properties and advancing neural network stability.
Joshua Moerman is an Assistant Professor in Computer Science at the Open University of the Netherlands, affiliated with the Faculty of Beta Sciences. He holds a PhD from Radboud University (Nijmegen) under supervisors Frits Vaandrager, Bas Terwijn, and Alexandra Silva, focusing on nominal techniques and black-box testing for automata learning. His research spans formal verification, PAC learning, coalgebraic methods, and category theory. Education: PhD in Computer Science (2015-2019), MSc Mathematics (Radboud University, 2013-2015), BSc Double Major in Mathematics and Computer Science (Radboud University, 2009-2013). His research interests emphasize automata learning, particularly for nondeterministic systems, and applications in formal verification. Recent work includes contributions to weighted register automata and compositional learning techniques. He co-organized the LearnAut 2024 workshop and serves on the RP’25 program committee. His PhD student collaborated on automata learning papers, and he teaches logic in the AI master’s premaster program at the Open University.
Ricky R Savjani is an Assistant Professor-in-residence in the Department of Radiation Oncology at the University of California Los Angeles (UCLA), David Geffen School of Medicine. He clinically treats patients with Head & Neck cancers, as well as Brain and Spine metastases. He leads a research laboratory focused on advanced imaging, clinical informatics, and artificial intelligence applications in radiation oncology. Dr. Savjani's research interests span radiation oncology, medical imaging, and artificial intelligence in healthcare. His work focuses on translating neuroimaging advances to capture cancer tissue properties and guide radiotherapy. He has developed innovative frameworks for sharing radiation dose distribution maps in electronic medical records (Push2PACS project published in Radiology: Imaging Cancer) and works with industry partners including Varian and NVIDIA. His research integrates machine learning models into clinical radiation oncology practice, with emphasis on image registration, motion management, and tumor segmentation. His recent publications demonstrate strong trends in applying AI and advanced imaging techniques to radiation oncology challenges, particularly in head and neck cancer, brain metastases, and motion management. The research spans from clinical implementation of novel imaging techniques like 5DCT to developing machine learning models for treatment prediction and optimization. His work bridges the gap between advanced computational techniques and practical clinical applications in radiation therapy. ASTRO-Varian Research Training Fellowship (2020-2021) Trainee Editorial Board of Radiology: Imaging Cancer Dr. Savjani mentors a diverse research team including postdoctoral fellows, graduate students, medical students, and research staff. His lab has developed computational infrastructure with multiple GPU servers (totaling 14 GPUs, 256 CPU cores, 3086 GB RAM, 376 TB storage) to support AI research in radiation oncology. Current projects include functional MRI neuroscience, resting-state fMRI, radiation therapy dose distribution sharing (Push RT 2 PACS), and saccadotopy functional mapping.
Hsiao-Chun Wu is a Professor and holder of the Michel B. Voorhies Professorship at Louisiana State University (LSU), affiliated with the Division of Electrical & Computer Engineering within the School of Electrical Engineering and Computer Science. He earned his Ph.D. in 1999 from the University of Florida. His research focuses on statistical learning, embedded algorithms, digital signal processing, and wireless communications. Key areas include optimization techniques for detection and estimation, image/speech processing, and networked systems design. He has contributed to advancements in sensor deployment strategies, tensor-based signal processing, and machine learning applications for posture recognition and multimedia classification. Notable technical interests span computational photography, robust multichannel decorrelation, and energy-efficient data collection in wireless sensor networks. His work often integrates tensor analysis and graph convolutional networks to address challenges in high-dimensional data processing. Dr. Wu's academic contributions include pioneering methods for indoor line-of-sight coverage optimization and developing novel modulation recognition techniques. His research also addresses industrial IoT performance optimization and biomedical applications such as noninvasive activity recognition using mmWave radar.
David McAllester is a Professor at the Toyota Technological Institute at Chicago (TTIC) and holds a part-time Professor position at the University of Chicago's Department of Computer Science. He earned his B.S., M.S., and Ph.D. from MIT (1978, 1979, 1987). His research spans Artificial Intelligence, Machine Learning, and Theoretical Computer Science , with notable contributions to automated theorem proving (Ontic system), reinforcement learning, probabilistic programming, and computer vision. He is a Fellow of AAAI (since 1997) and has received multiple test-of-time awards for seminal papers in AI planning, constraint solving, and computer vision. Key Contributions: Developed the Ontic verification system for mathematical proofs. Pioneered conspiracy numbers in game tree search (influenced Deep Blue). Co-authored foundational work on policy gradient methods in reinforcement learning. Advanced PAC-Bayesian learning theory and co-training methods. Teaching: Teaches TTIC31230 (Fundamentals of Deep Learning), emphasizing mathematical rigor and research skills in computer vision, NLP, and reinforcement learning. Labs/Teams: Co-founded TTIC's research initiatives in AI and machine learning. Collaborates with industry and academia on foundational AI challenges. Awards: AAAI Fellow (1997) Test-of-Time Awards (AAAI, ICLP, CVPR)
Andrea Paudice is an Assistant Professor in the Department of Computer Science at Aarhus University. Their primary affiliation is with the Department of Computer Science, located at Åbogade 34, Building 5335, Room 317 in Aarhus N, Denmark. Paudice's research focuses on theoretical and algorithmic aspects of machine learning, optimization under uncertainty, adversarial robustness, and clustering methods. Research interests include developing robust statistical learning frameworks for heavy-tailed distributions, designing optimization algorithms with provable guarantees in stochastic settings, and exploring active learning strategies for efficient label usage. Recent work emphasizes high-probability bounds for stochastic methods, median-of-means techniques, and zeroth-order optimization under budget constraints. Publications span topics like adversarial noise mitigation, margin-based active learning, and exact cluster recovery via oracle queries. While no specific grants or awards are listed, their work demonstrates contributions to foundational machine learning theory and algorithmic robustness. No lab affiliations or student advising information is included in the provided text.
