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Renaud RaquépasView profile

Researcher

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

Renaud Raquépas is a Courant Instructor in the Mathematics Department at New York University, hosted by Professor Lai-Sang Young. Previously, he was a postdoctoral researcher at CY Cergy Paris Université (Laboratoire AGM), working with Professor Armen Shirikyan. He earned his PhD in 2020 from McGill University and Université Grenoble Alpes under Professors Vojkan Jakšić and Alain Joye. His research focuses on mathematical physics, particularly the time-dependent aspects of statistical mechanics and entropy production in quantum and classical systems. Key tools include probability theory (large deviations, stochastic differential equations), dynamical systems (recurrence, mixing, C*-algebras), and operator theory (spectra, perturbation theory). He co-organizes the Courant Dynamical Systems Seminar, held Wednesdays at the Courant Institute. His work spans entropy production estimation, large deviations, and non-equilibrium statistical mechanics. Collaborations include studies on fermionic systems, quantum measurements, and diffusion processes. His research has been published in journals like Communications in Mathematical Physics and Annales Henri Poincaré. Raquépas has taught courses at NYU, including Ordinary Differential Equations and Linear Algebra, and has extensive teaching experience in both French and English. His academic journey includes a MSc from McGill University and interdisciplinary research spanning Canada and France.

Denis LarocqueView profile

Professor

• Non-parametric statistical methods
• Data mining
• Multidimensional Data Analysis
• +6 more

Denis Larocque is a Full Professor in the Department of Decision Sciences at HEC Montréal, specializing in statistical methods, data mining, and multidimensional data analysis. He serves as Director of the Doctorate in Administration (Ph.D.) program and is affiliated with the GERAD (Group for Research in Decision Analysis) and CIRRELT (Interuniversity Research Center on Enterprise Networks, Logistics and Transport). His academic background includes a M.Sc. and Ph.D. in Statistics from the University of Montreal. His research focuses on non-parametric statistical methods, survival analysis, reinforcement learning applications in transportation systems, and machine learning techniques such as random forests. Recent work includes studies on traffic signal control optimization, causal inference in medical data, and predictive modeling in healthcare. Larocque has supervised over 100 students and projects, including 2 doctoral theses and 4 master’s theses in the last five years. He teaches advanced data science courses and has contributed to interdisciplinary research across transportation, healthcare, and marketing domains. His publications span journals in statistics, transportation engineering, and bioinformatics. He actively collaborates with industry partners, applying statistical and machine learning methodologies to solve real-world problems in sectors such as finance, logistics, and healthcare. His work emphasizes practical applications of data-driven decision-making and methodological advancements in statistical computing.

Laurent CharlinView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +3 more

Roles & Affiliations Associate Professor at HEC Montréal (Department of Decision Sciences) with adjunct ties in Computer Science. Holder of a Canada CIFAR AI Chair. Member of MILA, the Chair Data Science for Real-Time Decision Making, and the Centre de recherche en mathématiques. Education Ph.D. in Computer Science (University of Toronto), MMath (Computer Science, University of Waterloo). Research Interests Focuses on machine learning applications in decision-making, including reinforcement learning for traffic control, energy systems, and recommender systems. Specializes in topics like continual learning, graph-based methods, and operations research integration. Develops foundational models such as collaborative topic Poisson factorization and neural combinatorial optimization approaches. Publications Overview Recent work includes reinforcement learning for sustainable energy use (2025), traffic signal control innovations (2024-2022), and foundational contributions like CTPF (2014). His research bridges theory and application, emphasizing scalable solutions for complex systems. Awards & Recognition Canada CIFAR AI Chair in Artificial Intelligence Advising & Grants Supervised over 30 students across PhD and Master's programs, with notable projects in traffic optimization, electricity pricing, and toxic content analysis. Active in grant-funded research through collaborations with industry partners like Beneva and academic networks like MILA. Labs & Collaborations Core member of MILA and affiliated with HEC's decision science research groups. Leads projects in real-time decision-making and data science applications across domains.

Marc FredetteView profile

Professor

• Analysis of longitudinal data
• Prediction
• Biostatistics
• +7 more

Marc Fredette is a Professor in the Department of Decision Sciences at HEC Montréal. His roles include academic supervision of the Master of Science (MSc) - User Experience in a Business Context and the Short Graduate Program in User Experience. He is also a board member of Tech3Lab, a member of CIRRELT (Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation), and GERAD (Group for Research in Decision Analysis). Education: M.Sc. in Statistics from Université de Montréal and Ph.D. in Statistics from the University of Waterloo. Research interests focus on longitudinal data analysis, biostatistics, Bayesian statistics, and random effects models. His work also explores human factors, UX design, and neuroIS applications, including studies on pedestrian safety, multitasking with IT systems, gamification, and neurophysiological correlates of collaborative tasks. Recent publications emphasize IT-related multitasking effects, gamification in workplaces, and neurophysiological studies of collaborative tasks. He has been recognized for contributions to UX evaluation methodologies and neuroIS research. Supervision includes 3 PhD dissertations and 7 MSc theses, along with 18 supervised projects on data analysis and business applications. His teaching includes courses on database analysis and project-based data analysis. Labs/Teams: Active in Tech3Lab, CIRRELT, and GERAD, focusing on interdisciplinary research in decision sciences and user experience innovation.

Aurélie Bayen-PoissonView profile

Professor

• Political History
• Sociology of the Internet in China
• Sociolinguistics
• +4 more

Aurélie Bayen-Poisson is a tenured Professor and researcher in sociology and sinology at the Catholic University of the West (UCO), based in French Polynesia. She holds dual affiliations with UCO’s Research Laboratory on Psychological and Social Processes (2PS) and the China, Korea, Japan laboratory (CCJ/UMR 8173/EHESS Paris). Her roles include academic leadership as an educational manager for social sciences programs and international educational coordination on UCO’s Papeete campus. Her research focuses on Chinese political history, digital governance, Pacific geopolitics, and sociolinguistics of minority languages in French Polynesia. Key projects include analyzing China’s strategic influence in Oceania, the evolution of Mandarin/Hakka language dynamics, and sustainable governance frameworks for marine resources. She actively collaborates on transnational initiatives like the Coastwatchers 2.0 project studying Chinese interference in the Pacific. Recent publications examine sharp power tactics in Palau, Taiwan’s civic tech models, and French Polynesia’s role in the Indo-Pacific strategic landscape. Her work bridges academic research with policy impact through projects like the Loï Kong Hakka Vah language manual and the Encyclopedia of the Anthropocene as associate editor.

Guillaume CharpiatView profile

Researcher

• Machine Learning
• Deep Learning
• Computer Vision
• +8 more

Guillaume Charpiat is a Research Officer at INRIA Saclay and Head of the Data Science department at the LISN laboratory (Paris-Saclay University). He holds a PhD in computer vision from the École Polytechnique (2006), conducted under Olivier Faugeras and Renaud Keriven. His postdoctoral research at the Max Planck Institute for Biological Cybernetics focused on statistical learning. He joined the TAO/TAU team in 2015, specializing in machine learning and optimization. His research spans deep learning theory, neural networks, and applications in satellite imagery, genetics, fluid dynamics, and medical imaging. Education: PhD in Computer Vision (École Polytechnique, 2006), Postdoc in Statistical Learning (Max Planck Institute). Teaching includes advanced deep learning courses at MVA/CentraleSupélec and AI master's programs. Research interests emphasize neural architecture design, generative models, and interdisciplinary applications. His work bridges machine learning with physics and biology, addressing challenges like dynamical systems modeling and population genetics inference. Key contributions include neural architecture growth strategies, equivariant GNNs for materials science, and deep learning frameworks for genomic data. Advised over 20 students across PhD and master's levels, focusing on topics like neural networks, causal discovery, and generative models. Active in seminars and interdisciplinary collaborations, he co-leads the TAU team's research on machine learning and optimization.

Felix Luis Belzunce TorregrosaView profile

Professor

• Probability Theory
• Statistics
• Operations Research
• +3 more

Félix Belzunce Torregrosa is a Full Professor at the University of Murcia, affiliated with the Department of Statistics and Operational Research within the Faculty of Mathematics. He earned his doctorate from the University of Murcia in 1996 with a thesis on 'Ordering in Dispersion and Concentration of Truncated Distributions.' His research focuses on stochastic orders, reliability theory, actuarial science, and multivariate analysis. He has co-authored over 70 publications in top-tier journals such as Statistics & Probability Letters and Probability in the Engineering and Informational Sciences . Notable contributions include foundational work on stochastic comparisons, hazard rate ordering, and applications of stochastic orders in risk assessment. He has collaborated extensively with researchers like José-María Ruiz, Carolina Martínez-Riquelme, and Franco Pellerey. His work bridges theoretical probability with practical applications in finance, engineering, and actuarial science. Belzunce has led research groups such as 'Characterization, Ordering and Classification of Distributions,' and his studies on multivariate mixture models and nonhomogeneous processes have influenced reliability engineering and insurance mathematics. He has also contributed to developing methodologies for analyzing dependent random variables and optimizing maintenance policies in systems engineering.

Albert ShiryaevView profile

Professor

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

Albert Shiryaev is a leading Russian mathematician currently serving as Professor and Head of the Probability Theory Department at the Moscow State University , Faculty of Mechanics and Mathematics. He has been affiliated with the Steklov Mathematical Institute since 1957 and was a member of the Russian Academy of Sciences (Correspondent member since 1997). Research Interests: A pioneer in probability theory and mathematical statistics , his work focuses on stochastic processes , optimal stopping , sequential analysis , and financial mathematics . His publications span over 170 scientific papers and 9 monographs, including foundational texts on martingales and stochastic calculus. Scientific Awards: Markov Prize (1974) Kolmogorov Prize (1994) Humboldt Research Award (1996) Honoris Causa degrees from Albert Ludwigs University and Amsterdam University Distinguished Professor of Moscow State University (2003) Leadership Roles: Shiryaev has led the Laboratory of Statistics of Stochastic Processes at Steklov Institute (1986–2002) and currently chairs the Probability Theory Department at MSU. He has held editorial leadership in journals like Theory of Probability and its Applications and Finance and Stochastics .

Jérémie GuilhotView profile

Lecturer

• Coxeter Groups
• Hecke Algebras
• Kazhdan-Lusztig Cells
• +3 more

Jérémie Guilhot is a Lecturer at the Institut Denis Poisson within the Faculty of Science and Technology at the University of Tours, France. His research focuses on Coxeter groups, Hecke algebras, and Kazhdan-Lusztig cells in affine Weyl groups with unequal parameters. He is a member of the ANR ACORT project on algebraic combinatorics and leads the regional Madaca project on random walks and Dunkl processes. Key research interests include algebraic structures such as Hecke algebras, combinatorial problems in representation theory, and the interplay between affine Weyl groups and cellular theory. His work often involves collaborations with researchers like Cédric Bonnafe, V. Miemietz, and J. Parkinson. Recent publications explore affine Hecke algebras, asymptotic cells, and proofs of Lusztig’s conjectures in affine types. Member of ANR ACORT: Algebraic Combinatorics Research Network Project Leader for Madaca: Focuses on stochastic processes and combinatorial algebra Publications span journals like Journal of Algebra and Mathematische Zeitschrift His research trends emphasize cell structure analysis in affine Weyl groups, cellular properties of algebras, and combinatorial methods in representation theory. Collaborations highlight interdisciplinary approaches between algebra and stochastic processes.

Russell C. HardieView profile

Professor

• Digital Signal Processing
• Image Processing
• Machine Learning
• +3 more

Russell C. Hardie is a full-time Professor at the University of Dayton , holding positions in the Department of Electrical and Computer Engineering with joint appointments in Electro-Optics and Photonics and Bioengineering . His academic journey began with a B.S. in Engineering Science from Loyola College (1988), followed by M.S. and Ph.D. in Electrical Engineering from the University of Delaware (1990, 1992). Prior to joining the University of Dayton in 1993, he served as a Senior Scientist at Earth Satellite Corporation (now MDA Federal). Research Interests : Digital signal/image processing, medical imaging, super-resolution techniques, hyperspectral/infrared imaging, pattern recognition Key Awards : 2006 Alumni Award in Teaching (University of Dayton) 1998 Rudolf Kingslake Medal (SPIE) 1999 School of Engineering Excellence in Teaching 2002 IEEE Professor of the Year 1997 Epsilon Delta Tau Engineering Professor of the Year Recent Work : Focuses on machine learning applications for medical imaging (lung segmentation, nodule detection), atmospheric turbulence mitigation, and hyperspectral data analysis. His 15 most recent publications span topics from zero-shot chest X-ray analysis to methane plume detection and turbulence-corrected imaging systems. Contact: rhardie1@udayton.edu

Yakov Berchenko-KoganView profile

Assistant Professor

• Numerical Analysis
• Differential Geometry
• Finite Element Methods
• +2 more

Yakov Berchenko-Kogan is an Assistant Professor in the Department of Mathematics and Systems Engineering at Florida Institute of Technology (FIT), part of the College of Engineering and Science. He holds a Ph.D. in Mathematics from MIT (2016) and a B.S. in Mathematics from Caltech (2011), with a minor in Control and Dynamical Systems. His research focuses on developing high-order, structure-preserving numerical methods for problems in differential geometry and geometric analysis, particularly within the framework of finite element exterior calculus. He has held postdoctoral positions at institutions including Pennsylvania State University, the University of Hawaii at Manoa, and Washington University in St. Louis. His expertise spans numerical analysis, differential geometry, and computational mathematics, with a strong emphasis on geometric numerical methods and their applications to geometric PDEs. He actively engages in academic outreach, encouraging undergraduate and graduate students to pursue capstone projects or research collaborations. His work has been published in high-impact journals such as Foundations of Computational Mathematics , Geometriae Dedicata , and Journal of Nonlinear Science . Berchenko-Kogan’s research interests include finite element methods, geometric decomposition techniques, and the numerical approximation of geometric objects like the Levi-Civita connection and mean curvature flow self-shrinkers. He also explores structure-preserving algorithms for equations in mathematical physics, such as Maxwell’s and Yang-Mills equations.

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• Actuarial Mathematics
• Risk Theory
• Ruin Theory
• +5 more

Chadjiconstantinidis Stathis is a Professor at the Department of Statistics and Insurance Science, University of Piraeus. He earned his Doctorate in Statistics (1990) and Diploma in Mathematics (1985) from Aristotle University of Thessaloniki. His academic roles include undergraduate and postgraduate teaching across multiple institutions including the University of Athens and Athens University of Economics and Business. Research Focus: His work spans actuarial mathematics, risk theory, and statistical optimization. Primary domains include: Ruin theory and bankruptcy modeling in insurance contexts Collective risk models with dependencies Optimal experimental designs for statistical efficiency Reliability theory and stochastic pattern analysis Publications: His 15 most recent articles show consistent focus on risk modeling (compound processes, ruin probabilities) and statistical design optimization (D-optimal cyclic designs). Actuarial mathematics dominates newer works (2005–2013), while earlier publications emphasize experimental design combinatorics. Academic Leadership: Department President (2007–2009) Director of Master’s Program in Actuarial Science & Risk Management (2007–2011) Deputy Chairman of Actuary Licensing Committee (Ministry of Finance) Senate Member at University of Piraeus (1997–1998, 2007–2009) Doctoral Supervision: Mentored dissertations on risk theory and statistics pedagogy. Current advisees not specified.

Giorgio StefaniView profile

Assistant Professor

• Analysis of Partial Differential Equations (PDEs)
• Functional Analysis
• Metric Geometry
• +4 more

Giorgio Stefani is a Fixed-Term Researcher (Ricercatore a Tempo Determinato di tipo A - RTDA) at the Department of Mathematics, University of Padova (Italy). He holds a Ph.D. in Mathematics from Scuola Normale Superiore of Pisa (2020) and has held postdoctoral positions at the University of Basel (2020-2022) and SISSA Trieste (2022-2024). Ph.D.: Mathematics, Scuola Normale Superiore di Pisa (2020) PostDoc: University of Basel (2020-2022) PostDoc: SISSA Trieste (2022-2024) Current: Fixed-Term Junior Assistant Professor, University of Padova (2024-) His research focuses on Analysis of PDEs , Functional Analysis , and Metric Geometry , with particular emphasis on: Fractional calculus and non-local functionals Geometric measure theory and perimeter minimization Sharp inequalities in Sobolev spaces Applications to Euler and Vlasov-Poisson systems Topological singularities in variational problems His recent publications demonstrate a strong trend toward Gamma-convergence of fractional energies , Hardy inequalities , and non-local perimeters . He actively participates in geometric analysis seminars and workshops, including the 2022 Rome workshop on anisotropic isoperimetric problems. Contact: giorgio.stefani@unipd.it | giorgio.stefani.math@gmail.com

• Fluid Dynamics
• Partial Differential Equations
• Geometric Measure Theory
• +3 more

Gianluca Crippa is an Associate Professor in the Department of Mathematics and Computer Science at the University of Basel, affiliated with the Analysis research group. His work focuses on differential equations arising from fluid dynamics and geometric measure theory. He contributes to understanding irregular fluid behaviors, such as chaotic flows and advection processes. His research also explores nonlocal conservation laws, mixing phenomena, and transport equations. Crippa organizes the BZ Seminar in Analysis, a collaborative event with the University of Zurich and ETH Zurich. He has published extensively on topics like Euler flows, Navier-Stokes equations, and Vlasov-Poisson systems, with over 50 peer-reviewed articles. His current projects include studies on localized Yudovich spaces and entropy solutions in nonlocal models. Education: Ph.D. in Mathematics (unspecified institution). Research Themes: Fluid dynamics, PDEs, geometric measure theory, advection, and nonlocal phenomena. He collaborates with institutions globally, including SISSA Trieste and ETH Zurich. His work bridges theoretical mathematics and applications in physics and engineering, emphasizing rigorous mathematical analysis of complex systems.