Chantal David is a Professor in the Department of Mathematics and Statistics at Concordia University. Her research focuses on number theory and its intersections with mathematical statistics. Formal Affiliation: Concordia University, Department of Mathematics and Statistics Email: chantal.david@concordia.ca Office: Library Building, LB 927.09 Research Interests revolve around Number Theory , particularly: L-functions and their non-vanishing properties Elliptic curves over finite fields and function fields Statistics of group structures and root numbers Connections to random matrix theory and metaplectic functions Extremal primes and Frobenius distributions Drinfeld modules and supersingular reductions Article Trends show a focus on cubic and quartic L-functions, non-vanishing phenomena, and statistical properties of elliptic curves over finite fields. Recent work explores metaplectic theta functions, extreme value distributions, and one-level density analysis. Labs & Teams : She is affiliated with the Montreal Number Theory Group (CICMA) .
Renjie Feng is a Research Fellow in Mathematics and AI at the School of Mathematics and Statistics and the Sydney Mathematical Research Institute , University of Sydney. His work bridges probability theory, statistics, and applications in machine learning, deep learning, and artificial intelligence. His research interests focus on probability theory and its applications to machine learning , random matrix theory , and statistical physics . He investigates extreme value problems, spectral properties of random matrices, and topological features of random fields over Riemannian manifolds. Recent publications highlight trends in random matrix theory (GUE, GOE, GSE), extreme gap problems , determinantal point processes , and Wiener chaos . Collaborative works with F. Götze, D. Yao, and R. Adler emphasize U-statistics , multivariate linear statistics , and random topology inspired by Poisson point process studies.
Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
Dr. Tarek Sayed is a Professor at the University of British Columbia's Department of Civil Engineering within the Faculty of Applied Science. He specializes in Transportation Engineering, focusing on road safety analysis, traffic operations, and Intelligent Transportation Systems (ITS). He serves as the Director of the Bureau of Intelligent Transportation Systems and Freight Security (BITSAFS-Engineering) and Editor of the Canadian Journal of Civil Engineering . His research addresses three core areas: improving road safety evaluation techniques, enhancing safety through traffic operations and highway design analysis, and advancing ITS technologies. Notable contributions include frameworks for safety-audits of infrastructure projects like the Sea to Sky Highway and methodologies for evaluating transit signal priority systems in Vancouver. He has authored/co-authored over 250 publications and supervised 60 graduate students. Key Roles: Professor, BITSAFS Director, Journal Editor Awards: Tier 1 Canada Research Chair, Wilbur Smith Award, Sandford Fleming Award, Prince Michael International Road Safety Award Consulting: Global projects in traffic safety and ITS for agencies like ICBC, FHWA, and Ashghal His research integrates Bayesian statistical methods, extreme value theory, and machine learning to model traffic conflicts, pedestrian behavior, and safety interventions. Current projects include real-time safety optimization using autonomous vehicle data and analyzing shared space interactions between cyclists and pedestrians. Dr. Sayed chairs national/international committees, including the U.S. Transportation Research Board’s safety data committee. His work bridges academia and practice, influencing policy and infrastructure investment decisions worldwide.
University of Illinois Urbana-ChampaignUnited States
Liming Feng is an Associate Professor at the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, and has served as Director of the Master of Science in Financial Engineering (MSFE) program since 2022. His academic career at the university spans from Assistant Professor (2006-2012) to his current role. He earned his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2006), an M.S. in Mathematics from Northwestern University (2000), and a B.S. in Mathematics from Beijing Normal University (1997). Ph.D., Industrial Engineering and Management Sciences, Northwestern University, 2006 M.S., Mathematics, Northwestern University, 2000 B.S., Mathematics, Beijing Normal University, 1997 Feng’s research focuses on Financial Engineering, Stochastic Modeling, and Computational Methods. He has contributed extensively to quantitative finance, particularly in options pricing, portfolio optimization, and market impact models. His work leverages advanced numerical methods, Fourier transforms, and stochastic calculus to solve complex financial problems. The trends in his publications highlight expertise in Levy processes, jump diffusion models, and numerical algorithms for financial derivatives. He has developed innovative techniques for Bermudan options pricing, discretely monitored barrier options, and portfolio deleveraging strategies. His articles often intersect Operations Research with Financial Engineering, emphasizing computational efficiency and mathematical rigor. ISE Faculty Fellow (2025) INFORMS Financial Services Section Best Student Research Paper (2013) First runner-up of the 2012 Morgan Stanley Prize for Excellence in Financial Markets Feng has served on editorial boards for Operations Research Letters and Mathematical Finance . He has been recognized repeatedly for teaching excellence, including the Sharp Outstanding Teaching Award (2011, 2022) and multiple entries in the List of Teachers Ranked as Excellent by Their Students (2007-2024). He currently leads the MSFE program and contributes to curriculum development through courses like IE 522 (Statistical Methods in Finance) and IE 527 (MSFE Professional Development).
Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.
Prof. Dr. Nina Gantert is a distinguished Professor of Probability Theory at the Technical University of Munich (TUM) , affiliated with the TUM School of Computation, Information and Technology . She has held faculty positions at Karlsruhe Institute of Technology and the University of Münster prior to joining TUM in 2011. Her research focuses on probability theory , particularly stochastic processes , large deviations , and random media . She investigates random walks in random environments as models for transport in disordered systems and explores applications in physics and biology . Recent publications highlight her work on branching random walks , mixing times , biased random walks , and large deviation principles for complex stochastic systems. She has co-authored studies on random walks in dynamical percolation , interacting edge-reinforced processes , and extremal point processes in branching models. Scientific Awards: Elected fellow of the IMS (2016) Her academic career spans institutions including ETH Zürich, University of Bonn, Technical University of Berlin, and TUM. She has supervised numerous Bachelor’s and Master’s theses on topics ranging from mixing time analysis to percolation theory , often collaborating with international co-authors.
Professor Eugene O'Brien serves as Professor of Civil Engineering within the School of Civil Engineering at University College Dublin's College of Engineering and Architecture. His research focuses on critical structural assessment methodologies for long-span bridges, with particular expertise in traffic load modeling and bridge safety evaluation. His research interests center on structural engineering challenges related to bridge infrastructure, specifically traffic load assessment for long-span bridges , structural health monitoring systems , and sustainable infrastructure management . Professor O'Brien pioneered camera-based monitoring techniques to overcome limitations of traditional Weigh-in-Motion sensors during congested traffic conditions, enabling more accurate safety assessments of aging bridge infrastructure. His work addresses the critical gap in quantifying traffic loading on bridges with spans up to 2 kilometers, where conventional methods fail during stop-and-go traffic scenarios. Analysis of his 15 most recent publications reveals a consistent research trajectory focused on probabilistic modeling of traffic loads, with increasing sophistication in handling extreme events and long-term infrastructure performance. His work spans fundamental statistical methods for load effect prediction, practical applications in real-world bridge assessments, and environmental considerations regarding infrastructure carbon footprints. The research demonstrates strong methodological evolution from basic traffic modeling to comprehensive lifetime assessment frameworks incorporating sustainability metrics. As co-founder and director of Roughan O'Donovan's subsidiary Innovative Solutions (ROD-IS), Professor O'Brien has translated research into practice through significant projects including the Malahide Railway Viaduct assessment (2009), EU-funded bridge lifespan simulation tools (2011), vibration reduction systems for bridge cables, and structural assessments for major international projects including the Ting Kau Bridge in Hong Kong, the Forth Road Bridge in Scotland, and the Chacao Channel Bridge in Chile. His consultancy work demonstrates direct application of academic research to critical infrastructure challenges worldwide. Professor O'Brien's research group operates at the intersection of structural engineering and sustainable infrastructure management, with particular emphasis on extending bridge service life through accurate safety assessment. Their work on the Chacao Channel Bridge demonstrates practical implementation of traffic monitoring systems using toll data to manage truck loads, while their environmental impact analysis shows how accurate safety assessments reduce unnecessary bridge replacements, thereby lowering the carbon footprint of transportation infrastructure through extended service life of carbon-intensive materials like concrete and steel.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Joanna Millstein is a Post-doctoral Fellow in Geophysics at the Colorado School of Mines. She earned her Ph.D. in Geophysics from the Massachusetts Institute of Technology in 2023 as part of the MIT-WHOI Joint Program in Oceanography and Engineering, where her dissertation focused on The Flow and Fracture of Antarctic Ice Shelves . She also holds an A.B. in Earth Sciences from Dartmouth College (2017). Her research centers on the deformation and fracture of glacier ice, working at the intersection of fracture mechanics, remote sensing (particularly SAR and InSAR processing), statistical mechanics, and stochastic models. Millstein uses observational data from satellites and field measurements to derive mechanical and statistical models for glacier ice processes, with particular focus on reconciling observations of ice fracture and iceberg calving with theoretical models. Her work aims to resolve the nonlinear physics of glacier ice to better understand future global climate change impacts. Millstein's publication record shows a strong focus on Antarctic ice dynamics, with recent work applying extreme value theory to analyze 47 years of iceberg calving events. Her research demonstrates consistent attention to both theoretical modeling and practical applications for understanding climate change impacts on polar regions. She has developed computational tools including CryoCloud, reflecting her commitment to open science and cloud-based infrastructure for cryosphere research. Her scientific contributions span ice shelf mechanics, fracture prediction, rheology, and statistical modeling of glacial processes. Millstein maintains an active research presence with publications extending to 2025, demonstrating ongoing contributions to the field of glaciology and climate science. She is affiliated with the glaciology research center at Colorado School of Mines and maintains an active GitHub presence with climate-related code repositories. Her work bridges theoretical geophysics with practical climate change impact assessment, particularly regarding ice sheet stability and sea level rise projections.
Assoc Prof Ying Chen is an Associate Professor at the National University of Singapore , affiliated with the Department of Mathematics, Asian Institute of Digital Finance (as Academic Director of PhD Program in Digital FinTech 2022–2024), Risk Management Institute (2019–2023), Department of Statistics and Data Science (2019–2023), and Department of Economics (2018–2023). She also contributes to NUS Graduate School for Integrative Sciences and Engineering since 2016. Research Interests include: AI forecasting and quantum computing for finance Nonstationary time series and functional data analysis Energy data analytics and precision medicine Network autoregression and spatial-temporal modeling Explainable AI and citation metrics Portfolio liquidation and market-making algorithms Article Trends demonstrate expertise in: Adaptive forecasting for gas flows and electricity prices Blockchain network influence detection Quantum computing applications in finance Functional autoregression with mixed predictors Credit rating fairness and explainability High-resolution implied volatility modeling Scientific Awards include: ISI Elected Member (2016–) International Statistical Institute Council (2023–2027) IASC Scientific Secretary (2017–2019, 2023–2025) Advisory roles for EU FIN-TECH and xAIM projects
Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.
Paata Ivanisvili is an Associate Professor at the University of California, Irvine (UCI), Department of Mathematics, School of Physical Sciences. His research focuses on Analysis, Probability, Harmonic Analysis, and Functional Analysis, with a particular emphasis on isoperimetric inequalities, functional inequalities, and discrete structures such as the Hamming cube. He has held visiting positions at institutions including the Hausdorff Research Institute for Mathematics and Princeton University. Ivanisvili has organized conferences such as the Dual Trimester Program at the Hausdorff Institute on Boolean Analysis in Computer Science (2024) and annual Summer/Fall Schools since 2021. He earned his PhD in Mathematics from Michigan State University (2015) and a BS from Saint Petersburg State University (2011). His research interests include sharp inequalities in analysis (e.g., Poincaré, Beckner, Ehrhard), hypercontractivity, and applications to discrete mathematics and probability. He has collaborated with prominent mathematicians such as Fedor Nazarov, Alexander Volberg, and Roman Vershynin. Notable awards include the NSF CAREER Award (2021–2025) and Simons Fellowship in Mathematics (2025–2026). Ivanisvili’s recent work explores the interface between harmonic analysis and discrete mathematics, including studies on additive energies, convex hulls of space curves, and learning theory. His articles frequently address foundational questions in geometric functional analysis, often using tools like Bellman functions and optimal control theory. He actively advises PhD students and has mentored visiting researchers at UCI.
Massachusetts Institute of TechnologyUnited States
Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.
Martin Larsson is a Professor in the Department of Mathematical Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. He holds a Ph.D. from Cornell University and completed a postdoctoral appointment at the Swiss Finance Institute at EPFL, Lausanne, Switzerland. His research focuses on Mathematical Finance, stochastic analysis, probability, and statistics, with emphasis on affine and polynomial processes, stochastic portfolio theory, and sequential statistics. Key research domains include modeling interest rate term structures, large-scale equity market dynamics, and statistical testing in online settings. He serves as the Departmental representative for the Master of Science in Computational Finance (MSCF) program at CMU. Larsson has received the Bruti-Liberati Visiting Fellowship from the University of Technology Sydney. His work bridges theoretical probability with applications in finance, including contributions to stochastic volatility modeling, optimal contracts in trading, and robust portfolio optimization under uncertainty. Publications span topics such as martingale exit times, Wasserstein distance convergence, and ergodic control in stochastic systems, reflecting his interdisciplinary approach to mathematical finance and probability theory. His research often combines analytical techniques with stochastic control and geometric flows.
