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Carolina Osorio is a Professor at HEC Montréal, holding the Scale AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. She is affiliated with the Department of Decision Sciences and is a member of the Group for Research in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Her research focuses on transportation optimization, urban mobility, and data-driven simulation-based methods. She has been recognized among the world’s most influential researchers in 2023 and 2024. Education: Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne (EPFL) M.Sc. in Statistics, University College London (UCL) Bachelor’s in Engineering, École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble (ENSIMAG) Research Interests: Her work emphasizes scalable transportation modeling, simulation-based optimization, and AI applications for urban logistics. She develops methods for large-scale network analysis, traffic demand estimation, and sustainable urban mobility solutions. Key areas include traffic signal optimization, car-sharing service design, and high-dimensional stochastic systems. Publications: Recent articles highlight advancements in scalable traffic demand estimation, Bayesian optimization for transportation systems, and simulation-based toll optimization. Her work addresses challenges in global highway networks, urban congestion dynamics, and multi-city calibration. Awards: Scale AI Research Chair (Artificial Intelligence for Urban Mobility and Logistics) Recognition as a world-leading researcher in transportation science Advising & Grants: Osorio collaborates on projects funded by Scale AI and leads research initiatives through GERAD and CIRRELT. Her supervision activities include teaching courses such as Decision Analysis and Sample Efficient Optimization at HEC Montréal. Labs & Teams: She contributes to interdisciplinary teams at GERAD and CIRRELT, focusing on integrating advanced analytics into urban transportation systems.

• Transportation Science
• Operations Research
• Artificial Intelligence
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Patrick Brown is an Associate Professor at the University of Toronto , affiliated with the Department of Statistical Sciences and cross-appointed to the Centre for Global Health Research and St. Michael's Hospital . His research focuses on spatio-temporal data modeling , Bayesian inference , and non-parametric methods for spatial epidemiology and environmental sciences. Fields of Interest : Spatial Statistics, Cancer Statistics, Statistical Software Education : PhD from University of Lancaster His methodological work encompasses Bayesian inference for non-Gaussian spatial data, Gaussian Markov random fields, and computational techniques like INLA and MRA. Applied research themes include disease mapping, environmental risk assessment, and public health surveillance using real-world data sources such as electronic health records and wastewater monitoring . He has developed key R packages (mapmisc, geostatsp, diseasemapping) supporting spatial statistical applications. Current collaborative projects span diverse fields: Ultra-diffuse galaxy detection with astrophysical applications Multi-pollutant mortality studies in Canadian cities SARS-CoV-2 seropositivity tracking Homelessness population estimation using EHR Geospatial cancer risk tools for Nova Scotia His work bridges statistical innovation with global health challenges , emphasizing computationally efficient solutions for large-scale spatiotemporal datasets.

• Spatial Statistics
• Cancer Statistics
• Statistical Software
• +3 more

Christoph Frei is a Professor and Chair of the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a PhD in mathematical finance from ETH Zurich and previously worked as a researcher at École Polytechnique in Paris. His research focuses on quantitative finance, risk management, and mathematical economics, with applications to algorithmic trading, credit risk, and digital currencies. Education: PhD in Mathematical Finance, ETH Zurich Postdoctoral Researcher, École Polytechnique (Paris) Bachelor/Master in Mathematics, ETH Zurich His work bridges academia and industry through collaborations with institutions like ATB Financial, Canadian Western Bank, and the Federal Reserve System. Key research interests include over-the-counter markets, financial regulation, and machine learning applications in risk prediction. Frei has received notable recognition, including the Best Paper in Asset Pricing Award (2019). Current industry partnerships include AI-driven customer risk prediction projects with ATB Financial and credit risk analysis with Canadian Western Bank. He actively contributes to professional organizations like PRMIA Edmonton and advises on financial technology innovation. Research grants come from NSERC, SSHRC, and Mitacs. His consulting roles have spanned risk modeling at UBS and Credit Suisse, emphasizing practical applications of theoretical frameworks.

• Quantitative Finance
• Risk Management
• Mathematical Economics
• +4 more

Richard Zemel is a Professor in the Department of Computer Science at the University of Toronto, where he has been since 2000. He holds an Industrial Research Chair in Machine Learning and co-founded the Vector Institute for Artificial Intelligence. His research focuses on machine learning, including unsupervised learning, deep learning, and ethical AI, with contributions to probabilistic models, fairness, and representation learning. Zemel has developed influential systems like the Toronto Paper Matching System and holds awards such as the NVIDIA Pioneers of AI Award and multiple NSERC grants. Education: B.Sc. in History & Science from Harvard University (1984), Ph.D. in Computer Science from the University of Toronto (1993). Postdoctoral work at the Salk Institute and Carnegie Mellon University. Research Interests: Machine Learning (unsupervised/deep learning), probabilistic models, fairness in algorithms, computer vision, natural language processing. He emphasizes ethical AI and practical applications like recommendation systems and causal inference. Awards & Affiliations: Fellow of CIFAR, member of the Neural Information Processing Society (NIPS) Executive Board, and advisor to the Creative Destruction Lab. His work is funded by NSERC, CIFAR, Google, Microsoft, and DARPA. Grants & Labs: Active in grants supporting machine learning research, including projects on fairness and invariant learning. Collaborates with industry partners and leads teams at the University of Toronto and Vector Institute.

• Machine Learning
• Deep Learning
• Unsupervised Learning
• +5 more

Radu Craiu is a Professor in the Department of Statistical Sciences at the University of Toronto. Born in Bucharest, Romania, he earned his PhD in Statistics from the University of Chicago in 2001 under the supervision of Xiao-Li Meng. He joined the University of Toronto as an Assistant Professor, was promoted to Associate Professor in 2006, and to Full Professor in 2013. He served as Department Chair from 2018–2022 and 2023–2024. Education: PhD in Statistics (2001), University of Chicago MS in Mathematics (1996), University of Bucharest BS in Mathematics (1995), University of Bucharest His research focuses on computational methods in statistics, particularly Markov chain Monte Carlo (MCMC) algorithms, Bayesian inference, copula models, model selection, and statistical genetics. He has collaborated on interdisciplinary projects in genetics, ecology, astronomy, and demography, emphasizing flexible statistical modeling and efficient computational techniques. Radu Craiu has received prestigious awards, including the CRM-SSC Prize (2016), CJS Best Paper Award (2019), and fellowships from the American Statistical Association (2022), Institute of Mathematical Statistics (2020), and International Statistical Institute (2015). He actively contributes to editorial roles for journals like the IMS Bulletin and Harvard Data Science Review .

• Data Science
• Statistical Computing
• Efficiency of Monte Carlo methods
• +8 more

Franz Huber is an Associate Professor in the Department of Philosophy at the University of Toronto (St. George campus). He holds an MA from the University of Salzburg, Austria, and a PhD from the University of Erfurt, Germany. His primary research areas include Formal Epistemology, Philosophy of Science, Philosophical Logic, and Metaphysics. Huber’s work focuses on belief revision, counterfactual reasoning, and means-end rationality, often employing formal methods to address philosophical problems. His publications include influential books like A Logical Introduction to Probability and Induction (2018) and Belief and Counterfactuals (2021), as well as numerous peer-reviewed articles in journals such as Synthese and Journal of Philosophical Logic . His research explores the foundations of epistemology and metaphysics, particularly through ranking theory and its applications to belief change, counterfactuals, and causal reasoning. Huber has also contributed to the philosophy of science, examining confirmation theory and the logic of scientific reasoning. His recent work emphasizes a means-end approach to philosophical problems, integrating instrumental rationality with normative theory. Huber’s publications demonstrate a commitment to bridging formal methods with traditional philosophical inquiry. His academic contributions include editing volumes on belief revision and co-authoring works on ranking theory. He maintains an active research agenda, reflected in his latest book Causality, Counterfactuals, and Belief (2025). His office is located in the Jackman Humanities Building, and he is affiliated with the University of Toronto’s philosophy faculty and research initiatives such as the Balzan Research Project on Styles of Reasoning.

• Formal Epistemology
• Philosophy of Science
• Logic
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David A. Stephens is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. He served as Chair of the Department from 2015 to 2019 and as Vice-Dean in the Faculty of Science from 2019 to 2025. His research focuses on Bayesian inference, biostatistics, causal inference, bioinformatics, and statistical genetics. He holds prestigious fellowships: International Statistical Institute (2015), American Statistical Association (2019), and Royal Society of Canada (2024). His work addresses challenges in epidemiology, HIV transmission dynamics, and clinical trial design. Key research themes include: Bayesian hierarchical modeling for infectious diseases (e.g., SARS-CoV-2, HIV) Causal inference in dynamic treatment regimes Survival analysis and censored data methods Statistical genomics and epigenetics His publications analyze public health trends, such as HIV transmission clusters in Quebec and SARS-CoV-2 seroprevalence in Canada. Methodologically, he develops novel techniques for time-series analysis, recruitment forecasting in clinical trials, and computational statistics. Notable contributions include: Advancing phylogenetic cluster inference in HIV studies Optimizing warfarin dosing strategies via SMART trials Modeling gut microbiota impacts on growth faltering in infants His academic leadership includes roles at McGill and prior experience at Imperial College London. His work bridges statistical theory and practical healthcare applications, emphasizing interdisciplinary collaboration.

• Bayesian Inference
• Biostatistics
• Causal Inference
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Alex Shestopaloff is a Lecturer in Statistics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. Previously, he was a Research Fellow at the Alan Turing Institute (2017–2020) and a Junior Research Fellow at Campion Hall, Oxford. He holds a PhD in Statistics from the University of Toronto (2016), supervised by Radford M. Neal. His research focuses on developing efficient MCMC methods, high-dimensional time series analysis, network science, and applications in financial market microstructure. Education: PhD in Statistics, University of Toronto (2016) Supervisor: Radford M. Neal Research Interests: Bayesian online learning in non-stationary environments Limit order book modeling and trading strategies Graph clustering and network analysis Statistical methods for high-dimensional data Algorithmic trading and cryptocurrency markets His recent work spans financial engineering, machine learning, and statistical methodologies. Notable contributions include cluster-based trading strategies (ClusterLOB), generalized Bayesian filtering frameworks, and scalable graph analysis techniques. Collaborations with industry partners (e.g., Wise Plc) highlight applied research in financial systems. Advising & Alumni: Current advisees include Yichi Zhang (Oxford), Maria Fernanda Pintado (QMUL), and Dave Lui (Oxford) Alumni: Gerardo Duran-Martin (Postdoc at Oxford-Man Institute), Claudio Bellani (Citadel Securities) Labs/Teams: Leads interdisciplinary projects at QMUL and collaborates with the Alan Turing Institute on financial and network science initiatives.

• Markov Chain Monte Carlo (MCMC) Methods
• High-Dimensional Time Series Analysis
• Network Science
• +4 more

Brian Ingalls is a Professor in the Department of Applied Mathematics and cross-appointed to Biology at the University of Waterloo. His research applies mathematical and control-theoretic approaches to biological systems, including genetic regulatory networks, microbial communities, and cellular metabolism. Institutional Affiliation: Faculty of Mathematics, University of Waterloo Contact: bingalls@uwaterloo.ca His work focuses on systems biology and synthetic biology , particularly sensitivity analysis of biochemical networks, optimal experimental design, and mathematical modeling of cellular processes. Research funding comes from NSERC and CIHR . Notable contributions include the textbook Mathematical Modeling in Systems Biology (MIT Press, 2013) and the Ingalls Quantitative Cell Biology Lab , which investigates intracellular and intercellular network dynamics through computational and experimental methods. Key Collaborations: iGEM Waterloo, Chemical Engineering, and international synthetic biology networks Advising: Mentored 15+ graduate students and postdocs across applied math, biology, and engineering fields

• Systems Biology
• Synthetic Biology
• Mathematical Modeling
• +3 more

Aurélie Labbe is a Full Professor in the Department of Decision Sciences at HEC Montréal, holding the prestigious FRQ-IVADO Chair in Data Science. Appointed as Co-Scientific Director – Academic Partnerships at IVADO in October 2023, she plays a key leadership role in establishing connections between IVADO and partner universities. Her academic journey includes a PhD in Statistics from the University of Waterloo, a Master's degree in Statistics from the University of Montreal, and dual Bachelor's degrees in Applied Mathematics and Social Sciences from Paris-Dauphine University and Pure Mathematics from Versailles-St Quentin University. Her research spans multiple interdisciplinary domains with a focus on developing advanced statistical and machine learning methodologies for big data analysis. Labbe's work bridges theoretical statistics with practical applications across diverse fields including genomics, neuroscience, transportation systems, and health informatics. She has made significant contributions to kernel methods, matrix factorization techniques, random forest applications, and spatiotemporal data analysis, with publications appearing in top journals across multiple disciplines. Analyzing her recent publications reveals a clear trend toward methodological innovation applied to complex real-world problems. Her work demonstrates expertise in handling high-dimensional data from diverse sources including neuroimaging, transportation networks, and genomic studies. The interdisciplinary nature of her research connects statistical theory with applications in healthcare, transportation safety, and biological sciences, reflecting her ability to develop methods that address domain-specific challenges while advancing statistical methodology. Holder of the FRQ-IVADO Chair in Data Science Member of the Center for Mathematical Research Training Professor Labbe actively mentors the next generation of data scientists, supervising numerous doctoral and master's students. Her supervision portfolio includes 1 doctoral thesis (2023), 4 master's theses (2022-2024), and 32 supervised projects spanning 2019-2025. Her students' work covers diverse applications including transportation safety, healthcare analytics, financial modeling, and environmental analysis. Through her leadership of the FRQ-IVADO Chair in Data Science, she coordinates research activities that integrate mathematical, statistical, and computer science expertise with domain knowledge from various data-generating fields. As Co-Scientific Director at IVADO, Professor Labbe leads efforts to establish connections with faculties and departments across five partner universities, integrating them into IVADO's research and knowledge transfer activities. Her leadership role positions her at the forefront of advancing data science research and applications in Quebec's academic ecosystem.

• Data Science
• Statistical Learning
• Machine Learning
• +7 more

Reed Essick is an Assistant Professor at the Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto. His research focuses on experimental gravity, astrophysical signals, and nuclear physics, with particular emphasis on neutron stars, black holes, and gravitational waves. He develops advanced statistical methods like hierarchical Bayesian inference and nonparametric analysis for interpreting observational data from pulsars and gravitational wave detectors. Dr. Essick collaborates extensively with international observatories such as LIGO, Virgo, and KAGRA, contributing to cutting-edge projects like multimessenger astronomy and precision cosmology. His work bridges computational astrophysics with observational techniques, addressing fundamental questions about dense matter and strong-field gravity. Key contributions include studies on gravitational wave equation-of-state constraints, pulsar timing analysis, and the application of machine learning to detector data. His research leverages both ground-based interferometers and space-based observations to explore extreme astrophysical environments.

• Experimental Gravity
• Astrophysical Signals
• Nuclear Physics
• +8 more

Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics. Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow). His research explores information theories of learning , online learning , and nonstandard foundations for decision theory . Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures. Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.

• Machine Learning
• Statistics
• Probabilistic Programming
• +7 more

Jiannan Wang is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). He holds a Ph.D. from Tsinghua University (2013) and a B.Sc. from Harbin Institute of Technology (2008). His research focuses on database systems, data management, and data science, with particular emphasis on data cleaning, crowdsourcing, and big data technologies. He leads the SFU Data Science Research Group, aiming to accelerate data science workflows through innovative tools like DataPrep and ConnectorX. Education: Ph.D. in Computer Science and Technology, Tsinghua University, China (2013) B.Sc. in Computer Science and Technology, Harbin Institute of Technology, China (2008) Research Interests: Dr. Wang's work spans database systems, data cleaning, crowdsourcing, and big data education. He develops open-source tools for data scientists to streamline data preparation and analysis. His lab's mission is to make data science more efficient through technologies like DataPrep and ConnectorX . Awards: IEEE TCDE Rising Star Award (2018) CS-Can|Info-Can Outstanding Early Career Researcher Award (2020) VLDB Best Experiments, Analysis & Benchmark Paper Award (2021) PVLDB Distinguished Review Board Member Award (2020) Advising & Leadership: Director of SFU's Professional Master's Program in Big Data and Visual Computing. Supervised over 20 graduate and undergraduate students, many of whom have gone on to roles at top companies like Google, Amazon, and Huawei. Lab & Teams: Part of the SFU Data Science Research Group and the SFU Big Data Academic Advisory Committee. His lab collaborates with industry partners and contributes to open-source projects in data management and machine learning.

• Database Systems
• Data Management
• Data Cleaning
• +5 more

Roman Krems is a Professor and Distinguished University Scholar at the University of British Columbia (UBC) in the Department of Chemistry, with affiliations to the Stewart Blusson Quantum Matter Institute. His research focuses on the intersection of quantum physics, machine learning, and chemistry, particularly in quantum materials and quantum technologies such as quantum computing and sensing. Key Roles: Professor at UBC (2013–present), Distinguished University Scholar (2017–present) Education: Ph.D. from Göteborg University (2002), Postdoctoral Fellow at Harvard-MIT Center for Ultracold Atoms (2003–05) Research Interests include: Quantum machine learning (QML) for solving complex physics problems Quantum scattering theory in electromagnetic fields Applications of quantum computing to chemistry Developing machine learning algorithms for quantum dynamics Recent publications highlight advancements in extrapolating quantum observables, Gaussian process models for collision dynamics, and quantum walks in disordered systems. His work bridges theoretical physics, computational methods, and experimental applications in cold molecule research. Scientific Awards include the UBC Killam Teaching Prize (2017), election as Fellow of the American Physical Society (2015), and the Keith Laidler Award (2013). He has held editorial board positions for journals such as Machine Learning: Science & Technology and New Journal of Physics . Research Group members include graduate students and postdocs working on quantum technologies, machine learning, and molecular scattering. He also contributes to outreach through invited talks and seminars at institutions like MIT and Lawrence Berkeley National Laboratory.

• Quantum Physics
• Machine Learning
• Quantum Chemistry
• +3 more

Ryan Browne is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a BMath (2004), MMath (2006), and PhD (2009) from the same institution. His research focuses on model-based clustering , classification , and measurement system quality assessment , with applications in multivariate analysis and statistical inference. He is particularly known for contributions to mixture models and their computational optimization. Education: BMath in Statistics, University of Waterloo (2004) MMath in Statistics, University of Waterloo (2006) PhD in Statistics, University of Waterloo (2009) Ryan’s work emphasizes flexible statistical methodologies , including advancements in skewed distributions, high-dimensional data analysis, and robust algorithms for clustering and classification. He has received the prestigious 2011 W.J. Youden Award from the American Statistical Association for his PhD research on measurement system evaluation. His research trends span computational statistics (e.g., sketching algorithms for big data) and model-based clustering innovations (e.g., mixtures of generalized hyperbolic distributions). Recent work explores parsimonious models, nested Gaussian structures, and efficient parameter estimation for complex datasets. Key Awards: 2011 W.J. Youden Award (American Statistical Association) Ryan collaborates on applied projects, including industrial ecology and sensory data analysis. He has developed R packages like mixture and MixGHD , which implement his methodological contributions.

• Model-based Clustering
• Classification
• Measurement Models
• +4 more