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.
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.
Gabor Virag is an Associate Professor of Economic Policy Analysis at the University of Toronto, Mississauga , with a cross-appointment to the Rotman School of Management . He serves as the PhD Coordinator for the Economic Analysis and Policy area. His research focuses on market dynamics, auction theory, and search theory, with a particular interest in decentralized market interactions and information policy. Education : PhD in Economics from Princeton University, MA from Central European University, BA from Budapest University of Technology and Economics. Research Interests : Economic Theory, Industrial Organization, Game Theory, and their applications to dynamic contests, patent markets, and labor economics. Publications : His work appears in top-tier journals such as the American Economic Journal, Games and Economic Behavior, and Review of Economic Studies, emphasizing auctions with resale, innovation prizes, and wage inequality. Collaborations : Co-author of studies with scholars from Bocconi University, Claremont McKenna College, and the Hungarian Academy of Sciences.
Dr. Martin Scanlon is a Professor and Dean of the Faculty of Agricultural and Food Sciences at the University of Manitoba. His work focuses on physical and structural changes in plant materials during food processing, particularly in oilseed-based systems and cereal products. Education: Operative Miller Certificate (with Distinction), City & Guilds (London), England PhD (Food Science), University of Leeds, England BSc Hons (Food Science), University of Leeds, England His research spans modeling process-ingredient interactions, aerated food materials, ultrasonic analysis, and grain-legume science. Recent projects include novel canola oil extraction methods and mitigating acrylamide precursors in wheat. Analysis of his publications reveals expertise in sustainable processing (supercritical CO₂, microemulsions), dough rheology, antioxidant recovery, and bubble dynamics in cereal systems. No scientific awards are explicitly mentioned. Dr. Scanlon is not currently accepting graduate students and has not disclosed specific grant funding or lab affiliations in the provided texts.
Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.
Maurice D. Levi was a distinguished Professor of Economics at the University of British Columbia (UBC), affiliated with the Sauder School of Business. He held academic roles including Visiting Assistant Professor (pre-1974) and full Professor, contributing significantly to research and education. His career began with a Killam Post-Doctoral Teaching Fellowship at UBC in 1972. Education: B.A. Economics (First Class Honors, University of Manchester, 1967); M.A. (University of Chicago, 1968); Ph.D. Economics (University of Chicago, 1972). Research focused on econometrics, behavioral economics, and market psychology. Notable works include analyses of stock market cycles, daylight saving anomalies, and hormonal influences on M&A decisions. He authored influential textbooks like International Finance (5 editions) and Thinking Economically , translated globally. Awarded eight teaching awards for his ability to simplify complex economic concepts. He chaired the Finance Division (four terms) and the Centre for International Business Studies, and served on UBC's Appointment Promotion and Tenure Committee and Senior Appointments Committee. His legacy includes fostering interdisciplinary scholarship and collegial mentorship. Donations in lieu of flowers were directed to the Parkinson Society of BC, reflecting his family’s wishes.
Selim Topaloglu is an Associate Professor and RBC Fellow of Finance at the Smith School of Business, Queen's University. He holds a PhD in Finance from Arizona State University, an M.A. in Finance from the Wharton School of the University of Pennsylvania, and a B.S. in Management from Bilkent University. His research focuses on trading behavior of individuals and institutions, initial public offerings (IPOs), and analyst behavior. His work has been published in top journals such as the Journal of Finance, Journal of Financial Economics, and Review of Financial Studies. Education: Ph.D. in Finance, Arizona State University (2002) M.A. in Finance, Wharton School, University of Pennsylvania (1998) B.S. in Management, Bilkent University (1995) Research Interests: Selim's work explores institutional and individual investor behavior, IPO dynamics, and the role of investment banks. He examines how market structures and regulatory frameworks influence trading strategies and market efficiency. His research often bridges empirical finance and institutional economics, with a focus on understanding anomalies in financial markets. Awards: RBC Fellow of Finance (2006–2017) Queen’s School of Business New Researcher Achievement Award (2004) Dean's Fellowship for Distinguished Merit (Wharton School) Grants & Funding: SSHRC Research Grants (2005–2008, 2011–2014) General Research Grants from Smith School of Business McLeod Summer and Term Research Assistantships (2003–2014)
Murray Carlson is a Professor of Finance at the University of British Columbia's Sauder School of Business. He holds roles as Senior Associate Dean for Partnerships and Community Outreach and chairs the Advisory Council in Finance. His academic qualifications include a BSc from Queen's University, an MBA, and a PhD from UBC. Carlson's research focuses on empirical studies of corporate decisions and asset prices, with particular emphasis on equity risk premia, asset return predictability, and market dynamics. His work explores themes like information diffusion, arbitrage constraints, and heterogeneous agent behavior in financial markets. Recent publications highlight contributions to understanding the term structure of equity risk premia, horizon effects in returns, and municipal capital structure. His research often bridges theoretical models with empirical evidence, addressing practical questions in corporate finance and investment strategies. No scientific awards are explicitly listed, though his extensive publication record indicates significant scholarly impact. He advises students through advanced finance courses and maintains active engagement in academic and community partnerships.
Benjamin Croitoru is an Associate Professor of Finance at McGill University's Faculty of Management, serving as Associate Dean for Undergraduate Programs and Academic Director of the McGill Personal Finance Essentials course (300,000+ participants). He holds a PhD in Finance from the Wharton School, University of Pennsylvania, and diplomas in Actuarial Science and Economics from French institutions. His research focuses on asset pricing under market imperfections, including taxation, transaction costs, and portfolio constraints. Notable areas include international finance, heterogeneous beliefs, and strategic market behavior. His work has appeared in top journals like the Journal of Financial Economics and Review of Financial Studies . Dr. Croitoru has received awards including the Junior Faculty Chair (2000–2003) and MBA Japan Teaching Award (2019). His grants span SSHRC, IFM2, and FQRSC programs. He teaches finance at all academic levels and leads initiatives like the globally recognized Personal Finance Essentials course. His administrative roles include overseeing undergraduate programs and advising on financial literacy. He has presented research globally and engages in media commentary on portfolio management and financial markets.
Charles Martineau is an Associate Professor of Finance at the University of Toronto-Scarborough and holds a cross-appointment at the Rotman School of Management. He serves as Associate Director of Research at the Rotman Financial Innovation Hub (FinHub). University : University of Toronto School : University of Toronto-Scarborough Department : Finance Academic Rank : Associate Professor Research Interests : Martineau specializes in information economics, focusing on: Price discovery mechanisms in financial markets Investor attention to macroeconomic news Asset pricing anomalies Behavioral responses to financial disclosures Market microstructure dynamics Risk premia quantification Recent Publications : His 2017-2024 work explores: Conditional CAPM applications Earnings announcement drift FOMC-induced market reversals Social media's impact on price revelation Bond market excess returns News-driven trading behavior Grants & Collaborations : Funded by SSHRC, Toronto-Montreal Exchange, NASDAQ Educational Fund, and Canadian Securities Institute. Collaborates with scholars like Vincent Gregoire, Adlai Fisher, and Marius Zoican. Leads data science initiatives including open-source macroeconomic attention indices.
David Ardia is a Full Professor in the Department of Decision Sciences at HEC Montréal, promoted to this position on June 1, 2025. Previously, he served as an Associate Professor from June 2020 to May 2025. He holds the Research Professorship in Sentometry and is a member of the Study and Research Group on Decision Analysis (GERAD) and the International Statistical Institute. Ardia is also an elected member of the ISI Louis Bachelier Fellow and serves as Associate Editor for both the International Journal of Forecasting and the Journal of Statistical Software. His educational background includes a Ph.D. in Financial Econometrics from the University of Fribourg, a Master of Applied Sciences in Quantitative Finance from the Swiss Federal Institute of Technology Zurich and University of Zurich, and a Master of Science in Financial Engineering from the University of Neuchâtel. Ardia's research focuses on the intersection of quantitative finance, machine learning, and natural language processing, with particular emphasis on sentometrics (textual sentiment analysis in finance), risk management, and climate finance. His work spans financial econometrics, volatility modeling, and the application of advanced statistical methods to asset allocation and economic forecasting. He has pioneered methods for analyzing climate change concerns in financial markets and has made significant contributions to understanding green versus brown stock performance. His publication record shows a strong trajectory in high-impact finance and statistics journals, with recent work examining Robinhood trading patterns, cryptocurrency markets, climate finance, and innovative methodological approaches to financial time series analysis. His research demonstrates increasing focus on sustainability applications within quantitative finance. Prix de la qualité des données ouvertes 2024 (Canadian Open Data Community) Prix de recherche pour les professeures et professeurs agrégés (HEC Montréal, 2024) Prix pour l'excellence en pédagogie (HEC Montréal, 2022) Best Paper Award at the 38th International Conference of the French Finance Association Best Paper Award 2018-2019 from International Journal of Forecasting eRum 2020 COVID19 contest winner for the COVID-19 Data Hub Ardia actively supervises numerous graduate students, with over 70 mentorship activities documented in the past five years, spanning both thesis supervision and supervised projects. His research is supported by collaborations with institutions including IVADO, the R Consortium, and the University of Lugano. He co-created the influential COVID-19 Data Hub platform, which integrates epidemiological data with policy measures and spatial databases to analyze pandemic impacts. His research group focuses on developing computational tools for financial analysis, particularly through R packages like MSGARCH for Markov-switching GARCH models and sentometrics for textual sentiment analysis. This work bridges academic research with practical applications in financial institutions and policy analysis.
Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.
Prosper Dovonon is Full Professor of Economics at Concordia University, Montréal, Canada, where he holds the Tier 1 Concordia University Research Chair in Econometrics of Large Datasets . He is concurrently Adjunct Professor at the University of Adelaide, Australia, and has previously served as Associate and Assistant Professor at Concordia, Visiting Professor at HEC Montréal, and Assistant Vice-President at Barclays Wealth in London. Education Ph.D. in Economics, Université de Montréal (2007) M.Sc. in Statistics and Economics, ENSEA, Abidjan, Côte d’Ivoire (2000) M.Sc. in Mathematics, Université Nationale du Bénin, Abomey-Calavi, Benin (1996) Research Interests Professor Dovonon’s research lies at the intersection of theoretical econometrics and financial data applications . He focuses on developing robust inferential procedures for moment-condition models, bootstrap techniques for high-frequency data, identification issues in GMM, and volatility modeling with factor structures that accommodate skewness and leverage effects. His work on large-dimensional datasets emphasizes scalable methods for estimation and testing in big-data environments. Scientific Awards & Recognition Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets (2022–present) Collaborations & Affiliations Beyond Concordia and the University of Adelaide, he is affiliated with the Centre Interuniversitaire de Recherche en Économie Quantitative (CIREQ) in Montréal and has collaborated with leading scholars across North America, Europe, and Australia. His research is frequently cited in top econometrics and statistics journals, attesting to its broad impact.
Vincent Grégoire is a Full Professor in the Department of Finance at HEC Montréal. He holds a Ph.D. in Finance from the University of British Columbia, M.Sc. degrees in Financial Engineering and Electrical Engineering from Université Laval, and is a Chartered Financial Analyst (CFA). His research focuses on information economics, market microstructure, financial big data analytics, cybersecurity in finance, and machine learning applications in finance. Grégoire is affiliated with the Multidisciplinary Institute for Cybersecurity and Cyber Resilience (IMC²) and IVADO, and collaborates with Fin-ML. He co-chaired the Northern Finance Association in 2024-2025. His recent work includes groundbreaking studies on market microstructure dynamics, passive investing trends, and the implications of cybersecurity on financial systems. His research has been recognized with awards such as the 2022 Best Paper Award in Asset Pricing (Northern Finance Association) and the 2022 Chenelière Éducation/Gaëtan Morin Research Prize from HEC Montréal. Grégoire has supervised over 25 master’s theses and projects, covering topics like cryptocurrency diversification, ESG risk exposure, and fintech-driven solutions for sustainable practices. In teaching, he instructs courses such as Empirical Finance and Investment Analysis. His methodologies emphasize reproducibility and cutting-edge tools like Python for financial data analysis.
Margaret Fong is an Assistant Professor in the Department of Accounting at HEC Montréal. She holds a Ph.D. in Business Administration from the University of California, Berkeley, along with an M.Sc. in Business Administration and a B.Comm from UC Berkeley and the University of British Columbia, respectively. Her research focuses on quantitative finance and financial regulation, with recent publications exploring short sale disclosure rules and special purpose acquisition companies (SPACs). She teaches courses such as Management Accounting and Performance Measures, and Research in Financial Accounting. Recent supervision includes MSc projects analyzing AI's impact on restaurant franchising finance and the relationship between ESG performance and corporate fraud controversies. She is affiliated with HEC Montréal's main campus in Montréal, Canada.
