Nikolai Roussanov is the Moise Y. Safra Associate Professor of Finance at the Wharton School, University of Pennsylvania, and a Faculty Research Fellow at the National Bureau of Economic Research. His research spans asset pricing, econometrics, household finance, and macroeconomics, with a focus on market dynamics and behavioral economic factors. His research interests include: Asset pricing anomalies and risk factor modeling Household financial decision-making under uncertainty Macroeconomic impacts on commodity and currency markets Behavioral finance and mental accounting mechanisms Recent publications analyze inflation risks across asset classes, corporate bond valuation, behavioral retirement strategies, and the role of leisure economics in declining work hours. His work frequently integrates empirical finance and econometric methodologies. Scientific contributions include: Faculty Research Fellow, National Bureau of Economic Research His scholarship bridges technical financial modeling with real-world economic phenomena, covering topics like oil price shocks, mortgage liquidity, and systemic market failures.
Sean Foley is a Professor of Applied Finance at Macquarie University, specializing in Fintech, Cryptocurrencies, Trading, and Market Design. He leads the Decentralized Assets division at the Digital Finance Cooperative Research Centre (DFCRC), bridging academia, industry, and government. His research focuses on blockchain applications like automated market makers, DeFi protocols, and stablecoins. Education: PhD in Finance from the University of Sydney (2014), focusing on 'The Impact of Regulation on Market Quality'. Research Interests: Decentralized finance (DeFi) systems Cryptocurrency market dynamics and regulation Market microstructure and liquidity provision Energy market crises and policy Regulatory frameworks for financial markets Key Projects: Leading the DFCRC's Industrial PhD Scholarships (2021–2031), mentoring students like Arvind Rangarajan and Juuso Artturi Itkonen. Research on Australia's National Electricity Market (NEM) suspension and energy policy. Awards: Best Paper Award at the Cryptocurrency Conference (2019) Philip Brown Prize for Best Australian Paper (2021) Exceptional Research Prize (2019) Advising & Grants: Supervised over 10 PhD students through DFCRC scholarships. Secured $181 million for the Digital Finance CRC. Lead applicant in the Gunns Ltd shareholder class action. Labs/Teams: Head of Decentralized Assets at DFCRC, collaborating on policy and technology. Co-researcher on 'Electricity Markets in Crisis' and cryptocurrency illicit use studies.
Hakan Berument is a Professor in the Department of Economics at Bilkent University, Ankara, Turkey, where he has been teaching since 1995. His academic career spans over 25 years with progressive appointments from Assistant Professor to full Professor. He has also served as Director of the Bilkent Energy Policy Research Center (2018-2019) and Advisor/Director of Energy Studies at the Center for Middle Eastern Studies (2020-2022). Berument received his PhD in Economics from the University of North Carolina at Chapel Hill in 1994, following an M.S. in Economics from the University of Kentucky (1989) and a B.S. in Economics from Middle East Technical University (1987). His research focuses on Monetary Economics, Macroeconomics, Time Series Analysis, and Econometrics, with particular emphasis on energy economics, oil price dynamics, and monetary policy effectiveness. Berument has made significant contributions to understanding the relationships between oil prices, exchange rates, and economic performance, especially in emerging markets and Turkey. His work frequently examines asymmetric price effects, market structures, and policy impacts across various energy sectors. Berument's recent publications reveal a strong focus on energy economics, particularly oil and electricity markets. His research employs advanced econometric techniques to analyze time series data, with growing attention to cross-border energy trade, price transmission mechanisms, and the interplay between financial markets and energy commodities. The breadth of his work spans from micro-level consumer behavior to macroeconomic policy implications. Ranked #1 among Turkish academic economists on supervising PhD dissertations (1990-2011) Ranked #4 among Turkish academic economists based on international publications (1999-2003) Parlar Foundation Young Investigator Award (2003) Turkish Social Sciences Association Young Social Scientist Promotion Award (2002) Research Fellow to Economic Research Forum (2005-Present) Vice-president, Econometric Research Association (2005-Present) Berument has supervised numerous graduate students, including over 15 Master's theses and several PhD dissertations. His research has been supported by various funding agencies including TUBITAK and the Economic Research Forum. He has served on editorial boards of multiple economics journals and contributed to policy discussions through his work with the Central Bank of the Republic of Turkey and other institutions. He has advised on energy policy through his directorship roles and frequent participation in policy discussions. While not explicitly mentioned as leading a specific research lab, Berument has been instrumental in establishing academic initiatives including Pazar11 meetings among economists. His contributions to energy policy research through the Bilkent Energy Policy Research Center have shaped discussions on Turkish energy markets and policy frameworks.
Jordan Siegel is a Professor of Strategy at the Ross School of Business, University of Michigan, and a Michael R. and Mary Kay Hallman Faculty Fellow. He also serves as a Visiting Faculty member at The American College of Greece (ACG). His academic background includes a Ph.D. from MIT, and B.A. and M.A. degrees from Yale University. Professor Siegel’s research focuses on global strategy, particularly how companies leverage institutional differences across borders to gain competitive advantages through governance and human resource management strategies. His work examines institutional arbitrage—how firms exploit formal and informal rules (e.g., laws, cultural norms) to enhance performance, even in single-country operations. Notably, he investigates how foreign multinationals in Japan and South Korea exploit social biases by promoting female managers, leading to long-term performance improvements. His findings highlight the strategic use of labor market discrimination as a competitive tool. Professor Siegel’s research has been published in top-tier journals such as Management Science , Administrative Science Quarterly , and Strategic Management Journal . He is affiliated with the William Davidson Institute and Harvard Korea Institute, contributing to interdisciplinary studies on global business strategy and institutional dynamics.
Francisco Barillas Bedoya is an Associate Professor at the School of Banking and Finance within the UNSW Business School, University of New South Wales. His research focuses on theoretical and empirical asset pricing, particularly portfolio choice, asset pricing tests, macrofinance, and term structure of interest rates. He has published extensively in top-tier journals like the Journal of Finance and Management Science. PhD from New York University MA from University of British Columbia BSc from Trent University His recent publications analyze Sharpe ratios for model comparison, speculative behavior in bond markets, and risk premia in fixed income markets. While no formal awards are listed, his work intersects financial economics, econometrics, and computational methods. Office: Level 3, Room 333C, Ref E12 Email: f.barillas@unsw.edu.au
Prof. Claudio J. Tessone is a Professor of Blockchain and Distributed Ledger Technologies at the Department of Informatics, University of Zurich. He serves as Head of the Blockchain and Distributed Ledger Technologies group, Chairman of the UZH Blockchain Center, and is incharge of the NetSci Society. His academic background includes a PhD in Physics (Complex Systems) and an Habilitation in Complex Socio-Economic Systems from ETH Zurich. Education: PhD in Physics (2006): Thesis on synchronization in stochastic systems, Universitat de les Illes Balears, Spain Habilitation (2015): Thesis on agent-based modeling of socio-economic systems, ETH Zurich Master in Physics (1999): Thesis on stochastic resonance, Instituto Balseiro, Argentina Research Interests: Prof. Tessone specializes in modeling complex socio-economic and socio-technical systems, with a focus on blockchain-based systems. His work explores crypto-economics, blockchain scalability, decentralized finance (DeFi), and the interplay between micro-level agent behavior and macro-level emergent properties. Notable areas include transaction network analysis in Bitcoin/Ethereum, consensus mechanisms (Proof-of-Stake/Work), and blockchain governance models. Publications Trends: Recent articles emphasize empirical blockchain analysis (e.g., Ethereum microvelocity, Bitcoin mesoscopic structure), DeFi arbitrage strategies, and privacy-preserving blockchain applications in healthcare. His work bridges theoretical agent-based models with real-world blockchain datasets, addressing both technical and socio-economic dimensions of distributed ledger technologies. Grants & Labs: Director of the UZH Summer School on Blockchain and Certificate of Advanced Studies program. Active in interdisciplinary collaborations through the URPP Social Networks (2015–2021) and ETH Zurich’s Systems Design group (2007–2014). Labs/Initiatives: Leads the UZH Blockchain Center, a hub for academic-industry research on blockchain applications in finance, governance, and digital transformation.
Jean-Pierre Fouque is a Professor in the Department of Statistics and Applied Probability (PSTAT) at the University of California, Santa Barbara. His research focuses on stochastic processes, financial mathematics, systemic risk, and reinforcement learning, with a particular emphasis on mean field games and multi-scale stochastic models. He explores applications in portfolio optimization, risk management, and algorithmic finance. His work combines theoretical advancements in stochastic analysis with practical applications in economics and finance. Notable contributions include developing models for systemic risk in financial networks, analyzing reinforcement learning algorithms in mean-field frameworks, and studying stochastic volatility effects in derivatives pricing. Recent research trends include integrating deep learning techniques for systemic risk quantification, advancing multi-scale asymptotic methods for portfolio optimization, and investigating strategic interactions in financial systems using game-theoretic approaches. His publications frequently address topics such as stochastic volatility calibration, optimal investment strategies under uncertainty, and the dynamics of financial markets under stress scenarios. Dr. Fouque has contributed to foundational textbooks and edited volumes on systemic risk and mean field games. His interdisciplinary work bridges probability theory, mathematical finance, and computational methods, impacting both academic research and practical risk management practices.
James Anderson is an Assistant Professor in the Department of Electrical Engineering at Columbia University, with affiliations to the Data Science Institute (DSI) and multiple research centers including the Computing Systems for Data-Driven Science and Foundations of Data Science. Prior to Columbia, he was a Senior Research Scientist at Caltech’s Computing + Mathematical Sciences division (2016–2019) and held a Junior Research Fellowship at the University of Oxford’s Department of Engineering Science (pre-2012). He earned his DPhil (PhD) in Engineering Science from Oxford in 2012. His research focuses on optimal/robust control theory, mathematical programming, data privacy, and cyber-physical systems, with applications in smart grids, systems biology, and power systems. Recent work emphasizes energy storage strategies, distributed control algorithms, and cybersecurity in critical infrastructure. His publications span advanced control methodologies (e.g., reinforcement learning for LQR problems), energy market dynamics, and resilient system designs. Notable contributions include frameworks for decision-focused energy storage arbitrage and defenses against false data attacks in power grids. He actively collaborates on federated learning approaches for distributed systems and has pioneered techniques for system-level synthesis in cyber-physical architectures. Anderson’s affiliations include the Data Science Institute (DSI) and specialized centers focused on data-driven science and energy systems. His work bridges theoretical control advancements with real-world applications in energy and healthcare.
Prof. Rama Cont is a Statutory Professor of Mathematics at the University of Oxford and a Professorial Fellow at St Hugh's College . He serves as Director of the Centre for Doctoral Training in Mathematics of Random Systems , Faculty Member of the Stochastic Analysis Group , and Senior Research Fellow at the Institute for New Economic Thinking . Additional roles include Director of the Oxford Martin Programme on Systemic Resilience , Principal Investigator at the Oxford Suzhou Centre for Advanced Research , and Editor-in-Chief of Mathematical Finance . His research interests span pathwise methods in stochastic analysis, rough analysis, functional Ito calculus, mathematical modeling in finance, systemic risk, and data-driven decision systems. Recent publications focus on causal transport, rough volatility, and deep residual networks, reflecting his interdisciplinary approach to mathematics and finance. Functional Ito calculus and pathwise integration Rough volatility and financial market dynamics Systemic risk in financial networks Deep learning applications to finance and stochastic processes He has received prestigious awards including the Louis Bachelier Prize , SIAM Fellowship, Royal Society APEX Award, and IMA Fellowship. His editorial roles and seminar leadership underscore his influence in mathematical finance and stochastic analysis.
Dr. Mike Tehranchi is a faculty member at the University of Cambridge, affiliated with the Statistical Laboratory within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) . His research focuses on mathematical finance, stochastic processes, and probability theory. He holds a Lecturer position and is actively involved in academic research, with notable contributions to financial models, term structure analysis, and stochastic calculus. His work bridges theoretical probability and applied finance, addressing topics such as interest rate modeling, implied volatility, and optimal investment strategies. Tehranchi’s research often intersects with optimization, statistical methods, and interdisciplinary applications in astrophysics and fluid dynamics. He maintains an active publication record and contributes to the academic community through his role in the Statistical Laboratory. Key research trends in his articles include the analysis of financial derivatives, stochastic processes in market dynamics, and the application of advanced mathematical techniques to real-world financial problems. His work emphasizes rigorous theoretical foundations while addressing practical challenges in quantitative finance. Dr. Tehranchi has no listed students or academic awards in the provided texts. He can be reached via email and is based in Room D1.04 at the Statistical Laboratory.
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.
Lars Augestad Lochstoer is a Professor of Finance at the UCLA Anderson School of Management, where he teaches Empirical Methods in Finance and Data Analytics and Machine Learning in the Master of Financial Engineering program. He previously held faculty positions at Columbia University and London Business School, and served on the Asset Allocation Advisory Committee for the Norwegian Sovereign Wealth Fund from 2016 to 2022. Dr. Lochstoer earned his Ph.D. in Finance from the University of California, Berkeley's Haas School of Business in 2005, following his Sivilingeniør Business Economics degree from the Norwegian University of Science and Technology in 1999. His research focuses on understanding the economic mechanisms that drive asset prices, including stock market return dynamics, cross-sectional stock returns, exchange rates, and commodity markets. He has made significant contributions to asset pricing literature, particularly in volatility expectations, risk-return tradeoffs, and currency risk. His publication record reveals a strong focus on behavioral aspects of asset pricing, with recurring themes of investor expectations, volatility dynamics, and market anomalies. His work often combines theoretical models with empirical evidence, frequently incorporating quantitative methods and data science approaches. Recent publications show increasing attention to currency risk and multi-horizon risk-return relationships, reflecting evolving market conditions and research interests. EFA Viz Risk Management Prize for best paper in Energy Markets, Securities and Prices (2009) Michigan Ross School of Business Mitsui Finance Symposium Best Discussant Award (2012) UCLA Anderson Excellence in Teaching Award (2017, 2020, 2021) RFS Distinguished Referee Award (2021) As an active member of the academic finance community, Lochstoer serves as an associate editor for the Review of Finance and the Critical Finance Review, having previously served in the same capacity for the Review of Financial Studies. His professional service includes committee roles in major finance associations and extensive reviewing for top finance and economics journals. He has also contributed to practical finance through his service on the Asset Allocation Advisory Committee for the Norwegian Sovereign Wealth Fund.
Assoc. Prof. Zehra Eksi-Altay holds a position at the Institute for Statistics and Mathematics at Vienna University of Economics and Business (WU). Her research focuses on financial mathematics, stochastic modeling, and partial information control problems in finance. She has expertise in credit risk modeling, derivatives pricing, and commodity markets. Eksi-Altay has a PhD in Financial Mathematics (2011) and completed her Habilitation in 2017. She has advised one doctoral thesis and has published extensively in top-tier journals like Quantitative Finance and Journal of Computational and Applied Mathematics . Her work bridges theoretical advancements with practical applications in areas such as regime-switching models, optimal portfolio strategies, and liquidity analysis. Education: BSc, MSc (2005), PhD (2011) Habilitation: 2017 Key Research Themes: Partial Information Models, Stochastic Control, Credit Risk, Algorithmic Trading Her recent work explores regime-switching affine term structures, optimal trading strategies under uncertainty, and dark pool liquidity analysis. Eksi-Altay has received one academic prize, though its specific name is not detailed in the provided text. Her contributions span both theoretical developments and applied finance, often collaborating with institutions like WU’s Institute for Statistics and Mathematics.
Cody Hyndman is a Full Professor and Acting Department Chair at the Department of Mathematics and Statistics, Concordia University, with a focus on Mathematical Finance, Machine Learning, and Stochastic Analysis. He has held significant administrative roles including Department Chair (2017–2023) and Acting Graduate Programs Director (2025–2025). Education: PhD, University of Waterloo (2005) MSc, University of Alberta BCom, University of Alberta His research spans Mathematical Finance , Stochastic Differential Equations , and Machine Learning , with notable contributions to arbitrage-free modeling, neural networks, and computational methods. Recent publications emphasize geometric deep learning and regularization techniques in finance. Scientific Awards: 2023: Concordia Academic Leadership Award Hyndman supervises graduate students in Mathematics and Statistics and co-founded the NSERC CREATE Program on Machine Learning in Quantitative Finance and Business Analytics (FIN-ML) , fostering industrial internships and interdisciplinary training.
Ke Xu is an Assistant Professor at the Department of Finance, Faculty of Business and Economics, University of Victoria. His research bridges finance, econometrics, and cryptocurrency, focusing on market microstructure, high-frequency trading, and price discovery mechanisms. He has extensively studied Bitcoin ETFs, fractional cointegration models, and machine learning applications in financial markets. Key Research Areas: Market Microstructure High-Frequency Trading Cryptocurrency Dynamics Price Discovery Machine Learning in Finance Financial Econometrics Article Trends: Xu’s work spans empirical analyses of Bitcoin ETFs, volatility modeling (e.g., affine GARCH), and algorithmic trading strategies. His recent papers explore mini flash crashes using machine learning, regulatory impacts on market quality, and sustainable crypto portfolios.
