Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
David A. Hsieh is the Bank of America Professor of Finance at the Fuqua School of Business, Duke University, where he has been a faculty member since 1993. Previously, he served as Associate Professor and Assistant Professor at the University of Chicago's Graduate School of Business from 1981-1989. His extensive research has significantly contributed to the understanding of hedge funds, financial risk management, and nonlinear dynamics in financial markets. Massachusetts Institute of Technology, Ph.D. in Economics, 1981 Yale University, B.S. in Economics and Mathematics, 1976 (Summa Cum Laude, Phi Beta Kappa) Phillips Academy, Andover, 1972 (Cum Laude) Dr. Hsieh's research primarily focuses on the dynamics of asset prices and their implications for financial risk management. He has made significant contributions to understanding risk and return characteristics in hedge funds and commodity funds, pioneering work on nonlinear dynamics applications to financial markets. His research has evolved from early work on exchange rates and volatility modeling to more recent comprehensive analyses of hedge fund strategies, performance measurement, and industry structure. Hsieh's publication history reveals a clear progression from foundational work on nonlinear dynamics in financial markets to increasingly sophisticated analyses of hedge fund strategies and risk characteristics. His recent work, often in collaboration with William Fung and other prominent finance researchers, has focused on mega hedge fund firms, franchise value in the industry, and the evolution of hedge fund strategies toward more index-like products. The research consistently combines rigorous theoretical frameworks with robust empirical analyses across diverse market conditions. CAIA Award for Excellence in Alternative Investment Research (2015) CFA Institute Graham and Dodd Award of Excellence (2004) Bank of America Faculty Award (2002) Duke Cross-Continent Executive MBA Teaching Excellence Award (2002) Fischer Black Memorial Foundation Robert J. Schwartz Memorial Prize (1999) Smith Breeden First Prize (1990) Yale Science and Engineering Association High Scholarship Award (1976) Russell Henry Chittenden Prize (1976) Dr. Hsieh has served as a consultant for the International Monetary Fund (2007-2016) and the Bank for International Settlements (1998), and as a Visiting Scholar at both the International Monetary Fund and the Board of Governors of the Federal Reserve System. His editorial service includes Finance Editor for Management Science (2003-2009) and Associate Editor roles for several leading finance journals. He has developed extensive research resources including a Hedge Fund Data Library that has become widely used in academic and industry research.
Massachusetts Institute of TechnologyUnited States
Christopher J. Palmer is an Associate Professor of Finance at the MIT Sloan School of Management, specializing in financial decision-making, credit markets, and policy interventions. His research focuses on how individuals and institutions respond to economic upheavals in areas like bankruptcy, retirement savings, and real estate. He holds a PhD in Economics from MIT and a BA in Economics and Mathematics from Brigham Young University. Palmer’s work has been published in top journals such as the American Economic Review , Journal of Finance , and Review of Financial Studies . He explores topics including racial disparities in bankruptcy outcomes, consumer payment behavior, and the effects of quantitative easing. His research is supported by affiliations with the National Bureau of Economic Research (NBER) and the Jameel Poverty Action Lab (J-PAL). Key honors include the 2022 Jamieson Prize and the 2019 Society for Financial Studies Best Paper Award. Palmer has contributed to public debates on policy issues like rent control and retirement savings through media outlets such as The New York Times , Bloomberg , and Vox .
Zhi Da is the Howard J. and Geraldine F. Korth Chair in Finance and Professor of Finance at the University of Notre Dame , Mendoza College of Business, Department of Finance. He completed his Ph.D. in Finance at Northwestern University’s Kellogg School of Management (2006), preceded by an M.Sc. in Financial Engineering from the National University of Singapore (2001) and a B.B.A. with First-Class Honors (1999) from the same institution. Holding editorial roles at Journal of Finance , Management Science , Review of Financial Studies and several other top journals, he is a leading voice in empirical finance research. Education Ph.D. in Finance, 2006 – Kellogg School of Management, Northwestern University M.Sc. in Financial Engineering, 2001 – National University of Singapore B.B.A. (1st Class Honors), 1999 – National University of Singapore Research Interests Zhi Da’s scholarship sits at the intersection of asset pricing , behavioral finance , and market microstructure . He investigates how investor attention, institutional trading, liquidity frictions, and information flows jointly determine the cross-section of expected returns. His work delves into retail margin trading, the role of pension-fund flows in exchange-rate dynamics, the informational content of SEC filings, and the efficiency of short-selling mechanisms. By combining large-scale data analytics, textual analysis, and structural modeling, he uncovers novel predictors of returns ranging from presidential approval ratings to real-time attention measures. Recent projects explore fractional trading ’s impact on price efficiency, hedging demand as a driver of intraday momentum, and the hidden effort problem in delegated portfolio management. These themes collectively advance our understanding of limits to arbitrage and the formation of extrapolative beliefs. Publication Landscape Spanning 2025 back to 2009, his 15 most recent articles in Journal of Finance , Review of Financial Studies , Management Science , Journal of Financial Economics , and Journal of Financial and Quantitative Analysis converge on three broad motifs: (1) micro-level trading frictions—liquidity costs, margin requirements, and short-selling constraints; (2) macro-finance linkages—exchange rates, fiscal policy, and global capital flows; and (3) information economics—attention allocation, media analytics, and regulatory disclosures. The collective evidence demonstrates that seemingly small trading or informational frictions aggregate into large, persistent cross-sectional return predictability. Honors and Awards 2017 William F. Sharpe Award for Best Paper, Journal of Financial and Quantitative Analysis Lead-article distinctions in Journal of Finance , Review of Financial Studies , and Management Science Featured coverage in SmartMoney and CNBC Teaching & Mentorship At Notre Dame’s Mendoza College, Professor Da teaches Investments (undergraduate and MBA) and Fixed Income Securities , integrating cutting-edge research insights into the curriculum. While specific advisees are not listed, his extensive co-author network (22+ recurring collaborators) attests to a vibrant mentoring environment. Laboratory & Data Resources He publicly distributes the NAT (Net Arbitrage Trading) dataset, a stock-quarter panel of arbitrage positions used in Chen, Da & Huang (2019). This resource has become a standard tool for researchers studying arbitrage capital movements.
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).
Wei Xiong is the Hugh Leander and Mary Trumbull Adams Professor in Finance and Professor of Economics at Princeton University, affiliated with the Department of Economics and Bendheim Center for Finance. His research centers on behavioral finance and capital market imperfections with a focus on China's financial system and commodity markets. His educational background includes: Ph.D. in Finance from Duke University (2001) B.S. in Physics from University of Science and Technology of China (1993) Research Interests: Professor Xiong's work fundamentally explores behavioral finance and capital market imperfections , pioneering studies on speculative bubbles , heterogeneous beliefs in asset pricing , and financing frictions . His recent scholarship critically examines the financialization of commodity markets , belief distortions during financial crises , and the unique institutional dynamics of China's hybrid economy , where state and market forces interact. Publication Trends: Xiong's 48 publications in top-tier journals reveal an evolving trajectory from classical behavioral finance (2001-2013) toward contemporary China-focused systemic analysis (2017-2025). Recent works integrate cryptocurrency economics, real estate risk management, and policy design in emerging markets, demonstrating methodological rigor through structural modeling and empirical analysis of large-scale financial datasets. Scientific Awards: 2012 Smith Breeden Award (first prize) for the best non-corporate finance paper published in Journal of Finance 2013 NASDAQ OMX Award for the best asset pricing paper presented in Western Finance Association Meetings Professional Activities: As NBER research associate and former Management Science finance editor (2009-2011), Xiong bridges academia and policy. He co-founded VoxChina.org to foster China-world economic dialogue and co-edited The Handbook of China’s Financial System (Princeton University Press). His keynote addresses at major conferences—including the 2024 Chinese Economists Society meeting on “Information Discovery in a Hybrid Economy”—demonstrate sustained policy influence. Labs and Initiatives: Xiong leads Princeton's Bendheim Center for Finance research initiatives and directs VoxChina.org, which produces influential policy analyses on China's economy. His recent work includes the NBER-commissioned study on commodity market risks and ongoing projects examining big tech lending models and data privacy impacts on digital finance.
Suzanne S. Lee is an Associate Professor of Finance at the Scheller College of Business, Georgia Institute of Technology, where she has been a faculty member since 2005. She also serves as the Ph.D. Coordinator, playing a key role in graduate education and research training. Her educational background is highly quantitative and interdisciplinary: Ph.D., University of Chicago MBA, University of Chicago M.S. in Statistics, University of Chicago Dr. Lee's research lies at the intersection of asset pricing and financial econometrics, with a strong focus on jump processes, market microstructure, and derivative markets. She investigates how sudden price movements (jumps) impact asset returns, risk, and information flow in financial markets. Her work extends to cryptocurrency, currency markets, and carry trade strategies, combining theoretical rigor with empirical validation using high-frequency data. The analysis of her recent publications (2008–2024) reveals a consistent and influential research program centered on detecting and modeling jumps in financial time series. Her work spans equity, currency, and cryptocurrency markets, often employing advanced nonparametric and econometric techniques. A recurring theme is the role of jumps in pricing anomalies, risk measurement, and market efficiency, with increasing attention to digital assets in recent years. Dr. Lee is actively engaged in the academic community through editorial service: Associate Editor, Journal of Banking and Finance Associate Editor, Asia-Pacific Journal of Financial Studies She has presented her research at premier conferences such as the American Finance Association, European Finance Association, Econometric Society, and Society for Financial Econometrics. Her publications appear in the most prestigious journals in finance and econometrics, including the Journal of Finance , Review of Financial Studies , Journal of Financial Economics , and Journal of Econometrics . While specific grant details are not listed, her sustained publication record in top journals indicates significant research funding and academic impact. She advises Ph.D. students through her role as Ph.D. Coordinator, though individual advisees are not named in the text. Dr. Lee's work contributes to both theoretical and applied finance, improving our understanding of market dynamics, risk modeling, and asset pricing under extreme events. Her research has practical implications for risk management, trading strategies, and financial regulation.
Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.
Kevin Pisciotta is an Assistant Professor of Finance at the University of Kansas School of Business, specializing in empirical corporate finance, initial public offerings (IPOs), and information intermediaries. His work bridges corporate finance theory with real-world market dynamics, focusing on IPO processes, analyst behavior, and economic impacts of social issues like opioid abuse and sports betting. Education: Ph.D. in Business Administration, Pennsylvania State University M.S. in Finance, University of Delaware B.S. in Finance, University of Delaware Research Interests: His research explores how IPO bookbuilding mechanisms influence market outcomes, the role of sell-side analysts in information dissemination, and the macroeconomic consequences of phenomena like opioid abuse and sports gambling. He also investigates labor economics through the lens of corporate innovation and workforce productivity. Publications Trends: Recent work examines the interplay between sports betting and household financial stability (2024), IPO marketing strategies post-JOBS Act reforms (2018), and the spillover effects of opioid abuse on regional innovation ecosystems (2023). His studies frequently employ large-scale datasets to analyze market microstructure and policy impacts. Grants/Advising: No specific grants or advisee names are listed in available materials. Teaching experience includes undergraduate courses in investments, international finance, and corporate financial management. Labs/Teams: No dedicated lab affiliation is mentioned, though collaborations with institutions like Pennsylvania State University and the University of Delaware are implied through his educational background and co-authorships.
Robert P. Bartlett is the W. A. Franke Professor of Law and Business at Stanford Law School and a courtesy Professor of Finance at the Stanford Graduate School of Business. He serves as Co-Director of the Arthur and Toni Rembe Rock Center for Corporate Governance. Previously, he held roles at UC Berkeley School of Law and the University of Georgia School of Law, and practiced corporate law at Gunderson Dettmer. He earned his JD (2000) and BA (1996) from Harvard University. His research focuses on law and finance, particularly venture capital, market structure, corporate governance, and capital market regulation. Key areas include fractional shares' impact, odd-lot trading dynamics, and ESG integration in executive compensation. Recent articles address hidden liquidity, venture capital contract standardization, and fintech-driven consumer lending discrimination. Bartlett’s work bridges legal and financial disciplines, influencing policy debates on market transparency, regulatory frameworks, and corporate accountability. He has contributed to prominent journals like the Journal of Financial Economics and Review of Financial Studies , and authored book chapters on venture capital valuation. His affiliations include the Stanford Institute for Economic Policy Research and leadership roles in academic centers fostering corporate governance research.
Michael P. Wellman is a Professor of Computer Science and Engineering at the University of Michigan, specializing in computational game theory and its applications to economics and finance. He has advised 28 PhD graduates and currently mentors 6 students, emphasizing independent research and tailored advising approaches. His work focuses on multi-agent systems, strategic interactions, and agent-based modeling of financial markets. He holds the endowed Lynn A. Conway Professorship and created the Morris Wellman Faculty Development Professorship. His research group meets weekly for progress reports, paper discussions, and practice presentations. Wellman encourages internships, teaching experience, and conference participation (e.g., ICAIF, AAMAS, EC) to foster career readiness. His scientific contributions span empirical game-theoretic analysis (EGTA), market manipulation detection, and cybersecurity strategies. He prioritizes student independence, collaborative problem-solving, and ethical considerations in AI-driven financial systems.
Massachusetts Institute of TechnologyUnited States
Jiang Wang is the Mizuho Financial Group Professor at the MIT Sloan School of Management, where he has been a faculty member since 1990, progressing from Assistant Professor to his current named professorship. He holds appointments in the Finance department and maintains an active research program in financial economics. Massachusetts Institute of Technology, Sloan School of Management (2005-present) MIT Sloan School of Management: Assistant Professor (1990-1994), Associate Professor (1994-1998), Professor (1998-1999), NTU Professor (1999-2005) Wang's research focuses on financial economics, asset pricing, market liquidity, trading volume, and financial market microstructure , with significant contributions to understanding information dynamics in markets. His work bridges theoretical models with empirical analysis, particularly in Chinese capital markets. Wang has developed influential theories on liquidity, trading volume, and market efficiency that have shaped modern financial economics. His recent publications demonstrate continued scholarly productivity, with research spanning market uncertainty, circuit breakers, repo markets, and Chinese financial markets. Wang's work integrates theoretical modeling with empirical validation, maintaining relevance to both academic discourse and practical market concerns. China Economics Prizes (2018) Smith Breeden Prize (2007, 2006) New York Stock Exchange Award FAME Research Prize (2004) Trefftz Award, Western Finance Association (1990) Wang has advised numerous doctoral students and supervised significant research projects, though specific student names aren't listed in the available materials. His extensive grant history includes multiple NSF awards and industry-sponsored research. Wang has held leadership positions including President of the Western Finance Association (2017-2018) and Director of the China Center for Financial Research at Tsinghua University (2002-2014). His academic service includes editorial roles for major finance journals and advisory positions with institutions including the Federal Reserve Bank of New York, Nasdaq Stock Market, and China Securities Regulatory Commission.
Ronnie Sircar is the Eugene Higgins Professor of Operations Research and Financial Engineering at Princeton University , where he contributes to the Department of Operations Research and Financial Engineering (ORFE). His work spans financial mathematics, stochastic modeling, and applied probability, with a focus on market volatility, optimal investment strategies, and dynamic game theory. Email: sircar@princeton.edu Office: Sherrerd Hall, Room 208, Princeton, NJ 08544 His research interests include: Stochastic Volatility: Asymptotic analysis, calibration, and impact on option pricing and portfolio optimization. Mean Field Games: Applications to cryptocurrency mining, energy markets, and interbank network formation. Portfolio Theory: Forward performance processes, drawdown constraints, and risk-averse strategies. Credit Risk: Multi-name credit derivatives, CDO valuation, and risk measures. Energy Systems: Renewable reliability, unit commitment, and electricity market design. Recent publications emphasize mean field games in energy and blockchain, stochastic volatility in portfolio optimization, and machine learning applications for financial engineering. He has advised graduate students such as Giulia Crippa, Nicolas Garcia, and Burak Aydin, often collaborating with researchers including M. Soner, P. Chan, and A.M. Reppen.
Jin Ma is a Professor in the Department of Mathematics at the University of Southern California (USC), where he has served since 2007. He previously held professorships at Purdue University (1994–2008). His research focuses on stochastic analysis, stochastic differential equations, mathematical finance, and control theory. He directs USC's Mathematical Finance Program and serves on editorial boards for journals like Probability, Uncertainty and Quantitative Risk and SIAM Journal on Control and Optimization . Ma received his Ph.D. in Mathematics from the University of Minnesota (1992) and M.S./B.S. in Applied Mathematics from Fudan University (1985/1982). His work bridges theoretical stochastic analysis and applied domains like finance and insurance, with notable contributions to forward-backward SDEs and mean-field games. Research Highlights: Developed frameworks for stochastic control and backward SDEs in financial and insurance contexts. Advanced mean-field game models for limit order book dynamics and equilibrium analysis. Explored set-valued stochastic differential equations and their applications in risk management. Grants & Advising: Advised numerous graduate students in stochastic processes and mathematical finance. Research supported by NSF grants and industry collaborations.
W. Brent Lindquist is a Professor in the Department of Mathematics and Statistics at Texas Tech University, affiliated with the TTU Mathematical Finance Program. His contact details include office location in the Mathematics & Statistics building (Room 104), phone (+1 806 834 2348), and email brent.lindquist@ttu.edu. His research spans computational financial mathematics, porous media flow, neuroscience applications, and quantum electrodynamics. Key contributions include dynamic asset pricing with market microstructure integration, pore-scale flow modeling using 3D micro-tomography, automated neuron morphology identification, and QED computations for electron magnetic moments. Recent work emphasizes ESG factor incorporation into financial models. Analysis of 2023–2025 publications reveals a dominant focus on sustainable finance, particularly ESG-integrated option pricing and portfolio optimization. Methodologies include random forests for market microstructure analysis, skew random walks for volatility modeling, and Lévy processes for Bitcoin dynamics. Cross-cutting themes involve hedonic real estate models with ESG factors and unified asset pricing frameworks bridging classical finance theories.
