Alan M. Taylor is a prominent Research Fellow at National Bureau of Economic Research (NBER) and Centre for Economic Policy Research (CEPR) . He holds a permanent affiliation with Columbia University in the United States, where he contributes to economics research. His research spans financial history , macroeconomic policy , international trade , and capital market dynamics . Key themes include credit cycles , monetary policy , economic crises , and historical financial systems . He has extensively analyzed the trilemma of exchange rates, monetary policy, and capital mobility, and pioneered work on long-term rate of return data across capital assets. His recent publications focus on financial stability , credit booms , and macroeconomic consequences of pandemics . Collaborative works with economists like Òscar Jordà, Moritz Schularick, and Kevin H. O'Rourke demonstrate interdisciplinary engagement. His empirical methodology emphasizes local projections , historical econometrics , and long-run equilibrium modeling in international finance.
Peter Kondor is an Assistant Professor affiliated with the London School of Economics & Political Science (LSE) and Central European University (CEU). His research focuses on finance, asset pricing, liquidity risk, market microstructure, and arbitrage dynamics. Research Interests: Asset pricing with heterogeneous agents Liquidity risk and intermediary capital Over-the-counter market structures Behavioral finance and sentiment analysis Global financial cycles and investment waves Information diffusion and market stability Key Publications Trends: 2011-2025: Explores causal inference in asset pricing, hedge fund impacts on idiosyncratic risk, and liquidity risk dynamics. 2018: Investigates arbitrage capital and liquidity risk in global markets. 2020-2025: Analyzes rational sentiments, narrative momentum, and aggregate earnings.
Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.
Amir Sufi is the Bruce Lindsay Distinguished Service Professor of Economics and Public Policy at the University of Chicago Booth School of Business, where he has been a faculty member since 2005. He serves as a Research Associate at the National Bureau of Economic Research and co-director of its Corporate Finance Program. Bachelor’s Degree, Walsh School of Foreign Service, Georgetown University (1999) PhD in Economics, Massachusetts Institute of Technology (2005) His research focuses on finance , macroeconomics , and corporate finance . Key areas include household debt dynamics , credit market structure , income inequality , and interest rate impacts on productivity growth . Recent work examines customer capital investment and low-interest rate effects on market concentration . Selected scientific awards include the 2017 Fischer Black Prize, Econometric Society Fellow (2022), and American Academy of Arts and Sciences Fellow (2024). His peer-reviewed publications and working papers span topics from syndicated loans to global household debt cycles , with notable contributions to understanding credit-driven business cycles and government-led consumer credit programs . He teaches courses in leveraged finance , private credit , and corporate restructuring .
Robert C. Merton is the School of Management Distinguished Professor of Finance at MIT Sloan School of Management and John and Natty McArthur University Professor Emeritus at Harvard University. He holds a PhD in Economics from MIT (1970), with prior roles including George Fisher Baker Professor at Harvard Business School and J.C. Penney Professor of Management at MIT Sloan. His work revolutionized finance through the Black-Scholes-Merton options pricing model, earning the 1997 Nobel Prize in Economics. Current research focuses on lifecycle investing, systemic risk measurement, and financial innovation. Education: BS in Engineering Mathematics (Columbia), MS in Applied Mathematics (Caltech), PhD in Economics (MIT). Affiliated with MIT’s Golub Center for Finance and Policy and Harvard initiatives. Recognized via awards from CME Group, World Federation of Exchanges, and Risk magazine. Key publications include Continuous-Time Finance and co-authored works on financial systems and innovation. Research emphasizes translating theory into practice, with recent articles addressing volatility forecasting, trust in lending, bankruptcy frameworks, and performance fee valuation. A prolific academic leader, he advises on policy and systemic risk while maintaining ties to MIT’s finance community through roles like Killian Award recipient (2021).
George Skiadopoulos is a Professor of Finance at the University of Piraeus (Department of Banking and Financial Management) and Queen Mary University of London (School of Economics and Finance). He serves as Director of the Institute of Finance and Financial Regulation (IFFR) and holds an Honorary Senior Visiting Fellowship at Bayes Business School, City University of London. His research focuses on asset pricing, commodities, financial derivatives, climate finance, and ESG integration. He has published in prestigious journals like Management Science and Journal of Financial and Quantitative Analysis, and his work influences policy at institutions like the European Securities Markets Authority (ESMA). Education: PhD in Finance from the University of Warwick, M.Sc. in Mathematical Economics from LSE, and a Ptychion in Economics from Athens University of Economics and Business. He has advised financial institutions globally and received grants from the Chicago Mercantile Exchange and others. His notable award is the 2018 German Finance Association best paper prize for work on transaction costs and stock returns. He has also contributed to executive training and policy discussions on climate-related financial risks.
Jesper Rangvid is a Professor of Finance and Director of the Pension Research Centre (PeRCent) at Copenhagen Business School (CBS), where he also serves as Associate Dean of the Executive MBA program . His research focuses on financial markets, macroeconomics, financial crises, household finance, and mutual funds. He has authored influential works such as How Low Interest Rates Change the World (Oxford University Press, 2025) and From Main Street to Wall Street (Oxford University Press, 2021), exploring topics like interest rate trends, economic growth, and policy impacts. He chairs the Council for Return Expectations and holds board positions at Formuepleje, Advantage Investment Partners, and Finansiel Stabilitet, among others. His advisory roles include Levring & Levring and projects for Danish institutions like Forenet Kredit and BankInvest. Recent research highlights include analyzing Denmark’s pension system reforms, fiscal-monetary policy interactions, and the implications of low interest rates on global economies. Rangvid’s publications combine rigorous academic analysis with policy relevance, addressing topics like dividend predictability, economic growth’s impact on asset returns, and the behavior of professional investors. His work often bridges theoretical frameworks with empirical data, offering insights into macro-financial linkages and policy design.
Rajesh Karki is a Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan’s College of Engineering. He holds a B.E., M.Sc., and Ph.D. in related fields. His research focuses on power system reliability, renewable energy integration, and microgrid resilience, with particular emphasis on addressing challenges posed by extreme weather, cyber threats, and decarbonization targets. Dr. Karki’s work spans theoretical modeling, probabilistic analysis, and practical implementation strategies for smart grids, energy storage systems, and distributed generation. His educational background includes advanced degrees in electrical engineering, complemented by professional engineering licensure (P.Eng.). His research has explored diverse topics such as wind energy curtailment mitigation, energy storage optimization, and demand response mechanisms in developing economies like Nepal. He has authored numerous peer-reviewed publications on grid resilience, reliability economics, and cyber-physical system security. Key themes in his work include: (1) quantifying the reliability value of energy storage in active distribution systems, (2) modeling cyber-physical threats to microgrids, and (3) developing frameworks for extreme weather-resilient infrastructure. Despite the volume of his publications (over 50 articles), no specific awards or grants are explicitly listed in the provided materials. His research often intersects technical, economic, and policy dimensions of sustainable energy systems.
Sung Hoon Choi is an Assistant Professor in the Department of Economics at the University of Connecticut, part of the College of Liberal Arts and Sciences. His research focuses on developing econometric tools for analyzing big data, machine learning applications, and forecasting using high-dimensional panel datasets. He holds a Ph.D. in Economics from Rutgers University (2021), an M.A. in Applied Statistics from Yonsei University (2016), and a B.A. in Statistics from the University of California, Berkeley (2013). His research interests include econometric theory, financial econometrics, and high-frequency data analysis. Notable areas of concentration are large panel data and factor models, high-dimensional data techniques, and volatility matrix analysis. He teaches courses such as Econometrics I and III for Ph.D. students, and Python programming for economists at undergraduate and master's levels. Recent publications focus on volatility modeling using factor structures, high-frequency financial data, and panel data econometrics. His work addresses challenges in structural information analysis, standard errors for clustered panels, and feasible generalized least squares methods. He collaborates with researchers like Donggyu Kim and Jushan Bai, contributing to leading journals like the Journal of Econometrics and Econometric Theory .
Xinjie (Cynthia) Ma is an Assistant Professor in the Department of Accounting at the University of Iowa's Tippie College of Business. Previously, she held a Visiting Assistant Professor position at the University of Iowa in 2025. Her research bridges financial accounting with advanced technologies, focusing on corporate disclosure mechanisms, human capital valuation, and AI/NLP applications in capital markets. She earned her PhD in Accounting from Temple University in 2021. Research Expertise : Financial accounting, CSR strategy alignment, human capital analytics, and machine learning applications Publications : 2023 Review of Accounting Studies paper on CSR-performance alignment, 2022 The Accounting Review article on text-based investment opportunity sets Teaching : Instructed graduate Financial Statement Analysis at National University of Singapore (2021-2025), undergraduate Managerial Accounting at Temple University (2019) Academic Affiliation : Tippie College of Business Her recent work analyzes how textual disclosures in 10-K filings inform investment decisions and explores labor demand dynamics through real-time market data. Currently, she's developing machine learning models to predict startup innovation potential and examining managerial communication patterns in earnings calls. Email: xinjie-ma@uiowa.edu
Seth Blumsack is a Professor at the Pennsylvania State University in the Department of Energy and Mineral Engineering and serves as Director of the Center for Energy Law and Policy . He holds an Adjunct Research Professor position at the Carnegie Mellon Electricity Industry Center and is affiliated with the Santa Fe Institute as an External Faculty member. His research spans energy economics , power grid reliability , and complex infrastructure networks . Key projects include: Interdependent natural gas and electricity systems analysis Governance of regional transmission organizations Smart grid consumer behavior studies Power grid reliability tools development He has secured funding from the U.S. National Science Foundation , Department of Energy , Environmental Protection Agency , and private industry. His Best paper award at Hawai’i International Conference on System Sciences (2011) and John T. Ryan, Jr. Fellowship (2011-17) highlight his scientific recognition. Publications emphasize electricity market deregulation , energy infrastructure resilience , and consumer response to smart grid technologies . His work has been cited in major media outlets like The New York Times and The Los Angeles Times , and he has consulted for National Renewable Energy Laboratory , U.S. Department of Energy , and other industry stakeholders.
Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.
Robert Kosowski is Professor of Finance and Head of the Department of Finance at Imperial College Business School, Imperial College London. He holds a Ph.D. from London School of Economics, M.Sc. in Economics from London School of Economics, and B.A./M.A. in Economics from Trinity College, Cambridge University. His research examines asset management, risk management, machine learning applications in finance, hedge funds, and performance measurement. He has published in top finance journals including Journal of Finance, Journal of Financial Economics, and Review of Financial Studies. Awards include European Finance Association Best Paper Award (2007), four INQUIRE best paper awards, and British Academy Mid-Career Fellowship (2011-2012). Recent publications focus on machine learning in finance, regulatory impacts on funds, and innovative risk management approaches. Articles demonstrate consistent methodological rigor across quantitative finance topics with practical applications for investment management. Professor Kosowski is co-author of 'Principles of Financial Engineering' and directs executive education programs in Risk Management. He has industry experience as Head of Quantitative Research at Unigestion and previously worked at Goldman Sachs and Deutsche Bank.
Zhe Liu is an Assistant Professor in the Analytics & Operations group at Imperial College Business School, where he also serves as PhD Director. He holds a PhD in Operations Management from Columbia Business School and a BS in Industrial Engineering from Tsinghua University. His research focuses on revenue management and supply chain optimization, with specialized interests in sharing economy platforms and multi-sourcing strategies. Liu's work examines operational challenges in modern business environments through mathematical modeling, including queueing systems, pricing optimization, and risk management in volatile supply chains. His publications demonstrate consistent themes in platform operations, stochastic optimization, and behavioral interactions within multi-agent systems. Recognized with numerous honors, Liu received the 1st Place Service Science Best Cluster Paper Award (2024), 2nd Place CSAMSE Best Paper Award (2024), and was a finalist in the George Nicholson Student Paper Competition. He actively mentors PhD students and serves as judge for international paper competitions and conference program committees.
Michael O'Dea is a Senior Lecturer in the Department of Computer Science at the University of York, United Kingdom. He has held previous academic positions at York St John University, Beijing University of Technology, University of Hull, and Waikato Institute of Technology, bringing extensive international experience in computer science education. He is actively engaged in pedagogical scholarship and leadership in higher education innovation. Senior Lecturer, Department of Computer Science, University of York Senior Lecturer in Computer Science, York St John University Lecturer in Software Engineering, Beijing University of Technology, China Lecturer in Computer Science, University of Hull Lecturer in Information Technology, Waikato Institute of Technology, NZ Dr. O'Dea earned his Ed.D. in Computer Based Learning from the University of Leeds. His research centers on the integration of artificial intelligence into educational practices, with a strong emphasis on AI literacy, the effectiveness of generative AI in teaching and learning, and the evolving landscape of technology acceptance in higher education. He investigates how AI tools can enhance student learning, faculty development, and institutional policy. His recent publications span topics such as AI literacy assessment, the future of online and blended learning, the application of machine learning in earthquake prediction, and international study abroad effectiveness. These works reflect a broad interdisciplinary approach, combining computer science, educational theory, and policy analysis. His scholarship is increasingly focused on the transformative potential of generative AI in academic settings, as evidenced by his leadership in special journal issues and funded research projects. Dr. O'Dea holds significant editorial responsibilities as Associate Editor and Lead Guest Editor for the Journal of University Teaching and Learning Practice and as Guest Editor for a special issue in Education Sciences on generative-AI-enhanced learning. He is also an Invited External Academic Affiliate at the King's Institute for Artificial Intelligence, King's College London. Associate Editor - Special Issues, Journal of University Teaching and Learning Practice Lead Guest Editor, Special Issue on Technology Acceptance Models, JUTLP (2024) Guest Editor, Special Issue on Generative-AI-Enhanced Learning, Education Sciences Principal Investigator, QAA Collaborative Enhancement Project on Graduate Attributes in the Era of GenAI (2025) He has delivered numerous invited talks and workshops at institutions such as the University of York, Queen Mary University of London, and international conferences including the Academy of Management and the International Conference on Artificial Intelligence in Education. His work bridges research, practice, and policy in higher education, with a strong commitment to inclusive and innovative teaching methodologies.
