Mungo Wilson is a Professor of Finance at the Saïd Business School , University of Oxford, and an associate member of the Oxford-Man Institute of Quantitative Finance. He holds a PPE degree from Oxford, an MSc in Economics from LSE, and a PhD in Economics from Harvard. His research focuses on asset pricing, credit risk, mutual funds, and macroeconomic influences on financial markets. He has published extensively in top journals and engages actively with industry through board roles and advisory work. Education : Bachelor's in Philosophy, Politics & Economics (PPE), University of Oxford MSc Economics, London School of Economics PhD in Economics, Harvard University Research Interests : Assesses how macroeconomic conditions and credit risk shape asset prices Analyzes mutual fund behavior and market information transmission mechanisms Explores political dynamics' impact on financial markets His professional engagements include board memberships with funds like EA Asia Absolute Return Master Fund and advisory roles at Broadwell Capital. Teaching focuses on microeconomics for MBA programs, covering game theory, market structures, and price discrimination. Advisory & Grants : Supervises doctoral students at Saïd Business School and serves on programme committees for major finance conferences (e.g., European Finance Association, CEPR Annual Spring Symposium). Awards : While no specific prizes are listed, his impactful research has been widely cited and presented globally.
Charles M. Jones is a Professor of Finance at Columbia Business School, Columbia University, with an extensive publication record spanning several decades. His research focuses on market microstructure, high-frequency trading, short selling, retail investor behavior, and stock market liquidity. His work has appeared in top finance journals including the Journal of Finance, with his most recent publication "Nonstandard Errors" appearing in the June 2024 issue. Professor Jones's research interests center on understanding how financial markets function at a granular level. His work on market microstructure examines the mechanics of price formation, liquidity provision, and the impact of trading technologies on market quality. His research on short selling has been particularly influential, investigating when short sellers trade, what information they possess, and how regulatory interventions like short sale bans affect market functioning. His more recent work has explored the rise of retail trading through platforms like Reddit and its implications for price discovery, particularly during events like the GameStop phenomenon and the COVID-19 pandemic. Analysis of his publication trends reveals a consistent focus on market efficiency and price discovery mechanisms, with increasing attention to retail investor behavior in recent years. His work spans both theoretical modeling and empirical analysis of market data, often utilizing high-frequency datasets to examine intraday trading patterns. The interdisciplinary nature of his research bridges finance, economics, and information science, contributing to both academic understanding and practical market regulation. Professor Jones has collaborated extensively with researchers across the globe, as evidenced by his numerous co-authored papers with scholars from institutions worldwide. His work has significant implications for market regulators seeking to understand the impact of technological changes and regulatory interventions on market quality and efficiency.
Jean C. Oi serves as the William Haas Professor of Chinese Politics in Stanford University's Department of Political Science and holds multiple leadership positions including Senior Fellow at the Freeman Spogli Institute for International Studies, Director of the China Program, and Lee Shau Kee Director of the Stanford Center at Peking University. Professor Oi earned her PhD from the University of Michigan and previously taught at Lehigh University and Harvard University before joining Stanford in 1997. Her research centers on comparative politics with specialized expertise in Chinese political economy and reform processes in transitional systems. She has made seminal contributions to understanding rural politics in China, introducing the influential concept of 'local state corporatism' to describe China's development model in her award-winning book Rural China Takes Off (1999). Her current research examines China's Belt and Road Initiative, fiscal politics and central-local relations, and structural change in China. Professor Oi maintains an active publication record with works spanning four decades, including her most recent 2025 article 'A Perfect Storm: Fiscal Discipline, COVID, and Local Government Debt in China' which analyzes how pandemic-era policies exposed China's fiscal vulnerabilities. Choice Outstanding Academic Title (1999) for Rural China Takes Off Professor Oi leads significant collaborative research initiatives, including directing the Stanford China Program at the Walter H. Shorenstein Asia-Pacific Research Center. Her fieldwork with Chinese scholars has examined rural community organization, public goods provision, and fiscal pressures from rapid urbanization, culminating in works like Challenges in the Process of China's Urbanization (2017).
Melvyn R.W. Hamstra is a Full Professor at IÉSEG School of Management in France since 2023. His academic career includes roles as Associate Professor (2021–2023) and Assistant Professor (2020–2021) at IÉSEG, as well as Assistant Professor positions at the University of Maastricht (2015–2020) and the University of Amsterdam (2013–2015). He holds a Ph.D. in Behavioural and Social Sciences from the University of Groningen (2013), preceded by a Master’s (2009) and Bachelor’s (2007) in Psychology from the same institution. Research Interests : His work centers on organizational behavior, leadership, and employee motivation, with a strong emphasis on regulatory focus theory, employee voice dynamics, and knowledge management. Key themes include how leadership styles (e.g., promotion vs. prevention focus) influence team innovation, conflict resolution, and workplace morale. Publication Trends : Recent publications (2025–2024) explore topics such as gender bias in leadership evaluation, financial stress and counterproductive behavior, ethical conflict in teams, and the dual-edged role of humor in workplace negotiations. His longitudinal studies on religious engagement and life satisfaction, as well as theoretical models on knowledge withholding, highlight interdisciplinary breadth. Teaching : He contributes to bachelor and master’s programs at IÉSEG, focusing on leadership development, positive leadership, and organizational learning.
Michael Jong Kim is an Associate Professor at the Sauder School of Business, University of British Columbia, specializing in the Division of Operations and Logistics. His research focuses on dynamic programming, statistical learning, robust optimization, and the exploration vs exploitation trade-offs in sequential decision-making processes. BASc, M.Math, and PhD from the University of Toronto His work spans topics in stochastic optimization, supply chain dynamics, and information dissemination in uncertain environments. Publications highlight contributions to Bayesian inventory control, semi-Markovian system control, and variance regularization in optimization models. Dr. Kim teaches advanced business analytics courses, including Descriptive and Predictive Business Analytics and Advanced Predictive Business Analytics (MBAN) during the 2024-2025 academic year. He can be reached at mike.kim@sauder.ubc.ca or by phone at +1 604.822.8682.
Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science at Temple University’s Fox School of Business. He serves as the Founding Director of the Center for Business Analytics and Disruptive Technologies and has a secondary appointment in Information Systems. His roles include Ph.D. Program Concentration Director for Operations and Supply Chain Management. With over 240 publications, he ranks #1 globally in Information Systems Research publishing and holds a robotics patent. His research focuses on AI, blockchain, healthcare analytics, and cybersecurity, with notable contributions to digital transformation and operations management. Awards include the INFORMS ISS Distinguished Fellow and POMS Fellow. He advises major journals like Production and Operations Management and organizes conferences like POMS 2018. Media mentions span NYT, WSJ, and CBS. Education: University of Washington faculty Texas A&M University faculty Research Interests: Dr. Kumar explores AI applications, blockchain in fintech, healthcare system optimization, and supply chain analytics. He investigates cybersecurity’s operational impacts and data mining strategies. His work bridges theory and practice, addressing challenges in digital transformation and platform economies. Publications Trends: Recent work emphasizes digital platforms (e.g., blockchain adoption, telemedicine impact), organizational behavior (real-time feedback systems), and healthcare operations (personalized treatment algorithms). He frequently collaborates internationally, with studies appearing in Management Science, Information Systems Research, and Decision Sciences. Awards & Recognition: Changjiang Scholars Chair (China) ISB Visiting Professorship POMS Executive Editor Grants & Labs: Secured NIH grants for health analytics projects. Leads Temple’s analytics center fostering industry partnerships. Active in editorial roles (e.g., Production and Operations Management Journal Deputy Editor). Teaching: Offers courses in data science, optimization methods, and innovation entrepreneurship at graduate/undergraduate levels.
Sebastian Raisch is a Full Professor of Strategic Management at the Geneva School of Economics and Management (GSEM), University of Geneva, where he also serves as Director of the Executive MBA program. He is affiliated with the Institute of Management and holds a Ph.D. from the University of Geneva. His academic work is deeply rooted in understanding how organizations balance stability and change, particularly in the face of digital transformation. Research Interests: His primary areas of expertise include strategic management, organizational ambidexterity, organizational paradox, artificial intelligence in organizations, and digital transformation. His research investigates the dynamics of corporate growth and decline, strategic renewal, and the integration of human and artificial intelligence in organizational decision-making. He is especially interested in how firms navigate tensions between competing demands such as exploration and exploitation, automation and augmentation, and stability and innovation. Publications and Trends: His recent publications (2023–2025) show a strong shift toward the role of artificial intelligence in management, with a focus on hybrid human-AI systems, ethical AI, and the strategic implications of AI adoption. Earlier works center on ambidexterity, paradox theory, and corporate turnaround, establishing him as a leading scholar in these domains. Scientific Awards: Strategic Management Society’s Best PhD Paper Award Emerald Publishing Group’s Citation of Excellence Award Journal of Management’s Scholarly Impact Award Editorial and Professional Service: Sebastian Raisch serves as Associate Editor at the Academy of Management Review and sits on the editorial boards of the Academy of Management Journal and the Strategic Management Journal . He has advised numerous doctoral students and supervised research projects on organizational innovation and digital strategy, though specific names are not listed in the provided materials. He has not received explicit mention of research grants, but his extensive publication record suggests active research funding. Labs and Research Teams: While no formal lab is mentioned, Raisch leads or contributes to research initiatives within the Institute of Management at GSEM, particularly in strategic management and digital innovation. He collaborates with scholars across Europe and the U.S., including notable co-authors like J. Schad, S. Krakowski, and M. Tushman.
Wooyong Lee is a Lecturer in the Economics Discipline Group at the UTS Business School, University of Technology Sydney. He holds a PhD in Economics from the University of Chicago (2020), an MS in Statistics from the University of British Columbia (2014), and a BA in Economics and Statistics from Korea University (2012). His research focuses on econometrics and applied microeconomics, specializing in panel data methods, difference-in-differences frameworks, and dynamic models. He has developed methodologies addressing spillover effects in staggered DiD designs and partial identification in heterogeneous coefficient models. His work applies to real-world issues like lifecycle earnings dynamics and policy evaluation. Lee teaches econometrics at undergraduate and postgraduate levels and supervises research students. His publications appear in venues such as Statistical Inference for Stochastic Processes and peer-reviewed working papers. Research interests emphasize causal inference techniques, with contributions to handling unobserved heterogeneity and measurement errors in economic data. Ongoing work explores dynamic treatment choice models where treatment decisions respond to outcome shocks, challenging traditional parallel trends assumptions.
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.
Jan Herbst is a Professor of Music at the University of Huddersfield and Director of the Centre for Research in Music and its Technologies. His academic journey includes multiple doctorates (PhD, Dr. habil.) from Leuphana University Lüneburg and research expertise in popular music studies, music production, and systematic musicology. He leads AHRC-funded projects such as 'Heaviness in Metal Music Production' and 'Songwriting Camps in the 21st Century.' His work bridges practical music production and theoretical research, with over 80 publications including books like The Cambridge Companion to Metal Music and Heaviness in Metal Music Production . Herbst’s research focuses on metal music aesthetics, record production techniques, and the cultural dimensions of music technology. He has held roles at German and Swiss universities, including teaching guitar performance and music production. His editorial roles include the Cambridge Companions series and journals like Metal Music Studies . Current projects explore blockchain in music, gear acquisition syndrome, and extreme metal vocal techniques. His academic affiliations include the IASPM UK & I executive committee and editorial boards for major musicology publications. Herbst actively engages with industry through collaborations with producers and performers, maintaining a balance between academic rigor and practical music creation.
Kimon Fountoulakis is an Associate Professor at the University of Waterloo. His research focuses on Machine Learning on Graphs and Numerical Optimization, with a strong emphasis on algorithmic methods for graph-structured data. He holds a Ph.D. from The University of Edinburgh (2015), an M.Sc. from The University of Edinburgh (2010), and a B.Sc. from Athens University of Economics and Business (2009). His work spans theoretical foundations and practical applications in graph algorithms, optimization, and machine learning. Research interests include graph neural networks, local graph clustering algorithms, and algorithmic reasoning. His contributions address challenges in graph representation learning, message-passing architectures, and scalable optimization methods. Notable themes in his publications include improving counting abilities of vision-language models, analyzing graph convolutions, and developing flow-based clustering techniques with statistical guarantees. His work often bridges theory and practice, with applications in network analysis, pandemic containment strategies, and high-performance computing. While no specific grants or awards are listed, his research demonstrates significant contributions to graph-based machine learning and optimization. He maintains a research group at the University of Waterloo, with a focus on developing open-source tools and frameworks for graph algorithms. His lab’s work emphasizes local graph clustering methods and their scalability in real-world networks.
Dr Alvin Tan is a Lecturer in the School of Advertising, Marketing & PR at Queensland University of Technology (QUT). His research focuses on SME internationalisation, export readiness, and managerial decision-making in cross-border contexts. He holds a PhD in International Business from the University of Queensland (2012) and has over a decade of academic experience, including roles in teaching units like AMB110 Internationalisation. Education: Doctor of Philosophy (International Business), University of Queensland (2012) Bachelor of Business (Honours), Queensland University of Technology Bachelor of Commerce (International Business), University of Tasmania Research Interests: Small-medium enterprise internationalisation drivers, export decision rigidity, managerial commitment in export strategies, and inward internationalisation dynamics. His work emphasizes the pre-internationalisation phase and has contributed to frameworks like the export readiness index. Awards: QUT Vice Chancellor's Performance Award for Teaching Excellence (2008) Accredited Senior Fellow of the Higher Education Academy (SFHEA) Teaching and Supervision: Specializes in global business strategy, international marketing, and supervises postgraduate students on SME internationalisation topics.
Anders Damgaard is an Associate Professor and Head of BSc studies at the Department of Environmental and Resource Engineering (DTU Sustain), Technical University of Denmark. His research focuses on environmental assessment methodologies, particularly Life Cycle Assessment (LCA), and their application to waste management systems, resource recovery, and policy development. He leads the development of LCA models like EASEWASTE/EASETECH and collaborates with institutions such as the Danish EPA and Nordic Council of Ministers. Key research areas include carbon footprints of waste treatment, textile waste composition, and additive impacts in plastics recycling. Education: Not explicitly stated in provided texts. Research Interests: Waste management optimization, circular economy frameworks, sustainable technologies, and policy-driven environmental assessments. His recent publications address topics such as carbon footprints of sewage sludge treatments, Nordic textile waste composition, and challenges in plastic additive inclusion in LCA studies. He supervises PhD students in projects involving lifecycle modeling, construction waste recycling, and consumer practices in circular economies. As part of DTU Sustain, he contributes to interdisciplinary teams advancing sustainable resource management and policy solutions.
Chen Lian is an Assistant Professor in the Department of Economics at UC Berkeley. Holding a PhD from MIT, their research bridges macroeconomics, behavioral economics, and finance, with a focus on bounded rationality, monetary theory, and macro-finance interactions. Education: PhD in Economics, MIT Chen’s work explores how incomplete information and behavioral biases shape macroeconomic outcomes. Key themes include inflation effects on households, fiscal-monetary policy interactions, and financial stress dynamics. They employ heterogeneous-agent models and analyze how micro-level shocks propagate through the economy. Their publications and working papers address topics like credit cycles, demand shock propagation, and the psychological underpinnings of economic decisions. Papers such as Low Interest Rates and Risk Taking (2019) and Confidence and the Propagation of Demand Shocks (2022) highlight their interdisciplinary approach.
Dr. Shan Lu is a Lecturer in Finance at the Department of Accounting and Finance, Kent Business School, University of Kent, since August 2021. He previously held positions at the University of Aberdeen and the University of Bradford and earned his PhD from the University of Aberdeen. Research interests: Financial derivatives, option pricing, and quantitative finance. His work focuses on volatility modeling, risk-neutral density estimation, and computational finance, with publications in journals such as the European Journal of Finance, Journal of Futures Markets, and Economics Letters. Teaching: Covers financial markets, derivatives, econometrics, and quantitative methods at undergraduate and postgraduate levels. Scientific awards: Fellow (FHEA) of Higher Education Academy Advising: Offers PhD supervision in topics aligned with his research interests, including financial derivatives and quantitative finance. He emphasizes collaboration on research ideas directly related to his expertise. Publications: Recent work explores volatility dynamics in VIX/VXX options, risk-neutral density extraction, and implied volatility forecasting, leveraging computational methods and empirical finance techniques.
