Gregory Crawford is Professor of Economics at the University of Zurich and co-founder/Director of the CEPR Research and Policy Network on Competition Policy. His research focuses on empirical industrial organization with applications to antitrust policy, media economics, and digital platforms. Research interests include vertical integration in media markets, digital platform regulation, bargaining models, and empirical methods for analyzing antitrust cases. Recent work examines media mergers, public service broadcasting, and competition in digital markets. Publications demonstrate sustained focus on media economics, with recent expansion into digital platform governance and consumer protection. Research combines structural econometrics with policy analysis. Dr. Crawford has served as Chief Economist at the Federal Communications Commission (FCC) and frequently contributes to antitrust policy discussions. He leads the CEPR Competition Policy Network and advises regulatory bodies worldwide.
Dr. Fletcher Lu is an Associate Professor in the Department of Information Technology at the Faculty of Business and Information Technology, Ontario Tech University. His academic journey includes a PhD in Artificial Intelligence (specializing in Machine Learning) and a Master of Mathematics in Scientific Computation, both from the University of Waterloo. He also holds a Bachelor of Math in Computer Science from the same institution. His postdoctoral research at the Canadian Institute of Chartered Accountants (CIAC) focused on insurance fraud detection. His education includes: PhD in Artificial Intelligence, University of Waterloo Master of Mathematics in Scientific Computation, University of Waterloo Bachelor of Math in Computer Science, University of Waterloo Dr. Lu’s research interests span health informatics, fraud detection (including cybercrime and insurance fraud), and the application of machine learning and numerical analysis to business and health challenges. He has developed mobile technology applications to improve youth fitness and pioneered adaptive fraud pattern analysis in health insurance data. His work intersects with cybersecurity, data analytics, and gaming, emphasizing real-world collaborations with organizations such as Manulife Financial, the Canadian Department of National Defence, and Medavie Blue Cross. His recent publications highlight trends in supply chain resilience, optimization methodologies, and disruption risk analysis. These include studies on EV infrastructure planning, stochastic distribution networks, and Benders decomposition strategies for reliable systems design. Dr. Lu has received significant awards, including: First place at CORS 2014 Practice Prize Third place at INFORMS 2013 interactive poster sessions His research has been funded by the National Science Foundation, Ontario Partnership for Innovation and Commercialization, and NSERC. He is affiliated with the Business Analytics & AI Research Group, contributing to decision-making systems and data-driven solutions for business and health sectors.
Dr. Amir Rastpour is an Associate Professor of Operations Management at the Faculty of Business and Information Technology (FBIT), Ontario Tech University. He holds a PhD in Operations Management from the University of Alberta School of Business (2015), followed by a postdoctoral fellowship at the Ivey Business School, Western University (2017). His research focuses on applying operations research methodologies to healthcare challenges, including queuing theory, stochastic models, and big data analytics for optimizing healthcare operations. Specific interests include demand prediction for medical facilities, emergency medical services optimization, and quasi-birth-death (QBD) process analysis in healthcare systems. Education: PhD in Operations Management, University of Alberta School of Business (2015) Postdoctoral Fellow, Ivey Business School, Western University (2017) Visiting PhD Student, University of Chicago Booth School of Business (2014) Research Interests: Dr. Rastpour’s work emphasizes healthcare applications, including demand forecasting for heart attack treatment facilities, emergency medical services optimization, and QBD process modeling. His methods include queuing theory, count regression models, and empirical analysis to address both strategic and operational challenges in healthcare delivery. Recent work explores machine learning for predicting patient wait times and dynamic programming for ambulance fleet management. Awards: Nominated for Ontario Tech University Student Choice Teaching Award (2019) INFORMS Health Applications Society Finalist for Best Student Paper (2015) CORS Best Student Paper in Open Category (2015) University of Alberta Scholarships and Awards (2009–2014) Teaching and Advising: Dr. Rastpour teaches statistics and operations management courses, emphasizing hands-on data analysis and decision-making skills. His teaching style includes real-world examples and interactive sessions to enhance student learning. He collaborates on grants focused on healthcare operations and supply chain resilience. Labs and Teams: He is affiliated with the Business Analytics & AI Research Group at FBIT, which bridges computational methods with business challenges in marketing and operations management. His work contributes to initiatives like AI in healthcare collaboration with Lakeridge Health.
Dr. Nader Azad is an Associate Professor of Operations Management at the Faculty of Business and Information Technology, Ontario Tech University. He specializes in supply chain risk management, logistics, and stochastic optimization. His work focuses on developing strategies to mitigate disruptions and enhance resilience in supply chains using game theory and large-scale optimization models. Education: PhD in Industrial Engineering (Amirkabir University of Technology), Postdoctoral Fellowship in Operations Management (McMaster University), MSc and BSc in Industrial Engineering (Iran University of Science & Technology). Research interests include supply chain risk mitigation, sustainable logistics, and disruption recovery. He holds a NSERC Discovery Grant ($100,000) for modeling disruption risks in supply networks. Notable publications appear in Transportation Science , Omega , and European Journal of Operations Research . Grants: NSERC Discovery Grant (2017–2022), FGSR Faculty Grant (2017–2018). Awards include IFER Postdoctoral Fellowship (2012–2013) and Top PhD Student (2008–2009). Labs/Groups: Member of the Business Analytics & AI Research Group, collaborating on data-driven decision-making in operations and marketing.
Dr. Muhammad Farid Ahmed is a Senior Lecturer in the Department of Economics and Public Policy at Imperial College London's Business School. He holds a Ph.D. in Economics from the University of Cambridge and an undergraduate degree in Economics (silver medalist) from Lahore University of Management Sciences. His professional experience includes teaching roles at the University of Oxford, University of Cambridge, and Lahore University of Management Sciences, focusing on Macroeconomics, Econometrics, and Quantitative Methods. Education: Ph.D. in Economics, University of Cambridge Bachelor of Economics (Silver Medalist), Lahore University of Management Sciences His research intertwines Macroeconomics, Finance, and Time-Series Analysis. He investigates asset price bubbles and household debt dynamics, with recent work exploring asymmetric impacts of debt on economic growth. His publications appear in The B.E. Journal of Macroeconomics and The European Journal of Finance . Farid is also advancing economics education research, developing pedagogical frameworks and assessment methods grounded in empirical research. His work emphasizes improving teaching methodologies through evidence-based practices.
Ryan T Ball is a Clinical Assistant Professor of Accounting at the University of Michigan's Ross School of Business. His research focuses on the interplay between high-frequency economic activities and low-frequency accounting information, with applications in macroeconomic forecasting, tax decision analysis, and debt market contracting. He holds a PhD and MBA from the University of North Carolina, along with engineering degrees from Ohio University. Education: PhD in Accounting, University of North Carolina (2008) MBA, University of North Carolina (2003) MS in Structural Engineering, Ohio University (1998) BS in Civil Engineering, Ohio University (1996) His research explores two primary areas: (1) mixed-data sampling methodologies for linking accounting metrics to macroeconomic trends, and (2) the contractual and valuation roles of accounting information in debt markets. Notable contributions include pioneering work on real-time earnings forecasting and the valuation implications of equity cross-listings. Teaching Excellence: Five-time recipient of the Ross Neary Teaching Award and first faculty member to win the University of Michigan Golden Apple Award (2016). Known for innovative pedagogical approaches, including viral TikTok educational content. Research Trends: Recent work emphasizes machine learning applications in panel data analysis, high-frequency financial nowcasting, and behavioral aspects of managerial decision-making. His 2024 paper on price-earnings ratio nowcasting exemplifies cutting-edge integration of econometric techniques with real-time data.
John W. Byers is a Professor in the Department of Computer Science at Boston University since 1999. He holds dual roles as Founding Chief Scientist and Board Member at Cogo Labs (founded in 2005 as Adverplex, Inc.), where he oversees technology platforms for online advertising and data science analytics. His research bridges theoretical computer networking with empirical studies of Internet platforms like Airbnb and Groupon. Education: Ph.D. in Computer Science from U.C. Berkeley. Research Focus: Byers explores algorithmic challenges in transport protocols, network architecture design, and the sharing economy's economic impacts. His work on platforms like Airbnb and Groupon examines reputation systems, user behavior, and market dynamics. Recent projects include liquid wireless networking for rural applications and optimizing ride-hailing fleet coordination. Publications: Over 50 peer-reviewed articles spanning network architecture (e.g., XIA), streaming systems (liquid transport protocols), and sharing economy analyses. Key themes include data-driven experimentation and cross-disciplinary solutions for evolving internet challenges. Industry Engagement: Cogo Labs incubates tech startups leveraging his research in algorithmic marketing and data analytics. Byers' work frequently bridges academia and industry, addressing real-world scalability and economic factors in digital ecosystems.
Alexandre Ziegler is a Senior Lecturer at the Department of Finance, University of Zurich, affiliated with the Faculty of Economics. He holds a Ph.D. in Finance from the University of St. Gallen (1998, Summa cum Laude), an MBA from Stanford Graduate School of Business (2000), and a Habilitation in Economics from the University of St. Gallen (2001). His research focuses on asset pricing, game theory, corporate finance, and labor economics, with notable contributions to climate policy impact on stock markets and political economy dynamics. He teaches Advanced Investments, Portfolio Management, and Backtesting methodologies. **Teaching:** Asset Management: Advanced Investments (2025/26) Portfolio Management Implementation (2025/26) Portfolio Management Theory (2025/26) Backtesting for Portfolio Management (2025/26) **Research Interests:** Climate responsibility and stock price reactions to policy shocks (e.g., U.S. elections) Corporate finance strategies under tax and trade policy changes Market anomalies and seasonal trading patterns (e.g., 'Sell in May') Risk management in catastrophe reinsurance and securitization **Publications:** Over 15 peer-reviewed articles across finance, economics, and interdisciplinary fields, including high-impact studies in Review of Corporate Finance Studies , Journal of Financial Economics , and Biophysical Journal .
Erik Hjalmarsson is a Professor at the University of Gothenburg , affiliated with the Centre for Finance . His research lies at the intersection of empirical finance and econometrics, with a strong focus on return predictability, algorithmic trading, and market microstructure. He has published extensively in top-tier journals such as the Journal of Finance and Journal of Financial and Quantitative Analysis . Research Interests: Return predictability using long-horizon regressions Algorithmic trading and its impact on market volatility and liquidity Pairs trading and co-movement in equity prices Portfolio choice under skewness and long-run return dynamics Tax policy effects on investor behavior and capital gains His recent work includes the widely cited paper "Nonstandard Errors" (2024), co-authored with over 100 researchers, which investigates the reliability of standard errors in multi-analyst studies. Another notable publication, "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market" (2015), explores the role of algorithmic trading in FX markets. Scientific Impact: His SSRN profile shows over 31,000 total citations and a top 2,151 ranking in total downloads, reflecting significant academic influence.
Jules van Binsbergen is the Anthony L. Davis Director of the Joseph H. Lauder Institute for Management and International Studies and holds the Lauder Chair Professorship at the University of Pennsylvania. He is also the Nippon Life Professor of Finance at The Wharton School and currently a Visiting Professor at MIT Sloan School of Management. His research focuses on asset pricing, financial markets' macroeconomic interactions, and financial intermediaries' performance. He earned a PhD in Finance from Duke University and an MA in Financial Econometrics from Tilburg University, with an undergraduate degree in Jazz Piano from The Hague's Royal Conservatory. Prof. van Binsbergen’s work bridges theoretical and empirical finance. His current research explores the dynamics between financial systems and broader economic trends, including monetary policy impacts, environmental risks, and mutual fund management strategies. He co-hosts the podcast All Else Equal: Making Better Decisions and is affiliated with the NBER and CEPR as a research scholar. His academic trajectory includes teaching roles at Stanford Graduate School of Business and Northwestern’s Kellogg School. Beyond research, he addresses practical finance challenges such as risk-free rate calculation methodologies and the valuation of long-term liabilities. His contributions span asset pricing models, corporate finance, and the evaluation of financial professionals’ skill and incentives. Prof. van Binsbergen’s interdisciplinary approach integrates econometric rigor with real-world financial phenomena, emphasizing both theoretical innovation and policy-relevant insights.
Mingli Chen is an Associate Professor of Economics at the University of Warwick’s Department of Economics. She holds affiliations including Turing Fellow at the Alan Turing Institute, External Fellow at the Centre for Panel Data Analysis (University of York), and Warwick-China Coordinator. Her research focuses on econometrics, machine learning, time series analysis, financial econometrics, and empirical industrial organization. She has served as an Associate Editor for the Journal of Econometrics since 2024 and organized workshops on data science and network analysis. Education: Ph.D. in Economics from Boston University (2015), B.A. in Information and Computing Science from Shanghai University (2009). She has held visiting positions at Stanford University, UC Berkeley, and the Federal Reserve Bank of Boston. Research Interests include high-dimensional econometrics, panel data models, social networks, quantile regression, and the integration of AI with econometrics. Key publications cover topics like quantile graphical models for systemic risk, latent panel quantile regression in asset pricing, and sparse β-models for network analysis. Awards include the International Partnerships Fund (2023), Turing PDRA Award (2020), and Co-Winner of the LABOUR Prize (2017). She advises Ph.D. students at Warwick and Cambridge, with placements at leading institutions like the University of Tokyo. Grants include leadership in UK-China partnerships and the Turing Institute. Teaching focuses on advanced econometrics at the Ph.D. level, including causal inference and machine learning. She co-organizes workshops and serves on conference committees, emphasizing data science and policy applications.
Afrooz Jalilzadeh is an Assistant Professor in the Department of Systems and Industrial Engineering at the University of Arizona, part of the College of Engineering. She is also a member of the Applied Mathematics and Statistics Graduate Interdisciplinary Programs (GIDP), highlighting her strong cross-disciplinary research profile. She leads the Optimization and Mathematical Analysis (OPTIMA) Lab, which focuses on algorithmic innovation for stochastic optimization and variational problems. PhD in Industrial Engineering and Operations Research, The Pennsylvania State University BS in Mathematics, University of Tehran, Iran Her research lies at the intersection of stochastic optimization , variational inequalities , and machine learning , with applications in game theory, healthcare, and power systems. She develops and analyzes algorithms such as stochastic approximation, primal-dual methods, and variance-reduced schemes to solve complex minimax and equilibrium problems. Her work emphasizes theoretical convergence guarantees and computational efficiency. The recent publications show a strong trend in nonconvex-concave saddle-point problems , stochastic Nash games , and projection-free optimization . These are central to modern machine learning and adversarial training. Keywords across her work include stochastic approximation, accelerated methods, risk aversion, and distributed computing, indicating a deep engagement with both theoretical and applied aspects of optimization. Her scientific recognition includes: Teacher of the Year, College of Engineering, University of Arizona (Spring 2022) Gerald J. Swanson Prize for Teaching Excellence NSF Grant: Generalized Stochastic Nash Equilibrium Framework James E. Marley Graduate Fellowship Max and Joan Schlienger Graduate Scholarship Third Place in INFORMS Poster Competition (2018) University Graduate Fellowship, Penn State (2015) H. Marcus Dean’s Chair Scholarship, Penn State (2015) She actively advises students and researchers in her OPTIMA Lab, with multiple publications co-authored with graduate students. She has secured competitive grants, including an NSF award, supporting her research group. Her lab seeks students with strong mathematical and coding skills (MATLAB/Python) for PhD-level research in optimization and mathematical analysis. The OPTIMA Lab conducts cutting-edge research in algorithm design for stochastic variational inequalities , Nash equilibrium computation , and minimax optimization . The lab emphasizes theoretical rigor and practical implementation, with applications spanning machine learning, healthcare, and energy systems. It has published in top venues such as NeurIPS, ACM TOMACS, and Mathematical Programming.
Dr. Xiaoyu Xia is a Lecturer (equivalent to Assistant Professor in North America) in Cybersecurity & Software Systems at RMIT University's School of Computing Technologies. He received his PhD with the prestigious Alfred Deakin Medal from Deakin University, Australia, and has established himself as a leading researcher in distributed systems and cybersecurity with over 50 peer-reviewed publications in top-tier venues including IEEE S&P, ACM WWW, and IEEE Transactions. Dr. Xia's research spans critical areas at the intersection of computing and security: System Privacy and Security Distributed Systems and Edge Computing AI Privacy and Machine Learning Systems Sustainable Computing Cybersecurity and Privacy-Preserving Technologies His recent work demonstrates a clear trajectory toward developing practical privacy-preserving frameworks for emerging technologies, particularly in edge computing environments and large language models. Dr. Xia has made significant contributions to machine unlearning, secure data management in distributed systems, and energy-efficient edge computing solutions that balance performance with sustainability concerns. Dr. Xia has received notable recognition for his scholarly impact: World's Top 2% Scientists by Stanford University (2022-2024) Alfred Deakin Medal for PhD research excellence (2021) Teaching Excellence Award from Swinburne University of Technology (2021) As an active researcher, Dr. Xia currently leads multiple funded projects including an ARC Discovery Project grant worth over $500,000 for developing privacy-aware intelligent digital twins for secure critical infrastructures. He is open to supervising motivated PhD students with interests in system security and privacy, and distributed ML systems. Dr. Xia serves the academic community through editorial roles as Associate Editor for IEEE Transactions on Dependable and Secure Computing and as a Review Board Member for IEEE Transactions on Parallel and Distributed Systems, and regularly participates in program committees for major conferences including ACM WWW and IEEE ICDCS.
Sancho Salcedo Sanz is a Full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department and the GHEODE Research Group. His work focuses on applying machine learning and optimization techniques to energy systems, climate science, and environmental modeling. He holds PhDs from Universidad Complutense de Madrid (2019) and Universidad Carlos III de Madrid (2002). Key research interests include deep learning for energy price prediction, spatio-temporal climate analysis, and hybrid models for renewable energy forecasting. His GHEODE group develops optimization algorithms for network design and distributed systems. Recent publications highlight advancements in extreme weather prediction, smart grid optimization, and explainable AI for environmental monitoring. He has pioneered methodologies like Autoencoder-based flow analogues for heatwave reconstruction and multi-method ensembles for energy demand modeling. Labs/Teams: Leader of the GHEODE Group, specializing in modern heuristics and network design. Collaborates extensively on interdisciplinary projects combining AI with environmental and engineering applications.
Daniel N. Treku is an Assistant Teaching Professor at The Business School, Worcester Polytechnic Institute (WPI). He specializes in blockchain technology, data analytics, AI, and fintech, focusing on financial inclusivity and social good. His research bridges network technologies, digital platforms, and ESG principles. Education: Ph.D. in Business Admin. (Information Systems), University of Texas Rio Grande Valley, 2022 M.Sc. in Management Information Systems, Ghana Institute of Management and Public Administration, 2015 B.Sc. in Physics, Kwame Nkrumah University of Science and Technology, Ghana, 2007 His research explores blockchain applications in supply chains, DeFi, and energy transitions, alongside AI ethics and digital divide solutions. Recent work addresses cryptocurrency market dynamics and social media's role in financial decision-making. Professional Affiliations: Association for Information Systems INFORMS: Information Systems Society INFORMS: Analytics Society INFORMS: College on Artificial Intelligence He co-authored projects like blockchain frameworks for food security and advised state legislative leaders on blockchain adoption. His work emphasizes sustainable development goals, including climate technology and financial equity.
