Archives & Presentations

Speaker Bios

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• Keywan Rasekhschaffe

  • Keywan Rasekhschaffe, PhD, is a portfolio manager at Quantbot Technologies, LP. Prior to joining Quantbot he was a senior quantitative strategist at Gresham Investment Management LLC and he oversaw quantitative research at System Two Advisors LP. He was a Chazen Visiting Scholar at Columbia Business School and earned his Ph.D. at the University of Lugano, and received his MBA from the University of Oxford. He holds a joint BSc in Politics and Economics from the University of Bristol. His research is focused on asset pricing anomalies in the macro and equities space and applied machine learning methods. His article Machine Learning for Stock Selection was recently published in the Financial Analysts Journal.

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• Yuriy Bodjov

  • Mr. Bodjov joined TD Asset Management in 2008. He is responsible for the research and development of low volatility, absolute return and derivatives strategies and the quantitative risk modeling. Mr. Bodjov has many years of portfolio management experience. From 2004 through 2008, he was director and portfolio manager at the Caisse de dépôt et placement du Québec where he managed tactical asset allocation strategies. Prior to that, Mr. Bodjov was portfolio manager of U.S. and international equities at Fiera Capital and Elantis Investment Management and fixed income portfolios at Baker Gilmore and Associates. He worked also as a senior consultant at BARRA International – a leader in financial risk management. Mr. Bodjov has Master’s degrees in Economics from the University of National and World Economy in Sofia, Bulgaria and from the Université du Québec à Montréal. Mr. Bodjov is a CFA charterholder.

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• Solon Barocas

  • Solon Barocas is Assistant Professor in the Department of Information Science at Cornell University. His current research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference. He is also a Principal Researcher in the New York City lab of Microsoft Research and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University.

  • His research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference.

  • He co-founded the annual workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) and later established the ACM conference on Fairness, Accountability, and Transparency (FAT*).

  • He was previously a Postdoctoral Researcher at Microsoft Research as well as a Postdoctoral Research Associate at the Center for Information Technology Policy at Princeton University. He completed his doctorate at New York University, where he remains a Visiting Scholar at the Center for Urban Science + Progress.

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• Oded Netzer

  • Professor Netzer's expertise centers on one of the major business challenges of the data-rich environment: developing quantitative methods that leverage data to gain a deeper understanding of customer behavior and guide firms' decisions. He focuses primarily on building statistical and econometric models to measure consumer preferences and understand how customer choices change over time, and across contexts. Most notably, he has developed a framework for managing firms' customer bases through dynamic segmentation. More recently, his research focuses on leveraging text-mining techniques for business applications.

  • Professor Netzer published numerous papers in the leading scholarly journals. His research was nominated for and won multiple awards including, ISMS Long-term Contribution Award, the John Little Best Paper Award, the Frank Bass Outstanding Dissertation Award, the Paul E. Green Best Paper Award, the William O’Dell Best Paper Award, the Gary L. Lilian ISMS/MSI Practice Prize Award, the Society for Consumer Psychology (SCP) Best Paper Award, and the George S. Eccles Research Fund Award. He serves on the editorial board of several leading journals including: Marketing Science, Management Science, Journal of Marketing Research, Journal of Marketing, Quantitative Marketing and Economic, and International Journal of Research in Marketing.

  • Oded teaches several courses including the Core Marketing course, a course on Marketing Research, a course on Developing Quantitative Intuition (QI), a masters and doctoral course on Empirical Models in Marketing, as well as several executive education programs. Professor Netzer has won the Columbia Business School Dean’s Award for Teaching Excellence, and the Columbia University GSAC Faculty Mentoring Award to commemorate excellence in the mentoring of Ph.D. students.

  • Professor Netzer frequently consult to Fortune 500 companies and entrepreneurial organization on strategy, data-driven decision making, marketing research and extracting useful information from rich and thin data.

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• Zheng Tracy Ke

  • Zheng Tracy Ke is Assistant Professor of Statistics at Harvard University. She received her Ph.D. in Operations Research and Financial Engineering from Princeton University in 2014. She was a tenure-track Assistant Professor of Statistics at University of Chicago from 2014 to 2018. Her research interest is in high-dimensional statistics, sparse inference, machine learning, network data analysis, text mining, and biostatistics and bioinformatics. Some of her recent projects include studying the optimal statistical inference when the signals are very rare and weak, finding latent community structures from large social networks, and developing statistical methods for natural language processing.

  • She was the recipient of Gordon Y.S. Wu Fellowship from Princeton University in 2009, LAHA Award from Institute of Mathematical Statistics in 2013, and NSF CAREER Award in 2020.

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• Bryan Kelly

  • Bryan Kelly is Professor of Finance at the Yale School of Management, a Research Fellow at the National Bureau of Economic Research, Associate Director of SOM’s International Center for Finance, and is the head of machine learning at AQR Capital Management, LLC. Professor Kelly’s primary research fields are asset pricing and financial econometrics. He is interested in issues related to financial machine learning; volatility, tail risk, and correlation modeling in financial markets; banking sector systemic risk; financial intermediation; and financial networks. His papers in these areas have been published in the American Economic Review, the Quarterly Journal of Economics, the Journal of Finance, the Journal of Financial Economics, and the Review of Financial Studies, among others. He is co-editor of the Journal of Financial Econometrics and associate editor of the Journal of Finance and the Journal of Financial Economics. Before joining Yale, Kelly was a tenured professor of finance at the University of Chicago Booth School of Business.  He earned a bachelor’s degree in economics from the University of Chicago, a master’s degree in economics from University of California San Diego, and a PhD in finance from New York University’s Stern School of Business. Kelly worked in investment banking at Morgan Stanley prior to pursuing his PhD.​