Staff Directory

Xunyu Zhou is the Liu Family Professor of Financial Engineering at Columbia University. His research focuses on quantitative behavioral finance models that incorporate human emotions and psychology into financial decision makings, and on intelligent wealth management solutions using optimal control and machine learning techniques.

Professor Zhou is well known for his work in indefinite stochastic LQ control theory and application to dynamic mean—variance portfolio selection, in asset allocation and pricing under cumulative prospect theory, and in general time-inconsistent problems. He has addressed the 2010 International Congress of Mathematicians, and has been awarded the Wolfson Research Award from The Royal Society (UK), the Outstanding Paper Prize from the Society for Industrial and Applied Mathematics, and the Alexander von Humboldt Research Fellowship. He is both an IEEE Fellow and a SIAM Fellow.

Professor Zhou received his Ph.D. in Operations Research and Control Theory from Fudan University in China in 1989. He was the Nomura Professor of Mathematical Finance, the Director of Nomura Center for Mathematical Finance, and the Director of the Oxford—Nie Financial Big Data Lab at University of Oxford prior to joining Columbia.

Shipra Agrawal is an Assistant Professor in the Department of Industrial Engineering and Operations Research, and a member of Data Science Institute at Columbia University. Her research focuses on learning to make decisions using synergistic methods that combine data analytics, machine learning, and optimization techniques. Specific topics of her interest include multi-armed bandits, online learning, online optimization, and reinforcement learning, with applications in dynamic pricing, online retail, internet advertising, recommendation systems, and dynamic resource allocation.

Agrawal joined Columbia in September 2015 from Microsoft Research where she worked in the Machine learning and Optimization group in Bangalore. Previously, she received her PhD in Computer Science from Stanford University in June 2011.

Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is currently working for UBS AG (Switzerland). He worked as a CFO and CIO for a European bank from 2000 to 2006. He started his career at KPMG.

He is Adjunct Assistant Professor at Columbia University teaching Asset Allocation, Big Data in Finance, Fintech and Hedge Fund Professor at ESADE. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED - Madrid Spain). He also holds the Certified European Financial Analyst diploma ( 2000 ).

His research interests range from asset allocation, big data to algorithmic trading and fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and giving presentations in Indiana University, ESADE, London Business School and several industry seminars.

Dr. Haoran Wang is a postdoctoral research scientist in the Department of Industrial Engineering and Operations Research and the FDT Center for Intelligent Asset Management at Columbia University. His research interest lies at the interface of reinforcement learning (RL), mathematical finance, and stochastic control and optimization. Previously, together with his collaborators, he has developed a novel theoretical framework for exploratory (relaxed) stochastic control in continuous-time RL setting. In a more recent work, Dr. Wang and Prof. Xunyu Zhou presented an interpretable and efficient RL algorithm for solving the continuos-time mean-variance portfolio allocation problem under the exploratory control framework, with performance exceeding that of the classical econometric method and the deep RL method. He is currently working on scalable RL algorithms for risk-sensitive asset management.

Dr. Wang obtained PhD degree in mathematics from The University of Texas at Austin in 2018, advised by Prof. Thaleia Zariphopoulou. His PhD research focused on forward performance processes and real-time model adaptation with applications ranging from portfolio management, option valuation in incomplete market to optimal execution. He is the holder of the Distinguished Bachelor’s Thesis Award, the IPAM Long Program Award and the SIAM Early Career Travel Award.

Garud Iyengar received his B. Tech (EE) from the Indian Institute of Technology at Kanpur in 1993, and his Ph.D. (EE) from Stanford University in 1998. Since then he has been with the Engineering School at Columbia University where he is currently a Professor. He was the valedictorian of his undergraduate class and received the President's Gold Medal from IIT Kanpur in 1993. He received the NSF CAREER Award in 1999.

He is broadly interested in robust optimization, machine learning, data analytics, and information theoretic analysis of biological systems. His current research projects include scalable algorithms for solving portfolio selection problems with multiple spectral risk constraints, tax-aware utility maximization and index tracking, and portfolio selection and securitization methods for peer-to-peer loans.

David Yao is the Piyasombatkul Family Professor of Industrial Engineering and Operations Research at Columbia University, where he has been the founding chair (2013-16) of the Financial and Business Analytics Center at Columbia Data Science Institute. He has also helped establish several masters programs, including the MS/FE (Financial Engineering) program, and has taught regularly executive programs in portfolio management. 
Professor Yao's teaching and research interests focus on the analysis, design and control of stochastic systems, such as stochastic networks, health care systems, supply chains, and related resource control and risk management issues. Author/co-author of some 200 scientific publications, he is a principal investigator of over thirty grants and contracts from government agencies and industrial sources, and a holder of eight U.S. patents. 

His honors and awards include the Presidential Young Investigator Award from the National Science Foundation, Guggenheim Fellowship from the John Simon Guggenheim Foundation, Franz Edelman Award from the Institute for Operations Research and Management Sciences, SIAM Outstanding Paper Prize from the Society for Industrial and Applied Mathematics, Outstanding Technical Achievement Award from IBM Research, Great Teacher Award from the Society of Columbia Graduates, and the IBM Faculty Award. He is an IEEE Fellow, an INFORMS Fellow, and a member of the U.S. National Academy of Engineering.
 

Agostino Capponi is an assistant professor in the IEOR Department at Columbia University, where he is also a member of the Institute for Data Science and Engineering.He is broadly interested in the area of networks and dynamic games, along with the role they play in explaining the behavior of financial institutions and their interactions with society. Agostino is also conducting research on cooperative inverse reinforcement learning, targeting the construction of autonomous systems which enhance human effectiveness in complex environments. The broad applications of his framework are in the areas of transportation systems, defense, and financial robo-advising.

Agostino's research has been funded by the Institute for New Economic Thinking, the Global Risk Institute, and DARPA. For his research on systemic risk in networks, he has received the the Bar-Ilan general prize for research in Financial Mathematics,and a honorable mention from the MIT Center for Finance and Policy and the Harvard Crowd Innovation Laboratory. 

He currently serves as a consultant for the Office of the Chief Economist at the US Commodity Futures Trading Commission, and has been a visiting scholar at the Federal Reserve Board of Governors since March 2016. Agostino served as a member of the roundtable on central clearing interdependencies, a study group established by the Basel Committee, the Committee on Payments and Market Infrastructures, the Financial Stability Board, and the International Organization of Securities Commissions (IOSCO).

Agostino's research on clearinghouses has received attention by various media outlets, including Reuters, Bloomberg, and the American Banker. His research on resolution policies for interbanking networks has been invited for presentation at the plenary session chaired by Prof. Joseph Stiglitz at the 2017 Eighteenth World Congress of Economics.

Agostino serves as the department editor for financial engineering at the Institute of Industrial Engineering Transactions, and as an associate editor of Operations Research Letters and Mathematical Finance. He also serves as a board member of Applied Probability for the Informs Society, and as the director of the SIAM Activity Group on Financial Mathematics and Engineering.