Organizers

Nitesh Chawla, University of Notre Dame: Dr. Chawla is the Frank M. Freimann Professor of Computer Science and Engineering, and director of the research center on network and data sciences (iCeNSA) at the University of Notre Dame. He started his tenure-track career at Notre Dame in 2007, and quickly advanced from assistant professor to an endowed (chaired) full professor position in 2016 (nine years). He has brought in over $26M dollars in research funding to Notre Dame. He has received numerous awards for research, innovation, and teaching. He is the recipient of the 2015 IEEE CIS Outstanding Early Career Award; the IBM Watson Faculty Award, the IBM Big Data and Analytics Faculty Award, National Academy of Engineering New Faculty Fellowship, 1st Source Bank Technology Commercialization Award, and his PhD dissertation also received the Outstanding Dissertation Award. His papers have received several outstanding paper nominations and awards. In addition, his students are also recipient of several honors, including Honorable Mention for Outstanding Dissertation Award at KDD’17. In recognition of the societal and impact of his research, he was recognized with the Rodney Ganey Award and Michiana 40 Under 40. He is a two-time recipient of Outstanding Teaching Award at Notre Dame. He is a Fellow of the Reilly Center for Science, Technology, and Values;, Fellow of the Institute of Asia and Asian Studies; and Fellow of the Kroc Institute for International Peace Studies at Notre Dame. He is the founder of Aunalytics, a data science software and solutions company.

Mihai Cucuringu, University of Oxford & The Alan Turing Institute: Dr. Cucuringu is an Associate Professor in the Department of Statistics, an Affiliate Faculty in the Mathematical and Computational Finance group at the Mathematical Institute, at University of Oxford, and a Fellow at Turing Institute in London. Previously, he was a CAM Assistant Adjunct Professor in the UCLA Dept. of Mathematics. He finished his Ph.D. in Applied Mathematics at Princeton University in 2012. His research pertains to the development and mathematical analysis of algorithms that extract information from massive noisy data sets, network analysis and certain inverse problems on graphs, such as clustering and ranking, with an eye towards extracting structure from time-dependent data which can be subsequently leveraged for prediction. His research interests in finance focus on statistical arbitrage, machine-learning for asset pricing, market microstructure in equity and crypto markets, synthetic data generation, and anomaly detection in financial networks. He previously organized a workshop on Network Science in Financial Services at The Turing Institute.

Xiaowen Dong, University of Oxford: Dr Xiaowen Dong is an Associate Professor in the Department of Engineering Science at the University of Oxford, where he is an academic member of both the Machine Learning Research Group and the Oxford-Man Institute. His main research interests concern signal processing and machine learning techniques for analysing network data, and their applications in studying questions across social and economic sciences.

Dhagash Mehta, Blackrock, Inc. (Primary Organizer): Dr Dhagash Mehta is the Head of Applied Machine Learning Research (Investment Management) at Blackrock Inc. Previously, he was a Senior Manager of Investment Strategies at The Vanguard Group leading a research group on machine learning and investment strategies. He was a Senior Research Scientist at United Technologies Research Center; a research assistant professor at University of Notre Dame and North Carolina State University; a research fellow at Syracuse University, the University of Cambridge, Simons Center for Theory of Computing and National University of Ireland Maynooth. He possesses Part III Mathematical Tripos from the University of Cambridge, and a Ph.D. between the University of Adelaide, Australia, and Imperial College London in Applied Math/Theoretical Physics areas. He has published 55 journal articles and 25+ conference proceedings, and his research expertise are in loss landscapes of deep learning, applications of algebraic geometry to machine learning, machine learning and non-convex methods for portfolio optimization, NLP and graph machine learning for financial applications, robo-advisory system and behavioural finance. Dr Mehta is also on the editorial advisory board at Journal for Financial Data Science. Dr Mehta has co-organized three large conferences at International Center for Theoretical Physics Trieste (Italy) and Sanya International Mathematics Forum; a workshop at Neurips 2020; multiple mini-symposia at Society for Applied and Industrial Mathematics conferences; and, multiple sectionals at American Mathematical Society conferences.

Senthil Kumar, Capital One: Dr. Kumar is the Chief ML Scientist at the Center for Machine Learning at Capital One where he applies Machine Learning and AI to various business problems. Prior to joining Capital One, he was at Bell Laboratories where he developed new technologies and managed several successful products that have been licensed around the world. He has published over 30 papers and holds 6 patents. Recently, he co-organized the 2019 ICML Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning, the 2020 NeurIPS Workshop on Fair AI in Finance, the 2021 KDD Workshop on Machine Learning in Finance, and the 2021 ICML Workshop on Representation Learning for Finance and e-Commerce Applications.

Stefan Zohren, University of Oxford: Dr. Stefan Zohren is an Associate Professorial Research Fellow at the Oxford-Man Institute of Quantitative Finance, an Associate at the Oxford Internet Institute and a Mentor in the FinTech stream at the Creative Destruction Lab at Said Business School, all at the University of Oxford. He is also a member of the European Laboratory for Learning and Intelligent Systems. Outside of academia, Stefan works as a Scientific Advisor at Man Group. His research is focused on machine learning in finance, including deep learning, reinforcement learning, network and NLP approaches, as well as early use cases of quantum computing. He is an Associate Editor of the Journal of Mathematical Finance. Stefan is also a frequent speaker on AI in finance at academic conferences, as well as industry panels and corporate events. He has organised a number of workshops on related areas, including one on Agent-based Modelling and Simulation in Finance at the Turing Institute and one on Machine Learning in Computational Finance at the SIAM conference on Financial Mathematics.