Speakers & Panelists

Anastassia Fedyk is an Assistant Professor of Finance at the Haas School of Business at UC Berkeley. Her research lies at the intersection of behavioral finance and technology, with a specific emphasis on using large unstructured data to understand firm valuations. She studies firms’ investments in technologies such as Artificial Intelligence, and how these investments are priced by the market. Dr. Fedyk holds a Ph.D. in Business Economics from Harvard University and a B.A. in Mathematics with honors from Princeton University. Prior to pursuing her academic career, Anastassia Fedyk was a researcher and portfolio manager at Goldman Sachs Asset Management.

Keynote Talk: Is Artificial Intelligence Good for Business? The Effects of AI on Firm Operations and Growth

Jonathan Lerner is the Head of Data Science and Client and Sales Solutions at BlackRock, Inc. He leads a team of data scientists working on projects related to investor modelling for sales and marketing. Prior to joining BlackRock, he had founded and led various data science teams at Uber. Before Uber, he was a portfolio manager at a hedge fund focusing on Global Macro and Algorithmic investing strategies including spending several years in Asia. Jon earned a BA in Applied Mathematics and Economics, magna cum laude, from Harvard University and MBA from the Wharton School of the University of Pennsylvania.

Keynote Talk: Data Science for Client Engagement in the Financial Industry

Andreas Lehrmann is a Research Director at Borealis AI, an RBC Institute for Research, and an Adjunct Professor at the University of British Columbia. Andreas works at the intersection of machine learning and market microstructure, with a focus on deep generative models for structured data and approximate inference for sequential prediction tasks. He received his PhD from ETH Zurich and held postdoctoral positions at Facebook Reality Labs and Disney Research.

Keynote Talk: Probabilistic Inference in Generative Models with Diffeomorphic Structure

He (Heather) He is a Lecturer in Data Science/Analytics at the Bangor Business School, Bangor University, UK. Prior to this position, Heather was a doctoral researcher at the Centre for Risk Research, University of Southampton, UK. Her research interests are so far focused on (i) applied data science for finance & business analytics, (ii) behavioural risk-taking & decision-making, and (iii) financial technologies. Heather’s research work has been presented at international conferences and workshops including INFORMS and ACM International Conference on AI in Finance (ICAIF). Heather also serves as a reviewer for several internationally leading academic journals. Heather has interests and a track record in developing and delivering data science and business analytics programmes at undergraduate and postgraduate levels.

Accepted Paper: "How Do Mobile Trading Apps Change Individual Investors’ Responses to News Sentiment?" with co-authors Tiejun Ma (University of Southampton), Ming-Chien Sung (University of Southampton), and Johnnie Johnson (Nottingham Trent University)

Kshama Dwarakanath is an Associate in the AI Research team at JP Morgan Chase. She completed her Master’s degree at UC Berkeley focused on nonlinear systems and control. At JPMC, she works on using reinforcement learning to design and learn trading agents with diverse objectives in simulated multi-agent markets. Her interests lie in the fields of reinforcement learning, multi agent simulations and mechanism design.

Accepted Paper: "Biased or Limited: Modeling Sub-Rational Human Investors in Financial Markets" with co-authors Penghang Liu (University at Buffalo) and Svitlana Vyetrenko (J. P. Morgan Chase)

Yongjae Lee is an Assistant Professor in the Department of Industrial Engineering at Ulsan National Institute of Science and Technology (UNIST). He is trying to utilize quantitative techniques such as ML/AI and optimization to analyze financial data and derive optimal decisions. He is particularly interested in analyzing individual and household financial activity to draw useful insights and design customized services. He has been working as an advisory editorial member for the Journal of Financial Data Science, and he has done projects to develop financial services using ML/AI techniques with several companies. He received his B.S. degree in computer science and mathematical sciences and PhD degree in industrial and systems engineering from KAIST.

Accepted Paper: Dr. Lee will present his accepted paper "A Paradigm Shift in Investor Modeling: From Organization-Centric to User-Centric".