Speakers

Umut Topkara, Head of AI Content, Bloomberg

Umut Topkara is the head of Bloomberg’s Artificial Intelligence (AI) Content team in the company's Engineering department. He holds a Ph.D. in computer science from Purdue University. Prior to joining Bloomberg, he held research and engineering positions at Google, IBM Research, and JW Player. Umut leads several engineering teams that build products using information extraction, information retrieval, summarization and analytics over the vast news and analyst research content at Bloomberg.

Talk Title: BloombergGPT

Dhagash Mehta, Head of Machine Learning Blackrock

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.

Talk Title: Distance-based unsupervised outlier detection method for mutual fund (mis-)categorization

Nhung Ho, Vice President of Artificial Intelligence, Intuit

Nhung is Vice President of Artificial Intelligence for Intuit’s QuickBooks Ecosystem, TurboTax, and Customer Success organizations. She leads applied science teams that build new-to-the-world products and services backed by artificial intelligence to serve the company’s small business and consumer customers. They solve a variety of problems, ranging from call center demand forecasting and natural language systems to identifying customer intent, automating accounting, and making tax automatic. During her time at Intuit, she has been part of evolving artificial intelligence from a niche field that solved narrow problems to one that is at the core of Intuit’s strategy to become the AI-driven expert platform. Nhung has a Ph.D. in Astrophysics from Yale University and a B.A. in Astrophysics from University of California, Berkeley.

Talk Title: Transforming the World of Consumer and Small Business Finances with Generative AI

Cristian Bravo Roman, University of Western Ontario

Dr. Cristián Bravo is an Associate Professor and Canada Research Chair in Banking and Insurance Analytics at the University of Western Ontario, Canada. He also serves as the Director of the Banking Analytics Lab. His research lies at the intersection of data science, analytics, and credit risk, researching how techniques such as multimodal deep learning, causal inference, and social network analysis can be used to understand relations between consumers and financial institutions. He has over 75 academic works in high-impact journals and conferences in operational research, finance, and computer science. He serves as an editorial board member in Applied Soft Computing and the Journal of Business Analytics and is the co-author of the book “Profit Driven Business Analytics”, which has sold over 6,000 copies to date. Dr. Bravo has been quoted by The Wall Street Journal, WIRED, CTV, The Toronto Star, The Globe and Mail, and Global News. He is also a regular panelist at CBC News’ Weekend Business Panel where he discusses the latest news in Banking, Finance and Artificial Intelligence. He can be reached via LinkedIn, by Twitter @CrBravoR, or through his lab website at https://thebal.ai.

Talk Title: Leveraging Deep Learning for Multimodal Data Analysis in Credit Risk Assessment

Ranak Roy Chowdhury, University of California, San Diego

Ranak Roy Chowdhury is a Ph.D. candidate in Computer Science at UCSD, advised by Professor Rajesh Gupta and Professor Jingbo Shang. His research focuses on data efficiency and robustness for time-series data mining. His work has been published at venues in machine learning, data mining, data engineering, and Internet-of-Things. He is the recipient of the Qualcomm Innovation Fellowship and the Halıcıoglu Data Science Institute Graduate Prize Fellowship. Prior to pursuing his Ph.D., he obtained his MS in Computer Science from UCSD and completed internships at Amazon Web Services (AWS) and Nokia Bell Labs.

Talk Title: PrimeNet: Pre-training for Irregular Multivariate Time-Series

Srijan Kumar, Georgia Tech

Srijan Kumar is an Assistant Professor at the College of Computing at the Georgia Institute of Technology. He completed his postdoctoral training at Stanford University, received a Ph.D. and M.S. in Computer Science from the University of Maryland, College Park, and B.Tech. from the Indian Institute of Technology, Kharagpur. He develops Data Mining methods to detect and mitigate the pressing threats posed by malicious actors (e.g., evaders, sockpuppets, etc.) and harmful content (e.g., misinformation, hate speech etc.) to web users and platforms. His methods have been used in production at Flipkart (India’s largest e-commerce platform) and influenced Twitter’s Birdwatch system. He has been selected as an NSF CAREER awardee, a Kavli Fellow by the National Academy of Sciences, named as Forbes 30 under 30 honoree in Science, ACM SIGKDD Doctoral Dissertation Award runner-up 2018, and best paper honorable mention award from the ACM Web Conference. He has also won the Adobe Faculty Award and the Facebook Faculty Award. His research has been covered in the popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine.

Talk Title: Robustness and Reliability of LLMs and Multimodal Models