Organizers

Prof. Yongjae Lee (Lead Organizer)

Prof. Yongjae Lee is an Associate Professor in the Department of Industrial Engineering and the Graduate School of Artificial Intelligence at Ulsan National Institute of Science and Technology (UNIST) in South Korea. Dr. Lee utilizes quantitative techniques such as ML/AI and optimization to analyze financial data and derive optimal decisions, and he is particularly interested in analyzing individual and household financial activity to draw useful insights and design customized services. Dr. Lee has published more than 30 papers in reputable journals and conferences like Quantitative Finance, European Journal of Operations Research, Annals of Operations Research, Journal of Portfolio Management, ICAIF, PAKDD and AISTATS. He is an advisory editorial member for the Journal of Financial Data Science and an advisor at Hana Institute of Technology. He received his B.S. degree in computer science and mathematical sciences and Ph.D. degree in industrial and systems engineering from KAIST.

Dr. Lee was a program committee of the ‘2018 FinTech Conference on the State of the Art in Robo-Advising Systems: Financial Technologies for Enhanced Social Security’ in Seoul, Korea (co-hosted by KAIST, Tsinghua University, Princeton University, and EDHEC Business School), a co-organizer of workshop on ‘Machine Learning for Investor Modelling and Recommender Systems’ at ICAIF’23, a program committee of ICAIF’23, and a program committee of IJCAI-2024.

Email: yongjaelee@unist.ac.kr
Webpage: https://felab.unist.ac.kr/
LinkedIn: https://www.linkedin.com/in/yongjae-lee-548982107/

Prof. John R.J. Thompson

Prof. John R.J. Thompson is an Assistant Professor at the University of British Columbia whose areas of expertise are nonparametric and applied statistics and machine learning. His methodological research interests lie in smoothing, distance metric learning, clustering, and change-point analysis. John is a member of the Financial Wellness Lab at Western University which aims to design data-driven tools that Canadians can use to improve their financial resilience, reduce financial stress, and support better financial decisions. John's current applied research includes modelling and clustering the behaviors of Canadian investors under the guidance of financial advisors, and designing effective financial measures and Robo-tools that aid financial advisors in supporting their clients’ investment portfolios.

John was the lead organizer for a BIRS Okanagan workshop on Climate Change Scenarios and Financial Risk on July 3rd-8th 2022, lead organizer of the series of workshops on Machine Learning for Investor Modelling at ICAIF’22 on November 2nd 2022, the Fields Institute on February 16th-17th 2023, and ICAIF’23 on November 27th 2023, and is a co-organizer on an application for BIRS 2025 workshop on Stochastic Models, Statistics and Machine Learning for Green Finance.

Email: john.thompson@ubc.ca
Research profile: https://www.researchgate.net/profile/John-Thompson-27
LinkedIn: https://www.linkedin.com/in/john-thompson-4b2b036b/

Dr. Dhagash Mehta

Dr. Dhagash Mehta is the Head of Applied Machine Learning Research (Investment Management) at Blackrock Inc. and an Editorial Board Member at the Journal of Financial Data Science and Journal of ESG and Impact Investing (both PMR journals). Previously he was a Senior Manager, Investment Strategist (Machine Learning – Asset Allocation) at Investment Strategy Group at The Vanguard Group. Before joining Vanguard, he was a Senior Research Scientist at United Technologies (UTX) Research Center. Prior to that, he was a Research Assistant Professor at the Department of Applied and Computational Mathematics and Statistics in conjunction with the Department of Chemical and Biomolecular Engineering at University of Notre Dame. He was a Fields Institute Postdoc Fellow for the Thematic Program on Computer Algebra at Fields Institute, Toronto, in Fall 2015 and a Visiting Fellow at Simons Institute for Theory of Computing at Berkeley in Fall 2014. Previously, he has held various research positions at the University of Cambridge (the UK), Imperial College London (the UK), the University of Adelaide (Australia), Syracuse University (USA) and National University of Ireland Maynooth (Ireland). Dr. Mehta’s research areas are machine/deep learning; quantitative finance, and computational mathematics, science and engineering. In particular, I work on optimization (convex and nonconvex), computational algebraic geometry, numerical analysis, network science and machine learning to solve various problems arising in financial services and wealth/asset management (and in the past, power systems and control theory; and theoretical and computational physics, jet-engines, HVAC and building systems, chemistry and biology).

Dhagash served as the workshop chair at ICAIF’23 and organized many workshops including workshops on Machine Learning for Investor Modelling at ICAIF’22, Machine Learning for Investor Modelling at the Fields Institute, Benchmarks for AI in Finance at ICAIF’22, Machine Learning for Environmental, Social and Governance (ESG) Investing at ICAIF’22, Natural Language Processing and Network Analysis in Financial Applications at ICAIF’22, and AI in Finance for Social Impact at AAAI 2024

Email: Dhagash.Mehta@blackrock.com
LinkedIn: https://www.linkedin.com/in/dhagash-mehta-ph-d-45000111a/

Mr. Thomas J. De Luca

Mr. Thomas J. De Luca is a Senior Researcher, investor behavior, in Vanguard’s Investment Strategy Group, responsible for conducting research on investor preferences and decision-making and applying behavioral insights to real-world settings. His expertise includes financial modeling, data analysis, and investor behavior. Tom’s research examines the behavior of self-directed individual investors, including retirement withdrawal trends, ESG (environmental, social, and governance) fund usage, and investor reactions to the COVID-19 pandemic. Prior to joining Vanguard in 2014, Tom served as a captain and meteorologist in the U.S. Air Force. Tom holds a B.A. in mathematics from Cornell University, M.S. degrees in applied mathematics and meteorology from the Naval Postgraduate School, an M.B.A. with distinction from the Kellogg School of Management, and an M.P.S. in data analytics from Pennsylvania State University.

Thomas was a co-organizer of workshop on ‘Machine Learning for Investor Modelling and Recommender Systems’ at ICAIF’23

Email: Thomas_j_de_luca@vanguard.com
LinkedIn: https://www.linkedin.com/in/thomas-de-luca-23371632

Prof. Richard Mccreadie

Prof. Richard Mccreadie is a Senior Lecturer at the Computing Science School at the University of Glasgow, UK. He leads the Financial Informatics Research Theme within the Information, Data and Analysis section, representing a group of researchers working on the application of recommender systems, search engines, natural language processing and AI for financial use-cases. Richard has over 90 publications in the areas of financial technologies, information retrieval, recommender systems and platform evaluation, as well as has extensive experience leading both research and innovation projects in the financial space, including the European Commision Flagship Horizon 2020 Infinitech Project. Richard also has over 10 years experience organizing research workshops and shared task challenges as part of the internationally renowned Text Retrieval Conference (TREC), and is also both on the TREC steering committee and a local chair for the up-coming ECIR 2024 conference.

Email: Richard.Mccreadie@glasgow.ac.uk
Webpage: https://www.gla.ac.uk/schools/computing/staff/richardmccreadie/

Prof. Jaesik Choi

Prof. Jaesik Choi is an associate professor of Graduate School of Artificial Intelligence at KAIST since September 2019. He is also a director of the Explainable Artificial Intelligent Center established by the Ministry of Science and ICT, Republic of Korea. He is the CEO of a startup company, INEEJI. He had been the Rising-Star Distinguished associate professor in the School of Electrical and Computer Engineering at UNIST (Ulsan, Korea) and an affiliate researcher of the Lawrence Berkeley National Laboratory (the Berkeley Lab) until August 2019. He had been an assistant professor at UNIST from July 2013 to August 2017. He was a Computer Scientist Postdoctoral Fellow of the Computational Research Division at the Berkeley Lab. His research focuses on learning and inference with large scale, complex systems, explainable machine learning models and spatio-temporal data analysis. He recently served area chairs for IJCAI, AAAI and UAI. He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (2012) and received B.S. degree in Computer Engineering from Seoul National University (2004).

Email: jaesik.choi@kaist.ac.kr
Webpage: http://sailab.kaist.ac.kr/

Dr. Min Hee Kim

Dr. Min Hee Kim currently holds the position of Tech Leader at Hana Institute of Technology, an organization specializing in the research and development of data-driven financial services integration. She is committed to exploring cutting-edge technologies that can empower Hana Financial Group to navigate the evolving business landscape shaped by emerging technologies. Dr. Kim's main focus lies in investigating innovative technologies that seamlessly blend with the financial sector, sharing insights to drive Hana Financial Group's Digital Transformation. Her primary research areas encompass credit scoring, fraud detection, personalized asset management, and chatbots for customer consultations based on AI technologies. Prior to joining Hana Institute of Technology in 2018, she served as a knowledgeable and dedicated data scientist at Samsung Research, Hyundai Card, and LG Economic Research Institute. In these roles, her duties predominantly involved the in-depth analysis of internal and external data to facilitate informed, data-driven decision-making. Dr. Kim earned both her bachelor's and master's degrees in statistics from Ewha Womans University and later achieved a Ph.D. in statistics from Pennsylvania State University (2010).

Email: mh0325.kim@hanafn.com
Webpage: https://hit.hanati.co.kr/en/

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