Natural Language Processing (NLP) and Digital Finance
Event Information
About this Event
Translational Research Workshop
Natural Language Processing (NLP) and Digital Finance
Harness the potential of Natural Language Processing (NLP) for digital finance
Aimed at industry practitioners, advanced graduate students, and academic researchers, the Translational Research Workshop series includes lectures and discussions of cutting–edge research delivered to the audience in a user-friendly manner with a focus on digital finance applications. The domain experts from academia will share their expertise on the hottest topics.
This programme will empower you with the skills and confidence to exploit opportunities in digital finance using Natural Language Processing (NLP). Lectures and discussions by leading scholars in the field will enable you to gain insights into the foundations as well as the various applications of Natural Language Processing (NLP) in digital finance.
Core Focus
Fundamentals, Challenges, and New Directions of Natural Language Processing (NLP);
Real–World Applications of Natural Language Processing (NLP) in Digital Finance
Event Details
Venue: Bridge+, 79 Robinson Road
Date: 18–19 Feb 2021, Thursday–Friday
Time: 9:00am to 6:30pm (SGT, GMT +8)
Registration fee (for in-person attendance): SGD50 (refreshments and lunch included)
Registration fee (for online attendance): free (refreshments and lunch not included)
Participants are strongly advised to register early as limited spaces are available for both in-person and online attendance.
Professors
Jin-Chuan Duan is the Jardine Cycle & Carriage Professor of Finance and the Executive Director of the Asian Institute of Digital Finance at the National University of Singapore (NUS). Prof Duan received a PhD in finance from the University of Wisconsin-Madison, and is an academician of Academia Sinica, a fellow of the Society for Financial Econometrics, and an advisory board member of the International Association of Credit Portfolio Managers. Prior to joining NUS, he held the Manulife Chair in Financial Services at Rotman School of Management, University of Toronto.
Being a leading expert and enthusiast on credit risk modeling, Prof Duan pioneered the Credit Research Initiative (CRI) in 2009 while he was the director of the Risk Management Institute, and has been leading the CRI team since its inception. CRI is a "public good" endeavor providing freely accessible, daily updated default probabilities on roughly 80,000 exchange-listed firms in 133 economies globally. In 2017, he co-founded CriAT, a FinTech company specializing in deep credit analytical solutions for financial institutions, and serves as its non-executive chairman.
Huang Ke-Wei Wei is an Associate Professor in the Department of Information Systems and Analytics at the National University of Singapore (NUS). Dr. Huang joined NUS in July 2007. He received his Ph.D. (2007), M.Phil. (2005), and M.Sc. (2002) degrees in Information Systems from the Stern School of Business at New York University, and his M.B.A. in Finance (1997) and B.Sc. in Electrical Engineering (1995) from National Taiwan University.
Dr. Huang's research interests are in machine learning for social science research methods, data mining for financial applications, and AI entrepreneurships. Currently, he focuses on various topics of using unstructured data in data analytics for finance and accounting applications.
Soujanya Poria is an Assistant Professor at Singapore University of Technology and Design. He is also part of the Institute of High-Performance Computing (IHPC), ASTAR as a senior scientist. Before joining SUTD, Dr. Poria worked at NTU where he was awarded the prestigious NTU presidential postdoctoral fellowship. His main areas of research interest are NLP and sentiment analysis. You can find more details at https://sporia.info/.
He works in DeCLaRe Lab where they focus on challenging NLP problems, such as dialogue comprehension and generation, commonsense reasoning, multimodal understanding, and more. You can find more details at https://declare-lab.net/.