A Conversation with Hengzhong Liu, Managing Partner, Thoken LLC
April 18, 2023
Gross Hall, Room 330
3:30- 4:45 pm
We’ll discuss how data science and other quantitative techniques have transformed retail banking product development, pricing and revenues as well as risk management; a relevant and timely topic in light of the recent bank failures such as SVB.
Can’t make it in person? Catch the talk streaming live here.
Hengzhong Liu, Managing Partner, Thoken LLC (Senior Executive in Banking and Data Science)
Ex-Managing Director, Bank of America and Citi Group
Dr. Hengzhong Liu is currently developing Thoken.io, a FinTech startup for online content micropayments. He also serves on the Advisory Board for the CUNY Economic Studies Group. Dr. Liu has over 20+ years in banking and financial services and has held senior positions in several large US banks including Bank of America and Citigroup, where he managed decision science, business strategy, and quantitative risk teams in retail and wholesale sectors. He received his PhD in Financial Economics from the Graduate Center at City University of New York.
David Ye, Executive in Residence, Department of Mathematics and Master in Interdisciplinary Studies (MIDS)
David has been an Executive in Residence at Duke since the Spring of 2021. In that capacity, he uses his extensive industry experiences and connections to advise the department in its curriculum, teaching, and research, particularly as it relates quantitative finance field. Prior to Duke, David had been Chief Risk Officer in several firms including Nomura Holding America, State Street Global Markets, and Guardian Life Insurance. He designed and has been teaching Math 585 – Algorithmic Trading since Spring 2021. David finds great satisfactions and intellectual stimulations in teaching and mentoring students. Beyond Math Department, David has been actively involved in supporting Duke in his capacity as a member of Board of Visitors of Graduate School from 2017 to 2023.
Department of Mathematics and Master in Interdisciplinary Data Science (MIDS)