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TransAI 2021, ai4i 2021

Joint Keynotes

(in alphabetical order)

Tucker Balch

Managing Director, J.P.Morgan AI Research, USA


​Title:  AI Research at J.P. Morgan


Bio: Dr. Balch is a Research Managing Director at J.P. Morgan AI Research and a Professor of Interactive Computing at Georgia Tech (on leave). He is interested in problems concerning multi-agent social behavior in domains ranging from financial markets to tracking and modeling the behavior of ants, honey bees, and monkeys. He co-founded Lucena Research, an investment software firm that applies Machine Learning and Big Data approach to investment problems. Balch has published 120 peer-reviewed articles. His work has been covered by the Wall Street Journal, CNN, New Scientist, Institutional Investor, and the New York Times. His graduated students work at NASA/JPL, Boston Dynamics, Goldman Sachs, Morgan Stanley, Citadel, AQR, and BlackRock. Before his career in computing, Tucker was an F-15 pilot in the US Air Force.

 Abhijit Bose

Head AI/ML, Capital One, USA

Title:  Scaling AI : Why It Is So Hard and How to Approach It for Mission-critical Applications


Bio: Abhijit Bose is the Managing Vice President for Capital One's The Center for Machine Learning (C4ML). Prior to joining Capital One, Abhijit served as Facebook's Head of Engineering (Montreal, NYC, Pittsburg) for Facebook AI Research. With over 20+ years of data science expertise, Abhijit encompasses an impressive technical and academic career history. He obtained a Bachelor's degree in Mechanical Engineering, received his dual-Masters in Mechanical Engineering as well as Computer Science, and received his dual-Ph.D in Computer Science and Engineering Mechanics. Before joining Facebook, Abhihit was the Managing Director of Data Science for JP Morgan's Digital Organization. He's also worked for IBM, Google, and American Express. Abhijit and his wife live in New Jersey with their 6-year-old twins and their family pet Eskie. When he's not working, Abhijit enjoys spending time volunteering with his family at their local animal shelter, as well as hiking and touring state parks.

Chung-Sheng Li 
Managing Director, PwC Lab, USA

Title:  Emerging Opportunity and Challenges from Applying AI in Finance Operations 


Abstract:  The advances of  finance operations, as precipitated by three primary driving forces, are likely to accelerate in the coming future:

  • continued challenges on the need for improved real-time operational visibility and audit quality in a fast evolving business environment,

  • rapid digitization of accounting processes within corporations, especially in the post pandemic world, and

  • much more available data that allows much easier corroboration by both endogenous and exogenous information,

These trends are closely associated with the trends that businesses are quickly moving their financial data to the cloud, fast maturing of structure reporting (such as XBRL), the adoption of industry ontology, and the focus on fully monetizing the hidden value of data.


In this talk, we will discuss emerging opportunities as we reimagine and apply AI in the future of finance operations from both management and auditing perspectives.  This includes using AI to automate (1) connecting the dots within order-to-cash, procure-to-pay, record-to-report, and financial planning and analytic business processes, (2) entity extraction and document comprehension from invoice, purchase order, contracts and lease agreements,   (3) evidence reasoning as part of control and substantive testing during an audit procedure, and (4) question answering for finance related questions.   Furthermore, digital transformation of the finance operations also offers the opportunity to refactor the business processes based on common computation in terms of data, process, and policy integrity, resulting in a much more computationally efficient while easily reconfigurable implementation of existing processes.    We will also discuss the challenges introduced as a result from heavy use of AI and machine learning, such as the possibility of introducing bias in models and training data as well as vulnerabilities due to AI attacks.   


Bio: Chung-Sheng Li is currently a Managing Director at PwC Lab with the focus on driving AI augmented assurance..  Prior to joining PwC, he was with Accenture Operations as the Global Research Managing Director of AI, with the focus on driving the development of new AI-enabled business process service offerings for Accenture Business Process Services from 2016 to 2019. Previously, he has been with IBM Research between 1990 and 2016 with various technical leadership responsibilities.

His career includes driving research and development initiatives spanning cognitive computing, cloud computing, smarter planet, cybersecurity, and cognitive regulatory compliance. He has authored or coauthored more than 130 patents and 170 journal and conference papers (and received the best paper award from IEEE Transactions on Multimedia in 2003). He is a Fellow of the IEEE.

He received BSEE from National Taiwan University, Taiwan, R.O.C., in 1984, and the MS and Ph.D.degrees in electrical engineering and computer science from the University of California, Berkeley, in 1989 and 1991,respectively. 

Michael Pyrcz

Professor, The University of Texas at Austin, USA

Title:  Subsurface Data Analytics and Machine Learning: A Geostatistical Perspective 


Abstract:  The most recent wave of artificial intelligence (AI) is transforming science and engineering over most disciplines, including the subsurface related geoscience and engineering fields. Yet, the subsurface is unique and in many ways progressed further along this digital transformation and offers useful insights on the path ahead for the merger of science and engineering with AI for improved practice and research.

Bio: Michael Pyrcz is an associate professor in the Department of Petroleum and Geosystems Engineering, and the Jackson School of Geosciences, The University of Texas at Austin, where he researches and teaches on the topics of subsurface, spatial data analytics, geostatistics and machine learning. Michael is also the principal investigator of the freshmen research initiative and a core faculty in the Machine Learn Laboratory in the College of Natural Sciences, The University of Texas at Austin, an associate editor for Computers and Geosciences and a board member for Mathematical Geosciences, the International Association for Mathematical Geosciences, and the program chair for the Petroleum Data Driven Analytics Technical Section of the Society of Petroleum Engineers. Michael has written over 60 peer-reviewed publications, a Python package for spatial, subsurface data analytics, and coauthored a textbook on spatial data analytics, 'Geostatistical Reservoir Modeling'. All of Michael's university lectures are available on his YouTube channel, to support his students and working professionals with evergreen educational content. To find out more about Michael's work and shared educational resources visit his website at

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