Title: Artificial Intelligence in Chemical Engineering: Challenges and Opportunities.
Time and Date: Friday, February 24. Refreshments served at 12:45 p.m. Seminar starts at 1 p.m.
Location: CHBE 102 (2360 East Mall)
Abstract
Artificial intelligence (AI) started off with great promise in the early 1980s, spurred by the success of the expert system paradigm in certain applications. This prompted a flurry of research activities in chemical engineering in the mid-1980s. However, as the ensuing three decades showed, AI didn’t quite live up to its promise in chemical engineering.
So, what went wrong with AI?
In this talk, I will review the different phases of AI in chemical engineering over the last 35 years, providing some background and explanation to this question. I will also argue that this time it is different – I believe the time for AI in chemical engineering, and in other domains, has arrived, finally. I classify the opportunities into three categories – easy, hard, and harder problems – and show examples. The truly interesting and intellectually challenging problems lie in developing such conceptual frameworks as hybrid AI models, mechanism-based causal explanations, and domain-specific knowledge discovery engines. These breakthroughs would require going beyond purely data-centric machine learning, despite all the current excitement, and leveraging other knowledge representation and reasoning methods from the earlier phases of AI. They would require a proper integration of symbolic reasoning with data-driven processing. I will discuss the challenges and opportunities in the coming years.
Biography
VENKAT VENKATASUBRAMANIAN is Samuel Ruben-Peter G. Viele Professor of Engineering in the Department of Chemical Engineering, Professor of Computer Science (Affiliate), and Professor of Industrial Engineering and Operations Research (Affiliate) at Columbia University. He earned his Ph. D. in Chemical Engineering at Cornell, M.S. in Physics at Vanderbilt, and B. Tech. in Chemical Engineering at the University of Madras, India. He taught at Purdue University for over two decades, before returning to Columbia in 2011.
Venkat is a complex-dynamical-systems theorist interested in developing mathematical models of their structure, function, and behavior from fundamental conceptual principles. He considers himself as an artist in science, whose natural tendency is to conduct curiosity-driven research in a style that might be considered impressionistic, emphasizing conceptual issues over mere techniques. He strives to create a simplified but essentially correct model of the reality that he studies. Venkat’s research interests are diverse, ranging from AI to systems engineering to theoretical physics to economics, but they are generally focused on the theme of understanding complexity and emergent behavior in different domains.
Venkat received the Norris Shreve Award for Outstanding Teaching in Chemical Engineering three times at Purdue University. He won the Computing in Chemical Engineering Award from AIChE and is a Fellow of AIChE. In 2011, the College of Engineering at Purdue University recognized his contributions with the Research Excellence Award. He served as an Editor for Computers & Chemical Engineering for over a decade. Three of his papers are among the ten most-cited papers in the 43-year history of Computers & Chemical Engineering. His book on economics, How Much Inequality is Fair? Mathematical Principles of a Moral, Optimal, and Stable Capitalist Society, was published in 2017. Venkat’s other interests include comparative theology, classical music, and cricket.