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== Agenda ==
== Agenda ==
* 12:00-12:05 '''[[RamSriram|Ram D. Sriram]]''' ''Healthcare Applications Panel''
* 12:00-12:05 '''[[RamSriram|Ram D. Sriram]]''' ''Healthcare Applications Panel''
* 12:05-12:35 '''Venkat Venkatasubramanian''' ''Use of ontologies in Hybrid AI systems for Pharmaceutical Engineering''
* 12:05-12:35 '''Venkat Venkatasubramanian''' ''Use of ontologies in Hybrid AI systems for Pharmaceutical Engineering''Ontology-based Machine Learning in Pharmaceutical Engineering
 
Venkat Venkatasubramanian
Department of Chemical Engineering,
Columbia University, New York, NY 10027
 
Abstract
 
The startling success of ChatGPT and transformer-based generative neural networks in
applications such as natural language processing and image synthesis has many researchers
excited about the potential opportunities in pharmaceutical engineering. However, there is an
essential difference between such applications and pharmaceutical engineering. The latter is
governed by fundamental laws of physics, chemistry, and biology, constitutive relations, and
highly technical knowledge about materials, processes, and systems. While purely data-driven
machine learning has its immediate uses, the long-term success of AI here would depend on
leveraging first principles and technical knowledge effectively using ontologies. In this talk, I will
discuss these challenges and opportunities going forward.
 
Bio:
Professor 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 many years 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
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 centered around 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 is a past president of the CACHE Corporation.
From 2009-19, he served as Editor for Computers and Chemical Engineering. His book, How Much
Inequality is Fair? Mathematical Principles of a Moral, Optimal, and Stable Capitalist Society, was
published in 2017. Three of his papers are among the ten most-cited papers in the 46-year history of
Computers & Chemical Engineering. Venkat's other interests include comparative theology, classical
music, and cricket.
* 12:35-13:00 '''Kaushik Roy''' - Topic and Brief Bio: Kaushik Roy is a fourth-year Doctoral student at the AI Institute at the University of South Carolina. His research interests include developing algorithms to combine statistical data-driven learning mechanisms with curated domain-specific information from external knowledge sources, focusing on social good applications. He has published several papers and delivered talks at premier venues on his work on AI for mental health assessment assistance, and other social good applications.
* 12:35-13:00 '''Kaushik Roy''' - Topic and Brief Bio: Kaushik Roy is a fourth-year Doctoral student at the AI Institute at the University of South Carolina. His research interests include developing algorithms to combine statistical data-driven learning mechanisms with curated domain-specific information from external knowledge sources, focusing on social good applications. He has published several papers and delivered talks at premier venues on his work on AI for mental health assessment assistance, and other social good applications.


Revision as of 06:51, 23 April 2024

Session Applications
Duration 1 hour
Date/Time 24 Apr 2024 16:00 GMT
9:00am PDT/12:00pm EDT
4:00pm GMT/6:00pm CEST
Convener Ram D. Sriram

Ontology Summit 2024 Applications

Agenda

  • 12:00-12:05 Ram D. Sriram Healthcare Applications Panel
  • 12:05-12:35 Venkat Venkatasubramanian Use of ontologies in Hybrid AI systems for Pharmaceutical EngineeringOntology-based Machine Learning in Pharmaceutical Engineering

Venkat Venkatasubramanian Department of Chemical Engineering, Columbia University, New York, NY 10027

Abstract

The startling success of ChatGPT and transformer-based generative neural networks in applications such as natural language processing and image synthesis has many researchers excited about the potential opportunities in pharmaceutical engineering. However, there is an essential difference between such applications and pharmaceutical engineering. The latter is governed by fundamental laws of physics, chemistry, and biology, constitutive relations, and highly technical knowledge about materials, processes, and systems. While purely data-driven machine learning has its immediate uses, the long-term success of AI here would depend on leveraging first principles and technical knowledge effectively using ontologies. In this talk, I will discuss these challenges and opportunities going forward.

Bio: Professor 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 many years 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 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 centered around 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 is a past president of the CACHE Corporation. From 2009-19, he served as Editor for Computers and Chemical Engineering. His book, How Much Inequality is Fair? Mathematical Principles of a Moral, Optimal, and Stable Capitalist Society, was published in 2017. Three of his papers are among the ten most-cited papers in the 46-year history of Computers & Chemical Engineering. Venkat's other interests include comparative theology, classical music, and cricket.

  • 12:35-13:00 Kaushik Roy - Topic and Brief Bio: Kaushik Roy is a fourth-year Doctoral student at the AI Institute at the University of South Carolina. His research interests include developing algorithms to combine statistical data-driven learning mechanisms with curated domain-specific information from external knowledge sources, focusing on social good applications. He has published several papers and delivered talks at premier venues on his work on AI for mental health assessment assistance, and other social good applications.

Conference Call Information

  • Date: Wednesday, 24 April 2024
  • Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
  • Expected Call Duration: 1 hour

The unabbreviated URL is: https://us02web.zoom.us/j/87630453240?pwd=YVYvZHRpelVqSkM5QlJ4aGJrbmZzQT09

Participants

Discussion

Resources

Previous Meetings

 Session
ConferenceCall 2024 04 17Applications
ConferenceCall 2024 04 10Synthesis
ConferenceCall 2024 04 03Synthesis
... further results

Next Meetings

 Session
ConferenceCall 2024 05 01Risks and Ethics
ConferenceCall 2024 05 08Risks and Ethics
ConferenceCall 2024 05 15Synthesis
... further results
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