The Inaugural CAIT Research Showcase
Columbia University Center of AI Technology in Collaboration with Amazon
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Thursday, June 17th
12:15 - 2:00 pm PT/3:15 - 5:00 pm ET
Online
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The Center of AI Technology in collaboration with Amazon invites you to join us for our Inaugural Annual Research Showcase. The showcase will highlight exciting research progress in emerging topics of AI technology including the following:
- Using speech and language to identify patients at risk
- Counterfactual reinforcement learning
- Natural language processing for extremely abstractive summarization
- Sequential decision making under uncertainty
- Sound and vision integration to improve machine perception
- Fairness and incentives in machine learning
- Inventory control for multi-location and multi-product systems
To attend the Showcase, please register at the link below.
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12:15 PT/3:15 ET
Opening Remarks
12:17 PT/3:17 ET
Using speech and language to identify patients at risk for hospitalizations and emergency department visits in homecare
Zoran Kostic, Maxim Topaz, & Maryam Zolnoori
Presented by Max Topaz & Maryam Zolnoori | School of Nursing
This study is the first step in exploring an emerging and previously understudied data stream - verbal communication between healthcare providers and patients. In partnership between Columbia Engineering, School of Nursing, Amazon, and the largest home healthcare agency in the US, the study investigates how to use audio-recorded routine communications between patients and nurses to help identify patients at risk of hospitalization or emergency department visits.
12:32 PT/3:32 ET
Counterfactual reinforcement learning for personalized decision-making
Elias Bareinboim
Presented by Adele Ribeiro | Department of Computer Science, Columbia Engineering & Data Science Institute
One pervasive task found through data-driven fields (including medical research, education, business analytics) is the problem of personalized decision-making, i.e., to determine whether a certain intervention will lead to a desirable outcome based upon the individual's characteristics and experiences. This project develops new machinery for advancing the state-of-the-art of personalized policy learning through causal lenses.
12:47 PT/3:47 ET
Extremely abstractive summarization
Kathleen McKeown
Presented by Kathleen McKeown & Fei-Tzin Lee | Department of Computer Science, Columbia Engineering
Most research in text summarization today focuses on summarization of news articles and for this genre, much of the wording in the summary is directly copied from the summarized article. In contrast, in many other genres, the summary uses language that is very different from the input. This project develops methods to enable generation of three forms of abstraction: paraphrasing, compression and fusion; it aims to develop separate models for each and compare with a joint learning approach.
1:02 PT/4:02 ET
Amazon Fellow: Noemie Perivier | Department of Industrial Engineering and Operations Research, Columbia Engineering
Interests: Sequential decision making under uncertainty, design of online algorithms in data-rich environments, with applications in revenue management problems
1:12 PT/4:12 ET
Amazon Fellow: Mia Chiquier | Department of Computer Science, Columbia Engineering
Interests: Computational framework that integrates sound and vision; to improve current machine perception systems by adopting a more integrated understanding of agents in environments
1:22 PT/4:22 ET
Fairness and incentives in machine learning
Christos Papadimitriou & Tim Roughgarden
Presented by Christos Papadimitriou | Department of Computer Science, Columbia Engineering
This project uses machine learning, algorithms, and social science techniques to explore through analysis and experiment ways in which the tremendous power of machine learning can be applied to render machine learning more fair. Can deep nets be trained through synthetic fairness criticism to treat their data more equitably, and can the unfair treatment of subpopulations be discovered automatically? How can one predict and mitigate the detrimental effect a classifier can have on people by incentivizing them to modify their behavior in order to "game" the classifier?
1:37 PT/4:37 ET
Inventory control for multi-location and multi-product systems
Awi Federgruen, Charles Daniel Guetta, & Garud Iyengar
Presented by Charles Daniel Guetta | Division of Decision, Risk, and Operations, Graduate School of Business
Inventory management is as old as retail - keeping too much inventory on hand results in locking up capital, and incurring high storage costs; keeping too little risks selling out, losing revenue, and customer dissatisfaction. Retail has changed in significant and dramatic ways over the last two decades. This project builds upon a long line of research on this problem, and extends it to be able to cope with the myriad new faces of retail and fulfillment in the 21st century.
1:52 PT/4:52 ET
Closing Remarks by Shih-Fu Chang, CAIT Director and Senior Executive Vice Dean, Columbia Engineering
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The mission of the Columbia University Center of Artificial Intelligence Technology in Collaboration with Amazon is to better society through the development and adoption of advanced AI technology contributing to a more secure, connected, creative, sustainable, healthy and equitable humanity.
Visit the CAIT website |
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