Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Kush Varshney

Kush Varshney
Principal Research Staff Member and Manager, IBM Research

Website

Kush R. Varshney was born in Syracuse, NY in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, NY, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow.

Dr. Varshney is a research staff member and manager with IBM Research AI at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the Learning and Decision Making group. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of statistical signal processing and machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences.

Sessions

9:00am12:30pm Tuesday, April 16, 2019
Implementing AI
Location: Regent Parlor
Secondary topics:  Deep Learning and Machine Learning tools, Ethics, Privacy, and Security
Rachel Bellamy (IBM Research), Kush Varshney (IBM Research), Karthikeyan Natesan Ramamurthy (IBM), Michael Hind (IBM Research AI)
Learn to use and contribute to the new open-source Python package AI Fairness 360 directly from its creators. Architected to translate new developments from research labs to data science practitioners in industry, this is the first comprehensive toolkit with metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias. Read more.