CAI Seminar Series

Upcoming

September 21, 2022

7:00PM - 8:00 PM

Online

Talk on "Scalable, Accurate, Robust Binary Analysis with Transfer Learning"by Dr. Suman Jana, from Columbia University

Speaker Name: Dr. Suman Jana

Suman Jana is an associate professor in the department of computer science and the data science institute at Columbia University. His primary research interest is at the intersection of computer security and machine learning. His research has received six best paper awards, a CACM research highlight, a Google faculty fellowship, a JPMorgan Chase Faculty Research Award, an NSF CAREER award, and an ARO young investigator award.

Past

July 11, 2022

3:30 PM

Offline

Talk on " New Opportunities in Automating AI" in the Seminar series of CAI by Dr. Lisa Amini, Director of IBM Research Cambridge

Speaker Name: Dr. Lisa Amini

Dr Lisa Amini is the Director of IBM Research Cambridge, which is home to the MIT-IBM Watson AI Lab, and of IBM's AI Horizons Network. She also leads IBM's AI Automation and Scaling Research efforts globally and is an IBM Distinguished Engineer. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York. She was also the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role, she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains.

June 21, 2022

7:30 PM - 8:30 PM

Online

Talk on "Guaranteed adversarially robust training of neural networks"by Dr. Raman Arora, from Johns Hopkins University

Speaker Name: Prof. Raman Arora

Raman Arora is an assistant professor in the Department of Computer Science at Johns Hopkins University where he is also affiliated with the Mathematical Institute for Data Science (MINDS), the Center for Language and Speech Processing (CLSP), and the Institute for Data-Intensive Engineering and Science (IDIES). Prior to joining Johns Hopkins, Raman was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), a visiting researcher at Microsoft Research, Redmond, and a research associate at the University of Washington, Seattle. He received his Ph.D. from the University of Wisconsin-Madison. Raman’s research interests are in machine learning, online learning, robustness, and privacy. He received an NSF CAREER

May 3, 2022

7:30PM - 8:30 PM

Online

Talk on "Making Invisible Visible with Data, ML and Devices" in the Seminar series of CAI by Prof. Ramesh Raskar from MIT Media Lab

Speaker Name: Prof. Ramesh Raskar

Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on AI and Imaging for health and sustainability.

14th March 2022

7:30 PM IST, 9 AM CDT

Online

Talk on "Machine Learning and Logic: Fast and Slow Thinking" in the Seminar series of CAI by Prof. Moshe Vardi, Rice University

Speaker Name: Prof. Moshe Vardi

Prof. Moshe Vardi, Rice University where he is leading an Initiative on Technology, Culture, and Society. His interests focus on automated reasoning, a branch of Artificial Intelligence with broad applications to computer science, including machine learning, database theory, computational-complexity theory, knowledge in multi-agent systems, computer-aided verification, and teaching logic across the curriculum.

2nd Feb 2022

07:30 PM - 08:30 PM

Online

Talk on "AI for social impact: Results from deployments for public health" in the Seminar series of CAI by Prof. Milind Tambe (Harvard University)

Speaker Name: Prof. Milind Tambe

Prof. Milind Tambe presented his work on public health, which is a perfect example of how one can design theoretical solutions to real problems, followed by large-scale deployment & field study.