Sponsor: IEEE Computer Society – San Diego Chapter
Speaker: Srijan Kumar, Ph.D. of Georgia Institute of Technology

Meeting Date: 21 Feb 2023
Time: 05:30 PM to 06:30 PM
Cost:
Location: TBD
Reservations: IEEE
Summary:
The safety, integrity, and well-being of users, communities, and platforms on the web and social media is a critical, yet challenging task. In this talk, I will describe the AI and machine learning methods, advancing natural language processing, graph machine learning, and adversarial machine learning, that my group has developed to efficiently fight malicious users and bad content online. I will talk about the four main pillars of my research: 1) Detection: developing multi-lingual, multi-modal, and multi-platform detection models; 2) Robustness: developing adversarially robust detection models; 3) Attribution: quantifying harms and impact of bad actors; 4) Mitigation: developing solutions and tools to mitigate online harms.

Bio: Srijan Kumar is an Assistant Professor at the College of Computing at the Georgia Institute of Technology. He completed his postdoctoral training at Stanford University, received a Ph.D. and M.S. in Computer Science from the University of Maryland, College Park, and B.Tech. from the Indian Institute of Technology, Kharagpur. He develops Data Mining methods to detect and mitigate the pressing threats posed by malicious actors (e.g., evaders, sockpuppets, etc.) and harmful content (e.g., misinformation, hate speech etc.) to web users and platforms. His methods have been used in production at Flipkart (India’s largest e-commerce platform) and influenced Twitter’s Birdwatch system. He has been selected as a Kavli Fellow by the National Academy of Sciences, named as Forbes 30 under 30 honoree in Science, ACM SIGKDD Doctoral Dissertation Award runner-up 2018, and best paper honorable mention award from the ACM Web Conference. He has also won the Adobe Faculty Award and the Facebook Faculty Award. His research has been covered in the popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine.