The safety, integrity, and well-being of users, communities, and platforms on web and social media is a critical, yet challenging task. In this talk, I will describe the cybersafety-focused machine learning methods, leveraging behavior modeling, graph analytics, and deep learning, that my group has developed to efficiently detect malicious users and bad content online. While developing models that are highly accurate is important, it is also crucial to ensure that the systems are trustworthy. Thus, I will describe my group's work on quantifying the reliability of cybersafety models against smart adversaries that are popularly used in practice, such as those in Facebook.
About the Speaker
Srijan Kumar is an Assistant Professor at the College of Computing at Georgia Institute of Technology. He develops data science, machine learning, and AI solutions for the pressing challenges pertaining to the safety, integrity, and well-being of users, platforms, and communities in the cyber domain. He has pioneered the development of user models and network science tools to enhance the well-being and safety of users. His methods have been used in production at Flipkart (India's largest e-commerce platform) and taught at graduate level courses worldwide. He has named to the Forbes 30 under 30 Class of 2022 and has received several awards including the Facebook Faculty Award, Adobe Faculty Award, ACM SIGKDD Doctoral Dissertation Award runner-up 2018, Larry S. Davis Doctoral Dissertation Award 2018, and 'best of' awards from WWW and ICDM. His research has been the subject of a documentary and covered in popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. He completed his postdoctoral training at Stanford University, received a Ph.D. in Computer Science from University of Maryland, College Park, and B.Tech. from Indian Institute of Technology, Kharagpur.