معرفی
Karthik Sridharan is an Associate Professor in the Department of Computer Science at Cornell University. His research focuses on theoretical machine learning, including online learning, optimization, and statistical learning theory. He holds a PhD from the Toyota Technological Institute at Chicago (2011) and has held postdoctoral roles at the University of Pennsylvania. His work bridges foundational theory with practical applications in optimization and decision-making.
Education:
- PhD, Computer Science (2011), Toyota Technological Institute at Chicago
- MS, Computer Science (2006), SUNY Buffalo
- B.E., Computer Science and Engineering (2004), M. S. Ramaiah Institute of Technology
Research Interests: Dr. Sridharan explores machine learning theory with emphases on online learning dynamics, optimization algorithms, and the theoretical underpinnings of stochastic methods. His work often addresses challenges like adversarial decision-making and the interplay between optimization and sampling techniques.
Recent Trends in Publications: His recent work spans advancements in reinforcement learning with function approximation, minimax analysis of online learning, and the theoretical properties of stochastic gradient descent. These contributions highlight his focus on foundational guarantees for modern machine learning systems.
Awards and Honors:
- Alfred P. Sloan Research Fellow (2018)
- NSF CAREER Award (2018)
- Best Paper Awards at COLT (2019, 2018) and ALT (2019)
- Simons-Berkeley Research Fellowship (2016)
Advising and Grants: He advises PhD students on topics like optimization, machine unlearning, and safety in ML. His grants include NSF CDS&E-MSS and collaborations on robust intelligence. He also serves on program committees for leading conferences like NeurIPS and ICML.
Labs and Teams: His research group contributes to theoretical foundations of machine learning, with active projects on plug-and-play ML systems, learning on graphs, and de-polarizing recommendation systems.
