Sumegha Garg

Princeton University

Graduate Student

I am a final-year graduate student in Computer Science at Princeton University, advised by Professor Mark Braverman. Before coming to Princeton, I did my Bachelor’s degree in Computer Science and Engineering from Indian Institute of Technology, Delhi.

Research Abstract:

The primary aim of my research has been studying the computation limits of and the role of randomness in space-bounded computational models. In particular, my work has focused on proving for a large class of learning problems the following: a low-memory learning algorithm requires an exponential number of samples to learn. These also give cryptographic protocols with unconditional security against low-space adversaries. The second aim of my research has been algorithmic fairness and investigating the sources of unfairness in classification algorithms. Machine learning algorithms are increasingly being used for making decisions about humans, which raises concerns that these might inadvertently discriminate against certain groups of society. In particular, my work has focused on understanding the role of information in fairness and exploring the mechanisms/incentives that lead to fair predictions.