- Website: https://thashim.github.io/
I am currently an assistant professor at the computer science department in Stanford university.
My research uses tools from statistics to make machine learning systems more robust and trustworthy — especially in complex systems such as large language models. The goal of my research is to use robustness and worst-case performance as a lens to understand and make progress on several fundamental challenges in machine learning and natural language processing. A few topics of recent interest are,
Long-tail behavior
How can we ensure that a machine learning system won’t fail catastrophically in the wild under changing conditions?
Understanding
A system which understands how to answer questions or generate text should also do so robustly out-of-domain.
Fairness
Machine learning systems which rely on unreliable correlations can result in spurious and harmful predictions.
Previously, I was a post-doc at Stanford working for John C. Duchi and Percy Liang on tradeoffs between the average and worst-case performance of machine learning models. Before my post-doc, I was a graduate student at MIT co-advised by Tommi Jaakkola and David Gifford and a undergraduate student at Harvard in statistics and math advised by Edoardo Airoldi.