I am a fourth-year PhD student in Computer Science at Stanford University advised by Prof. Chris Piech.
My research interests center broadly around enabling high-quality education at a global scale, with a specific interest in scaling the human and social dynamics of learning to these settings. My work often draws on tools from machine learning, game theory, and human-computer interaction.
Separetely, I also enjoy doing research in theoretical computer science and have worked on several problems in computational complexity and learning theory with Prof. Li-Yang Tan.
I have a deep love for teaching and learning. As an undergraduate, I was fortunate to lecture a class on industry C++ programming! I also keep a blog on the side where I write about random Maths, CS, and Philosophy related gems that come my way.
Here are some links to the class websites of the courses I have taught at Stanford so far. If you have any questions or suggestions for improvements, please feel free to email me!
Proceedings of the 55th Annual ACM Symposium on Theory of Computing (STOC), Orlando, USA. 2023
Proceedings of the 39th International Conference on Machine Learning (ICML), Balitmore, USA. 2022
Proceedings of the 35th Annual Conference on Learning Theory (COLT), London, UK. 2022
Proceedings of the 14th International Conference on Educational Data Mining (EDM), Paris, France. 2021
Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE), (Virtual) USA, 2021.
Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA. 2020.
Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, USA. 2019
Proceedings of the 12th International Conference on Educational Data Mining (EDM), Montréal, Canada. 2019
A secure, reliable, cross-platform desktop application to administer computerised examination for large classes. Has been used to administer over 5000 exams in Stanford's introductory CS classes.
Exploring the effectiveness of deep generative recurrent networks in the task of understanding and generating motion. In particular, we attempt to generate GIFs of realistic mechanical motion on a synthetic dataset from an initial seed frame.