I am a postdoctoral fellow at the Center for Computational Mathematics of the Flatiron Institute. I am broadly interested in the theory of machine learning. Active projects include using ideas from numerical integration to develop computationally efficient adaptive step size schemes for optimization, and studying the statistical efficiency of score estimation in the context of score-based diffusion modeling.
I graduated with a PhD from the University of California, Berkeley in May of 2022. My advisors were Michael I. Jordan and Michael R. DeWeese. I was also affiliated with the Statistical AI Learning group, the Berkeley AI Research group, and the Redwood Center for Theoretical Neuroscience.
In the summer of 2019 I interned at Google Brain, where I was hosted by Jascha Sohl-Dickstein. During the academic years 2018-21, my work was supported by a Google PhD Fellowship.
Before I went to Berkeley, I was a Junior Research Fellow at the National Center for Biological Sciences in Bangalore, India. Before that, I completed a Masters degree in theoretical physics at the Perimeter Institute for Theoretical Physics in Waterloo, Canada. I was an undergraduate at Amherst College, where I received a degree in physics.
You can find me at nwadia at flatironinstitute dot org.
Here is my Google Scholar page.
Machine learning at the Flatiron Institute has its own webpage here.