I am an associate research scientist at the Center for Computational Mathematics of the Flatiron Institute. I work in machine learning and on the theory of computing, with a focus on optimization and sampling algorithms, and in statistical mechanics. Most of my work is motivated by problems in physics, biophysics, and the neuroscience of vision. I also enjoy thinking about probability in high dimensions.
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. Prior to my current appointment, I held a postdoctoral fellowship at the Flatiron Institute.
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.