I am Anastasios Nikolas Angelopoulos, a fourth-year Ph.D. student at the University of California, Berkeley.
I work on theoretical machine learning with applications in vision and healthcare. My goal is to apply modern statistical ideas to increase robustness of black-box models like deep neural networks. I am motivated by medical diagnostics: statistical reliability will become paramount as computer vision and machine learning become ubiquitous in such high-risk settings. My other applied interests include computational imaging and ophthalmology.
I am privileged to be advised by Michael I. Jordan and Jitendra Malik. From 2016 to 2019, I was an electrical engineering student at Stanford University advised by Gordon Wetzstein and Stephen P. Boyd. See my website below.
people.eecs.berkeley.edu/~angelopoulos/