I am a second-year Ph.D. student in Computer Science at Oregon State University, advised by Fuxin Li.
I received my bachelor's degree in Computer Science with a minor in Mathematics from California Polytechnic State University, San Luis Obispo. At Cal Poly, I worked
on problems related to self-supervised denoising and novel view synthesis with Jonathan Ventura and
neural networks for solving PDEs with Stathis Charalampidis.
My research interests are in the fields of computer vision and machine learning. In particular, I am interested in
how various visual modalities (i.e, RGB images, point clouds) can be leveraged to reason about and recreate our 3D world.
Most recently, my work has focused on generative modeling of point clouds for 3D shape generation and completion.
I am always interested in meeting and collaborating with new people who share common research interests as me. Please feel free to reach out to me via email to chat about previous or future work.
[09/2023] Our paper "Diverse Shape Completion via Style Modulated Generative Adversarial Networks" was accepted at NeurIPS 2023.
[06/2023] Joined Reality Labs Research, Meta, as a Research Scientist Intern for Summer & Fall 2023.
[06/2023] Successfully passed my Ph.D. qualifying exam.
[04/2021] Accepted a Ph.D. offer at Oregon State University.
We introduce symmetry-preserving, physics-informed neural networks (S-PINNs) motivated by symmetries
that are ubiquitous to solutions of nonlinear dynamical lattices. Through the correlation of parity
symmetries in both space and time of solutions to differential equations with their group equivariant
representation, we construct group-equivariant NNs which respect spatio-temporal parity symmetry. Moreover,
we adapt the proposed architecture to enforce different types of periodicity (or localization) of solutions
to nonlinear dynamical lattices.
We extend Neural Radiance Fields (NeRF) with a cylindrical parameterization and learned exposure compensation technique
that enables rendering photorealistic novel views of casually captured 360-degree outward facing scenes.
We extend the blindspot model for self-supervised denoising to handle Poisson-Gaussian noise and introduce
an improved training scheme that avoids hyperparameters and adapts the denoiser to the test data.