CV
Resume
I am a Ph.D. Candidate with a focus on Generative modeling. I have experience in building Generative Adversarial Networks (GANs), Diffusion Models, Normalizing Flows, LLMs, ViTs, and other models with applications in generation, classification, segemntation, and detection. I have a focus on explicit density models, or invertible generative models. I am deeply interested in data efficiency, in making models more efficiently utilize data, model distillation, as well as generative data. I have experience training large models from scratch, working in high performance computing (HPC), multi-node training, and writing CUDA kernels. I have a strong background in mathematics and utilizing that knowledge into improving machine learning models.
I am available for full time opportunities and collaboration. Please reach out if you are interested.
Education
University of Oregon
Ph.D. in Computer Science
Eugene, OR
current
University of Oregon
M.A. in Computer Science
Eugene, OR
June 2023
Embry-Riddle Aeronautical Univeristy
B.S. Space Physics
Prescott, AZ
Dec 2014
Experience
NVIDIA
Metropolis Intern
Sep 2023 - Mar 2024
- Increase performance on multi-camera multi-object detection systems.
- Improve generatlization of networks to increase accuracy on customer data.
- Generating syntehtic data for training with multiple conditions.
University of Oregon
Graduate Researcher
Sep 2018 - current
- Research of generative models, with a focus on explicit density models.
- Researching attention mechanisms with restricted context window
- Developing better encoding methds for ViTs
Picsart
Research Intern
Jun 2021 - Nov 2022
- Researching Generative Adversarial Networks (GANs)
- Researching distillation methods for Normalizing Flows
Lawrence Livermore National Laboratory
Computational Scholar
Jun 2020 - Sep 2020
- Developed machine learning software to analyze noisy x-ray images
- Developed machine learning system to perform material identification and composition from noisy x-ray images.
Lawrence Livermore National Laboratory
Computational Scholar
Jun 2019 - Sep 2019
- Studied machine learning systems for High Performance Computing (HPC) platforms in the context of visualization
- Developed model for generating interpolation for intermediate visualization.
Oak Ridge National Laboratory
ASTRO Intern
Jun 2018 - Aug 2018
- Integrated ORNL's ADIOS2 data management framework in to LLNL's Ascent library
- Enabled in-line streaming of data between mutli-node HPC systems.
- Work led to development of Visualization as a Service (VaaS) frameworks
Gloyer-Taylor Labs LLC
Engineer and Lead Scientist
Jun 2015 - May 2018
- Performed computational modeling for radiation protection systems on spacecraft using GEANT4 toolkit.
- Performed computational modeling to simulate acoustic dymanics inside rocket engines.
- Lead Phase I and Phase II STTR research to develop hybrid radiation shielding that can convert radiation into power.
- Wrote grants that led to successful Phase I and Phase II fundings.
Publications
Teaching/TA
Course : CS 451/551
Fall 2024
Course : 472/572
Spring 2024
Course : 472/572
Winter 2023
Course : 472/572
Winter 2022
Course : 413/513
Winter 2021
Course : 314
Fall 2020
Course : 322
Fall 2018