Raymond Dueñas
PhD Student | Computer Science and Engineering at UCSD
Hello!
I’m Raymond Dueñas, a second-year Ph.D. student in the Department of Computer Science and Engineering at the University of California San Diego, where I’m fortunate to be co-advised by Dr. Pat Pannuto and Dr. Ryan Kastner.
My research interests lie at the intersection of embodied AI, computer vision, and algorithmic development for improved generalization in machine learning. I’m particularly focused on developing heterogeneous computing pipelines that strategically distribute neural network layers across CPUs and GPUs to optimize inference performance and resource utilization.
Prior to joining UCSD, I completed my undergraduate studies at California State University, Stanislaus, with dual majors in Mathematics and Computer Science, graduating as an Honors student. During my undergraduate career, I was honored to be a McNair Scholar, a Cal-Bridge Scholar, and an NSF Louis Stokes Alliances for Minority Participation (LSAMP) Scholar. I was also nominated as a 2022–2023 CSU-LSAMP PROUD Scholar for Outstanding Academic Performance and Outstanding Service. I was especially honored to receive the Outstanding Scholar of Mathematics award for my graduating class, recognizing my achievements in the Bachelor of Science in Mathematics program.
As an undergraduate, I worked with Dr. Kyu Han Koh in the Innovative Learning and Design Lab on computer vision applications for climate data analysis and visualization. I also collaborated with Dr. Adham Atyabi at the University of Colorado Colorado Springs on research utilizing computer vision and deep learning for autonomous drone flight with optical flow for obstacle avoidance.
Projects at a Glance
UnifiedSplitting: Practical Pipeline-Parallel Inference on Unified Memory Architectures
Exploring resource utilization optimization for deep learning inference on modern edge computing platforms, this project investigates strategies for leveraging multiple processing units in unified memory architectures. The work focuses on empirical analysis of computational workload characteristics across different neural network components to identify opportunities for improved throughput. The work democratizes implementation and requires no model modifications. This research is conducted under the co-advisement of Dr. Pat Pannuto and Dr. Ryan Kastner in the Department of Computer Science and Engineering at the University of California San Diego.
Optical Flow for Obstacle Avoidance in Autonomous Drone Flight
Utilizing a monocular quadcopter, this project aims to implement a pair of convolution neural networks to produce an autonomous flight controller that will successfully navigate the drone to its destination. The first CNN will produce optical flow values. Accepting these values as inputs, the second CNN will make the optimal directional flight decision to get to its destination safely. This work was started under the advisement of Dr. Adham Atyabi during the summer of 2022 at the University of Colorado Colorado Springs.
Computer vision for Analysis and Visualization of Climate Change Trajectory
Implementing computer vision practices and the OpenCV library on images rendered from data sets containing fifty years of climate information, this project aims to track the trajectory of the observable effects of climate change. This work is conducted under the advisement of Dr. Kyu Han Koh as a project of his Innovative Learning and Design Lab.
Fitting a change point model for circular vascular mortality in LA County
Implementing time series analysis and change-point regression techniques on a decade of Los Angeles County mortality data, this project aims to identify structural breaks in cardiovascular death patterns while accounting for environmental covariates. The methodology employs R statistical computing to fit autoregressive models with temperature and pollution variables, demonstrating that change-point models provide significantly superior fitting compared to linear trend approaches. Through systematic F-value optimization across 507 potential change points, the analysis reveals a distinct mortality shift at week 262, with AR(2) error modeling achieving robust residual correlation below 0.10 tolerance thresholds. This work was conducted under the advisement of Dr. Yangong Wu in the Department of Mathematics and Computer Science at California State University Stanislaus.
