Raymond Dueñas
PhD Student | Computer Science and Engineering at UCSD
Open to Summer 2027 research & industry internships · r1duenas@ucsd.edu
Hello!
I’m Raymond Dueñas, a fourth-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 focuses on making AI inference fast and efficient on the hardware people already have: edge devices, embedded platforms, and heterogeneous systems that pair CPUs with GPUs. I study how deep learning and large language model (LLM) workloads behave across processors and devices, and I build systems that run each part of that work where it performs best.
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 in 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.
At a glance
- Research areas: ML systems · efficient LLM and deep learning inference · edge and embedded AI · heterogeneous CPU/GPU computing · distributed inference · GPU performance profiling
- Tools: Python, C++, CUDA, PyTorch, Hugging Face Transformers, TensorRT, llama.cpp, Vulkan, Docker, Kubernetes (K3s), Linux, Git, NVIDIA Nsight Compute/Systems
- Infrastructure: cluster system administrator for the lab’s ~40-device ARM64 edge-AI research cluster: Kubernetes (K3s), networking, OS images, and access management for student researchers
- Leadership: led research projects with a team of 20 students; mentored four undergraduate researchers
- Fellowships: Cal-Bridge Fellow (2023–present); UCSD CSE Department and Rotational Fellowships (2023)
Open to research and industry internships for Summer 2027. Email: r1duenas@ucsd.edu
Projects at a Glance
UnifiedSplitting: CPU–GPU Co-Inference on Edge AI Platforms
Edge AI platforms such as NVIDIA Jetson place the CPU and GPU on a single shared memory. UnifiedSplitting explores how to divide deep learning inference across those processors to get more throughput from the same device, with no changes to the model. I build the inference pipelines in Python and PyTorch and profile them with NVIDIA Nsight Systems and Nsight Compute. Presented as a lightning talk and poster at the UCSD CNS Research Review (May 2025). This research is co-advised by Dr. Pat Pannuto and Dr. Ryan Kastner in the Department of Computer Science and Engineering at the University of California San Diego.
Junkyard GPU: Distributed LLM Inference on Repurposed Devices
Building a low-cost cluster of repurposed consumer devices that pool their compute to run large language models locally, without a data center.
Optical Flow for Obstacle Avoidance in Autonomous Drone Flight
Utilizing a monocular quadcopter, this project aimed to implement a pair of convolutional neural networks (CNNs) to produce an autonomous flight controller that successfully navigates the drone to its destination. The first CNN produces optical flow values. Accepting these values as inputs, the second CNN makes 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 aimed to track the trajectory of the observable effects of climate change. This work was 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 Cardiovascular Mortality in LA County
Implementing time series analysis and change-point regression techniques on a decade of Los Angeles County mortality data, this project aimed 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. Yanhong Wu in the Department of Mathematics at California State University Stanislaus.
