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. During my undergraduate career, I was honored to be a McNair Scholar, NSF Louis Stokes Alliances for Minority Participation (LSAMP) Scholar, Cal-Bridge Scholar, and was awarded the Outstanding Scholar for my graduating class in the Mathematics department for my BS in Mathematics. I was also nominated as a 2022-2023 CSU-LSAMP PROUD Scholar for Outstanding Academic Performance and Outstanding Service.
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
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.
