PhD Student · UMass Amherst · Berkeley Lab
I'm a third-year Computer Science PhD student at the University of Massachusetts Amherst (UMass), working with Prof. VP Nguyen and Prof. Deepak Ganesan. I am also an affiliated PhD student at Berkeley Lab, where I work on computer architecture and heterogeneous computing for real-time quantum systems, including machine learning-based quantum state classification under a sub μs latency budget, hosted by Dr. Yilun Xu.
I am broadly interested in HPC, computer architecture, and heterogeneous computing, with a focus on hardware-software co-design for real-time AI inference and scientific computing. My research explores how we can exploit key features of AI Engines, FPGAs, CPUs, GPUs and emerging hardware to efficiently execute compute-intensive & memory bounded workloads under strict latency and resource constraints, with applications in quantum computing and other performance-critical systems.
A heterogeneous computing pipeline for real-time recurrent ML inference on a spatial AI accelerator. Independent computation is parallelized on an AI Engine array while the sequential state update (biggest bottnecks) runs in FPGA programmable logic, targeting a sub-microsecond latency budget. Outperform prior hardware deployed SOTA in readout fidelity while using orders of magnitude less hardware resources (DSPs).
A study of latency-bounded deep learning inference on spatial AI accelerators like AMD AI Engines coupled with reconfigurable computing (FPGAs), characterizing and modeling timing behavior to support deployments with strict real-time guarantees for physical AI workloads like VLAs.
My research focuses on heterogeneous computing for real-time AI inference — combining AI engines, FPGAs, and accelerators to meet hard latency constraints. I'm broadly interested in computer architecture, quantum computing systems, deep learning, and scalable networked systems. Some papers are
2025
ACM MobiSys 2025 · A* conference
LiSWARM is a low-cost LiDAR system for accurate 3D tracking and recognition of drones in large swarms. Using point cloud processing, clustering, and neural networks, it achieves up to 98% accuracy and scales to 15,000 drones—enabling applications in airspace security, drone shows, and sensitive area monitoring.
2024
ACM SenSys 2024 · A* conference
MagicStream, a first-of-its-kind semantic-driven immersive telepresence system that effectively extracts and delivers compact semantic details of captured 3D representation of users, instead of traditional bit-by-bit communication of raw content.
2024
CVF/WACV 2024 · A conference
Using vision transformers for out-of-distribution data face identification, runs twice faster while achieving comparable performance with the state of the art DeepFace-EMD model.
Presented research on real-time qubit readout to LBNL and AMD in Berkeley, CA.
Presented “Opportunities in Computer Systems Research for Quantum Computing” at ACM QSys 2025 (in conjunction with ACM MobiSys 2025).
One paper accepted at ACM MobiSys 2025.
I officially become a research affiliate with Berkeley Lab.
My new academic website with the vule.us domain is live now.
One paper accepted at ACM SenSys 2024.
I joined University of Massachusetts Amherst, USA as a PhD student.
I received some CS PhD offers in the US.
One paper accepted at IEEE/CVF WACV.
I graduated with a bachelor's degree from Vietnam National University, Hanoi.
Apart from research, I'm an experienced software and DevOps engineer who enjoys building scalable backend systems. Outside of work, I'm an avid adventurer — I love road trips and have driven across the US to explore national parks and trails firsthand. Most of my hikes and adventures (Grand Canyon, Zion, Death Valley, Horseshoe Bend, Joshua Tree, the White Mountains in New Hampshire) were made possible by hitting the road. I also really enjoy lifting at the gym, jogging, brewing coffee, planting flowers, and skiing. I shoot with a Sony A6400 and a collection of Sigma and Sony lenses. Check out my photo gallery.