Ph.D. Candidate · Electrical and Computer Engineering
University
of California San Diego
I am a Ph.D. candidate at UC San Diego, advised by Prof. Tajana Rosing in the Systems Energy Efficiency Lab (SEELab). My research focuses on computer architecture and hardware–software co-design, particularly processing-in-memory, heterogeneous memory systems, and efficient AI and graph computing.
In summer 2026, I was an Emerging Memory AI Infrastructure Intern at SK hynix, where I worked on GLAIM and developed its trace-driven simulator for agentic AI inference. Previously, I received my M.S. from Tsinghua University and B.E. from Jilin University, with research in flexible electronics and medical image analysis.
I anticipate graduating in June 2027 and am seeking roles in computer architecture, performance modeling, and AI systems.
* Equal contribution. Google Scholar
Emerging Memory AI Infrastructure Intern
Developed a trace-driven, event-driven simulator for heterogeneous agentic AI systems, integrating LLM prefill/decode, CPU tool execution, near-memory retrieval, persistent state, and interconnect contention. Modeled hierarchical scheduling and compatible-request batching, with reproducible trace collection and replay across five agentic and retrieval benchmarks; this work led to the first-author GLAIM manuscript, submitted to ASPLOS 2027.
Graduate Researcher · Advisor: Prof. Tajana Rosing
Hardware–software co-design, processing-in-memory, in-storage processing, graph analytics, hyperdimensional computing, and database acceleration.
Worked on memristor fabrication and a multimodal sensing glove, with a focus on MEMS/NEMS fabrication and biomedical signal processing.
Developed wearable acetone gas sensors for breath analysis and laser-induced graphene flexible strain sensors, combining semiconductor devices, materials, and microfabrication.
Algorithm Intern · Department of Algorithm. Developed OCT imaging methods integrating near-infrared spectroscopy with machine learning for lipid imaging.
Through MITACS at University of Western Ontario, I contributed to a neuroscience project using texture analysis to extract microvascular signals from mouse heart videos and reconstructed 3D images—enabling cardiac blood flow heterogeneity diagnosis.
Presented two papers at EMBC 2020: “Quantifying Microvascular Alterations Due to a Pharmacological Agent” and “Fractal Dimension-Based Texture Analysis of CT Perfusion Imaging.”
Visited School of Mathematical Sciences and School of Electrical & Electronic Engineering at NUS and NTU. Attended lectures on advanced semiconductor materials and had discussions with professors. Experienced Singapore culture.
Attended workshops on micromaterials and devices at HKU, HKUST, and CityU. Learned from Prof. Li-Hsing Chen on brain-machine interfaces, visited 5G research labs, and saw MIT Robotics Atlas demonstration. This experience broadened my interdisciplinary research perspective.
Ph.D. in Electrical and Computer Engineering
Advisor: Prof. Tajana Rosing · Anticipated graduation: June
2027
GPA: 3.6 / 4.0 · Advanced to candidacy: March 2026
M.S. in Electronic Information (Intelligent Manufacturing)
GPA: 3.78 / 4.0
B.E. in Electronic Science and Technology · College Honors
GPA: 3.53 / 4.0 · Rank: 6 / 108
B.A. in International Economy and Trade, 2018–2020