I'm Lucius Vo — a Ph.D. candidate at Oklahoma State University working at the seam where neuromorphic hardware, in-memory computing, and machine learning meet.
Begin readingI grew up in Vietnam, learned to be an engineer in Finland, and learned to be a researcher in three places I never expected. This site is a small exhibition of that path — and a window onto what I am building next: machines that no longer separate memory from thought.
— L. V., Stillwater, MMXXVIFrom a childhood in Vietnam to research benches in Finland, factory floors at Intel, and now the prairies of Oklahoma — each place reshaped the questions I wanted to ask.
A childhood between books and circuit boards
Long before any of the labs that follow, there was a kid in Vietnam taking radios apart. The questions that drive my research today started there — quietly, and without language for them yet.
B.Tech., Electrical & Electronics Engineering
My first real engineering apprenticeship. I built a wireless sensor system for excavator safety, taught myself the patience that hardware demands, and earned Tutor of the Year for the company I kept among incoming students.
M.Sc., Automation & Electrical Engineering — Data Science
At Aalto's Metrology Research Institute I worked on the differential spectral responsivity of solar cells with Petri Kärhä and Erkki Ikonen. Quietly, in parallel, I fell into machine learning — and never came out. Graduated with honors and a thesis on deep learning for fault diagnosis in PV modules.
Manufacturing & Deep Learning Engineer
I returned home to put the research to work. At First Solar I built defect-commonality tooling that is still used across their factories. At Intel I led the ILLIAD inspection system — featured at the 2023 AI Everywhere Conference — and mentored Python and ML across the company's AI Everywhere community.
Ph.D., Industrial Engineering & Management
My current home. I work on resistive random-access memory, in-memory computing, and the optimization algorithms that can take advantage of both. Two-time finalist in the IISE student paper competition, SIAM travel awardee for PP26, and currently VP of the OSU INFORMS chapter.
Ph.D. Research Student Aide
A summer at a national lab, asking whether RRAM crossbars can solve the linear programs that move electricity across the U.S. grid. The preliminary answer became a paper accepted at SIAM PP26.
My research lives at the meeting of three disciplines. Resistive random-access memory (RRAM) gives us a substrate where computation and storage are no longer separate things. Analog & in-memory computing turns that substrate into a working machine. And optimization & learning ask what such a machine could solve that GPUs cannot.
I am building the bridge between the three — designing distributed primal-dual algorithms that run natively on RRAM crossbars, and chasing the day when neuromorphic hardware quietly outperforms a rack of GPUs on the world's hardest linear programs.
A glimpse at what currently occupies my desk. The full list lives in publications.
We present a distributed primal-dual hybrid gradient (PDHG) method co-designed for resistive random-access memory (RRAM) in-memory computing, enabling large-sc…
We introduce MELISO+: a distributed, full-stack RRAM in-memory computing framework with integrated two-tier error correction for scalable, high-dimensional mat…
We present SplitVAE, a decentralized scenario generation framework that leverages variational autoencoders to generate high-quality scenarios without moving st…
We introduce MELISO (In-Memory Linear Solver), a comprehensive end-to-end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the erro…
Always glad to meet new collaborators — for research, mentoring, or simply a conversation about David's brushwork over coffee.