Jane C. Odum — Ph.D. Candidate in Computer Science

Current Position

I am a Computer Science Ph.D. candidate (expected December 2026) in the NES Lab (Neuro-Symbolic Computing Lab) at the University of Georgia, advised by Dr. John A. Miller. My dissertation develops diffusion-based generative models for probabilistic epidemiological time series forecasting.

In Summer 2025, I interned as a Machine Learning Engineer at Stripe, where I developed and deployed ML models for secret key leak detection — cutting detection latency from 4.5 days to under 1 hour and engineering large-scale feature pipelines in Spark/Databricks processing 500M+ records.

What I Do

I build artificial intelligence for low-resource and resource-constrained environments — settings with sparse data, limited compute, and unreliable connectivity. My work spans generative diffusion models for probabilistic time series forecasting, multi-channel and multimodal learning, on-device and offline inference with medical foundation models, and low-resource African-language NLP. Epidemiological surveillance is where I prove these methods out, because it stresses every one of those constraints at once, but the same approach travels to any setting where decisions have to be made from thin, shifting signals.

EpiCast, a mobile-first surveillance platform for West African community health workers, won first place in the Google Health AI MedGemma Impact Challenge — a $30,000 grand prize, selected first from over 850 teams and 6,500 entrants worldwide.

My recent papers include MAAN: Multi-channel Adaptive Attention Network for Probabilistic Time Series Forecasting (SDM 2026, accepted) and Adaptive Quantile Guidance in Diffusion Models (ICMLA 2025, accepted), which achieved 73.3% lower MAE than state-of-the-art models across pandemic forecasting scenarios.

My Background

Before my Ph.D., I earned a B.Sc. in Computer Science from the University of Ilorin, Nigeria, where I developed a passion for solving problems through code. I later honed my software engineering skills at the 42 Silicon Valley bootcamp in California, gaining experience in building scalable systems and collaborating on complex technical projects.

Now, as a researcher, I blend my engineering mindset with my passion for machine learning. I care most about work that reaches the people who need it — models small enough to run on a health worker’s phone, forecasts honest enough to act on, and tools that do not assume a data centre or a stable network.

I write occasionally on Medium about research, engineering, and building AI systems that survive contact with the real world.

When I am not immersed in research or coding, I love staying active with weight lifting and hiking, getting creative through painting and dancing, and experimenting in the kitchen with new recipes.