Research Areas

Evolutionary Neural Architecture Search

I develop neuroevolution methods that automatically design recurrent neural networks, with a focus on discovering compact architectures that rival much larger hand-designed models. My work emphasizes ultra-lightweight designs suitable for edge deployment, where memory, power, and latency constraints rule out conventional deep learning pipelines.

neuroevolution RNN design edge AI

EXAMM Neural Architecture

Time Series Forecasting

I work on forecasting methods for multivariate time series across both offline and online settings. In the offline regime, my work focuses on designing accurate, compact models trained on historical data; in the online regime, I develop methods that adapt continuously as data distributions shift over time. Applications span industrial sensor data (coal-fired power plant operations) and financial forecasting (stock return prediction and portfolio trading).

multivariate time series offline and online forecasting non-stationary data

ONE-NAS: Online NeuroEvolution for time series

Cross-Disciplinary Edge AI Applications

A growing line of my work applies neuroevolved lightweight models to domains outside traditional ML benchmarks, partnering with researchers in engineering, energy, and finance. The goal is forecasting and decision-support systems that run on the device where the data is generated.

applied ML interdisciplinary collaboration resource-constrained inference

An evolved RNN architecture produced by EXAMM

AI Education & Pedagogy

I research how to broaden AI literacy beyond computer science majors, and I founded the Kean Cup AI Competition as a venue for students across disciplines to work on applied AI projects. This line of work explores curriculum design, assessment, and the pedagogy of AI for non-specialists.

AI literacy interdisciplinary education