G. Popoola

Predicting electronic structures at any length scale with machine learning

A. Thompson, S. Rajamanickam, N. Modine, 2022, 2210.11343.

Predicting electronic structures at any length scale with machine learning

A. Thompson, S. Rajamanickam, N. Modine, 2022, npj Computational Materials.

Predicting the Electronic Structure of Matter on Ultra-Large Scales.

A. Thompson, S. Rajamanickam, N. Modine, 2022 .

Remote Work and Satisfaction for Black Engineers and Computer Scientists

Trina L. Fletcher, G. Popoola, Simone Nicholson, 2022, 2022 IEEE Frontiers in Education Conference (FIE).

Training-free hyperparameter optimization of neural networks for electronic structures in matter

S. Rajamanickam, Vladyslav Oles, A. Cangi, 2022, Mach. Learn. Sci. Technol..

LDOS/SNAP data for MALA: Aluminium at 298K and 933K

A. Thompson, S. Rajamanickam, N. Modine, 2021 .

Electronic Structure Machine Learning Surrogates without Training

S. Rajamanickam, Vladyslav Oles, A. Cangi, 2022 .

Finding Machine-Learning Surrogates for Electronic Structures without Training

S. Rajamanickam, Vladyslav Oles, A. Cangi, 2022 .

Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

A. Thompson, S. Rajamanickam, N. Modine, 2020, Physical Review B.

Using Unsupervised Anomaly Detection to Analyze Physiological Signals for Emotion Recognition

Corey A. Graves, Gabriel A. Popoola, Phyllis Ford-Booker, 2018, 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

Using Unsupervised Anomaly Detection to Analyze Physiological Signals for Emotion Recognition

Corey A. Graves, Gabriel A. Popoola, Phyllis Ford-Booker, 2018, 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).