See my Google Scholar page for the most up-to-date list of publications.

[1] A. Bhargava, C. Witkowski, M. Shah, and M. Thomson. “What’s the Magic Word? A Control Theory of LLM Prompting“, arXiv preprint arXiv:2310.04444, 2023

[2] A. Bhargava, M. R. Rezaei, and M. Lankarany, “Gradient-free neural network training via synaptic-level reinforcement learning,” AppliedMath, vol. 2, no. 2, pp. 185–195, 2022.

[3] A. Bhargava and S. Mann, ”Adaptive Chirplet Transform-Based Machine Learning for P300 Brainwave Classification”, IEEE Engineering in Medicine and Biology Society Conference on Biomedical Engineering and Sciences, 2020 (Accepted & Presented)

[4] A. Bhargava, K. O’Shaughnessy, and S. Mann, ”A Novel Approach to EEG Neurofeedback via Reinforcement Learning”, Proc. IEEE Sensors, 2020 (Accepted & Presented)

[5] A. Bhargava, A. X. Zhou, A. Carnaffan, and S. Mann. “Deep Learning for Enhanced Scratch Input“, arXiv preprint arXiv:2111:15053, 2021

[6] S. Mann, C. Pierce, A. Bhargava, C. Tong, K. Desai, K. O’Shaughnessy, ”Sensing of the Self, Society, and the Environment”, Proc. IEEE Sensors, 2020 (Accepted & Presented)

  • Invited presentation.

[7] A. Bhargava, “Predicting Interest Level based on EEG Scan Data using Machine Learning Algorithms”, AP Capstone Project, 2018

  • Presentation: View here
  • Global top-scoring AP Research project 2018.

Academic Reviews

  • 2023 – IEEE American Control Conference (ACC).
  • 2022 – MDPI Applied Sciences.