Journal papers

• T. H. Nguyen, C. Akdis, A. Baccarelli, S. Banerjee, R. T. Bhowmik, et al., “Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures,” Allergy, 2026, pp. 1–16. (Link)
• C. Ding, R. T. Bhowmik, “Artificial Intelligence in Multimedia Content Generation: A Review of Audio and Video Synthesis Techniques,” Journal of the Society for Information Display, 34, 2025, pp. 49–67, Invited Paper. (Link)
• R. T. Bhowmik, Y. S. Jung, J. Aguilera, M. Prunicki, K. Nadeau, “A Multi-Modal Wildfire Prediction and Personalized Early-Warning System Based on a Novel Machine Learning Framework,” Journal of Environmental Management, 341, 117908, 2023. (Link)
• R. T. Bhowmik, C. Kandathil, S. P. Most, “Automating the Standardized Cosmesis and Health Nasal Outcomes Survey (SCHNOS) Classification with Convolutional Neural Networks,” Facial Plastic Surgery & Aesthetic Medicine, PMID: 36749153, 2023. (Link)
• R. T. Bhowmik, S. P. Most, “A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks,” Electronics, 11 (16), 2562, 2022. (Link)
• R. Parthasarathy, R. T. Bhowmik, “Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing,” IEEE Access, 9, 103337, 2021. (Link)
• R. T. Bhowmik, Y. S. Jung, J. Aguilera, M. Prunicki, K. Nadeau, “California Wildfire Inventory (CAWFI): An Extensive Dataset for Predictive Techniques based on Artificial Intelligence,” Fire (under review).

Preprints

• R. T. Bhowmik, Y. S. Jung, J. Aguilera, M. Prunicki, K. Nadeau, “California Wildfire Inventory (CAWFI): An Extensive Dataset for Predictive Techniques based on Artificial Intelligence,” arXiv:2509.11015, 2025. (Link)
• R. T. Bhowmik, “A Multi-Modal Wildfire Prediction and Personalized Early-Warning System Based on a Novel Machine Learning Framework,” arXiv:2208.09079, 2022. (Link)
• R. T. Bhowmik, “A Multi-Modal Respiratory Disease Exacerbation Prediction Technique Based on a Spatio-Temporal Machine Learning Architecture,” arXiv:2103.03086, 2021. (Link)
• R. Parthasarathy, R. T. Bhowmik, “Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing,” arXiv:2012.10812, 2020. (Link)

Conferences

• R. T. Bhowmik*, S. Sun*, E. McElhinney, V. Alexopoulos, S. Hickmann, F. Hoffmann, K. Song, R. Sato, L. Skaling, A. Krinos, C. Ogaki, O. Khatib, “3D Reconstruction of Underwater Features in Lake Tahoe with an Autonomous Underwater Vehicle,” International Conference on Computer Vision, 2025. (Link) (*: equal contribution)
• R. T. Bhowmik, “A Multi-Modal Wildfire Prediction and Personalized Early-Warning System Based on a Novel Machine Learning Framework,” Stanford BigEarth Hackathon, 2022.