Publications
- S. Komijani, S. Quynn, S. McClure, J. Schweitzer, and D. Ghosal (2025). Graph-Based Diffusion Learning of fMRI Data for Trait Impulsivity Prediction. Under preparation.
- S. Komijani, D. Ghosal, J. Geng, and J. B. Schweitzer (2025). Eye-Tracking Insights: Investigating the Link Between Performance and Distractibility in Virtual Reality. Under preparation.
- S. Komijani, S. Quynn, S. McClure, P. Mukherjee, J. B. Schweitzer, and D. Ghosal (2025). From Regions to Voxels: Benchmarking fMRI-Based Machine Learning Models. NeuroImage, under review.
- S. Quynn, S. Komijani, S. McClure, J. B. Schweitzer, and D. Ghosal (2025). Modeling Neural Activations of Impulsivity with Transformers Using fMRI Data. ICLR-2026, under review.
- S. Komijani, D. Ghosal, M. K. Singh, J. B. Schweitzer, and P. Mukherjee (2025). A Novel Framework to Predict ADHD Symptoms using Irritability in Adolescents and Young Adults with and without ADHD. Frontiers in Psychiatry, 15, Article 1467486. https://doi.org/10.3389/fpsyt.2024.146748
- M. E. Kounavis, D. Durham, S. Deutsch, and S. Komijani (2018). Implicit Data Integrity: Protecting User Data without MACs. In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications (ICETE 2018) – Volume 2: SECRYPT (pp. 543–552). SciTePress. https://doi.org/10.5220/0006905105430552
- M. E. Kounavis, D. Durham, S. Deutsch, S. Komijani, A. Papadimitriou, and K. Grewal (2018). There is no need to waste communication bandwidth on MACs. 2018 Global Information Infrastructure and Networking Symposium (GIIS) (pp. 1–7). IEEE. https://doi.org/10.1109/GIIS.2018.8635779
- M. E. Kounavis, D. Durham, S. Deutsch, and S. Komijani (2017). Non-recursive Computation of the Probability of More Than Two People Having the Same Birthday. IEEE International Symposium on Computers and Communications (ISCC 2017), 1263–1270. https://doi.org/10.1109/ISCC.2017.8024698
Poster Presentations
- “Modeling Neural Activations of Impulsivity with Transformers Using fMRI Data”, Berkeley Neuroscience, 2025
- “A Machine Learning Approach to Studying Impulsivity and Neural Basis of Reward Processing in Attention Deficit/Hyperactive Disorder”, Flux, 2025
- “VRAM: Investigating Attention in Children with ADHD using VR”, Meaningful eXtended Reality (MXR), UC Davis, 2025
- “Irritability and Neural Basis of Reward Processing in ADHD”, Cognitive Neuroscience Society (CNS), 2024
- “Analysis of Irritability using Delgado-Based Brain Imaging Data: A Machine Learning Approach”, Neuro Engineering and Medicine Symposium (NEM), UC Davis, 2023
- “A Machine Learning Approach to Predict Hyperactive/Impulsive Symptoms using Irritability”, American Professional Society of ADHD and Related Disorders (APSARD), 2023
- “A Machine Learning Approach to Predict Hyperactive/Impulsive Symptoms using Irritability”, Society for Neuroscience (SFN), 2022