Our AI research program delves into the intersection of methods, applications, and ethics of AI. Researchers develop novel methods to enhance capabilities, transparency, and interpretability of AI models. Simultaneously, the program applies these techniques to real-world challenges, including usability of computer systems by people with disabilities and to science problems, ensuring that AI technology benefits society responsibly and justly.
Academics and Coursework
Students may specialize their education around Artificial Intelligence/Machine Learning topics via these undergraduate and graduate level courses, as well as in a Graduate Certificate in Ethical Artificial Intelligence
Undergraduate Courses
| CSC 308 | Introduction to Machine Learning for Interdisciplinary Data Scientists |
| CSC 508 | Machine Learning and Data Science for Personalized Medicine |
| CSC 509 | Data Science and Machine Learning for Medical Image Analysis |
| CSC 601 | Data Science and Machine Learning for Biotechnology Seminar Series |
| CSC 602 | Interview Preparation for Data Science and Machine Learning for Biotechnology Opportunities |
| CSC 621 | Natural Language Technologies |
| CSC 665 | Biomedical Imaging and Analysis |
| CSC 671 | Deep Learning |
| CSC 676 | Soft Computing and Decision Support Systems |
Graduate Courses
| CSC 820 | Natural Language Technologies |
| CSC 821 | Biomedical Imaging and Analysis |
| CSC 859 | AI Explainability and Ethics |
| CSC 865 | Artificial Intelligence |
| CSC 871 | Deep Learning |
| CSC 872 | Pattern Analysis and Machine Intelligence |
| CSC 874 | Topics in Big Data Analysis |
| CSC 876 | Soft Computing and Decision Support Systems |
| CSC 878 | Big Data Platforms and Systems |
| CSC 890 | Graduate Seminar - Advanced Artificial Intelligence |
Faculty and Focus Areas
Publications and Reports
Bethel, EW, Cramer, V., del Rio, A., Narins, L., Pestano, C., Verma, S., Arias, E., Bertelli, N., Perciano, T., Shiraiwa, S., Sanchez-Villar, A., S., Wallace, G., & Wright, JC (2024). Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science. Lawrence Berkeley National Laboratory, LBNL-2001609. https://arxiv.org/abs/2409.06122
Sanchez-Villar, A., Bai, Z., Bertelli, N., Bethel, EW, Hillairet, J., Perciano, T., Shiraiwa, S., Wallace, GM, & Wright, J. (2024). Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches. Nuclear Fusion. https://doi.org/10.1088/1741-4326/ad645d
L. D. Narins, A. Scott, A. Gautam, A. Kulkarni, M. Castanon, B. Kao, S. Ihorn, Y. T. Siu, J. M. Mason, A. M. Blum, I. Yoon. Validated Image description Rating Dataset” NeurlIPS, Neural Information Processing Systems Datasets and Benchmarks Track, 2023. [Online]. Available: https://neurips.cc/virtual/2023/poster/73420
A. Stangl, S. Ihorn, Y. T. Siu, A. Bodi, M. Castanon, L. Narines, I. Yoon, “The potential of a visual dialogue agent in a tandem automated audio description system for videos,” in ASSETS, New York, NY, 2023. [Online]. Available: https://assets23.sigaccess.org/accepted-papers.html
Scott, A., Narins, L., Kulkarni, A., Castanon, J., Kao, B., Ihorn, S., Siu, Y. & Yoon, I. (2023). Improved Image Caption Rating - Datasets, Game, and Model. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA. https://doi.org/10.1145/3544549.3585632
C. Pitcher-Cooper, M. Seth, B. Kao, J. M. Coughlan, and I. Yoon, “You Described, We Archived: A Rich Audio Description Dataset,” J. Technol. Pers. Disabil., vol. 11, 2023, [Online]. Available: https://www.zotero.org/google-docs/?IF7cWk
Á. Sánchez-Villar, Z. Bai, N. Bertelli, E. W. Bethel, J. Hillairet, T. Perciano, S. Shiraiwa, G. Wallace, and J. C. Wright. Methodology for surrogate modeling implementation: application to the ICRF wave absorption forward problem. In 65th Annual Meeting of the APS Division of Plasma Physics, Denver, CO, USA, November 2023. Abstract: BO05.00015.
Chong Teng, Daniel Huang, Elizabeth Donahue, and Junwei Lucas Bao. Exploring Torsional Conformer Space with Physical Prior Mean Function-Driven Meta-Gaussian Processes. Journal of Chemical Physics, 2023.
Chong Teng, Daniel Huang, and Junwei Lucas Bao. A Spur to Molecular Geometry Optimization: Gradient-Enhanced Universal Kriging with On-the-Fly Adaptive Ab Initio Prior Mean Functions in Curvilinear Coordinates. Journal of Chemical Physics, Emerging Investigators Special Collection, 2023.
Hui-Ming Deanna Wang，Yinxing Li，and Hui Yang, “Content Diffusion on Social Media:The Roles of Emerging Ties and Users”. In Proceedings of the 13th International Conference on Information Integration and Innovation (ICIII 2022), Taiwan. December 2022.
G.M. Wallace, Z. Bai, R. Sadre, T. Perciano, N. Bertelli, S. Shiraiwa, E.W. Bethel, and J.C. Wright. Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive. Journal of Plasma Physics, 88(4):895880401, August 2022. https://www.doi.org/10.1017/S0022377822000708.
Shuai Zhang, Robbie Sadre, Benjamin A. Legg, Harley Pyles, Talita Perciano, E. Wes Bethel, David Baker, Oliver Rübel, and James J. De Yoreo. Rotational dynamics and transition mechanisms of surface-adsorbed proteins. Proceedings of the National Academy of Sciences, 119(16):e2020242119, April 2022. https://www.pnas.org/doi/abs/10.1073/pnas.2020242119.
Chong Teng, Yang Wang, Daniel Huang, Katherine Martin, Jean-Baptiste Tristan, and Junwei Lucas Bao. Dual-Level Training of Gaussian Processes with Physically Inspired Priors for Geometry Optimizations. Journal of Chemical Theory and Computation, 2022.
Daniel Huang, Junwei Lucas Bao, and Jean-Baptiste Tristan. Geometry Meta-Optimization. Journal of Chemical Physics, 2022.
Daniel Huang, Chong Teng, Junwei Lucas Bao, and Jean-Baptiste Tristan. mad-GP: Automatic Differentiation of Gaussian Processes for Molecules and Materials. Journal of Mathematical Chemistry, 2022.
John Wright, Greg Wallace, E. Wes Bethel, Zhe Bai, Talita Perciano, Robbie Sadre, Nicola Bertelli, and Syun’ichi Shiraiwa. Overview and status of the FES Scientific Machine Learning Project "Accelerating Radio Frequency Modeling Using Machine Learning". In Proceedings of the 63rd Annual Meeting of the APS Division of Plasma Physics, volume 66, Pittsburgh, PA, USA, November 2021. TM10.00002s
Hui Yang, Thomas W. Olson*, and Arno Puder, Analyzing Computer Science Students' Performance Data to Identify Impactful Curricular Changes. In the proceedings of IEEE Frontiers in Education 2021, Lincoln, Nebraska, USA, October 2021.
Hui Yang, Apurva Pimparkar*,Celia Graterol, Rama Ali Kased, and Mary Beth Love, Analyzing college students’ interaction records to improve retention and graduation outcome. In the proceedings of IEEE Frontiers in Education 2021, Lincoln, Nebraska, USA, October 2021.
Aditya Bodi, Pooya Fazli, Shasta Ihorn, Yue-Ting Siu, Andrew Scott, Lothar Narins, Yash Kant, Abhishek Das, and Ilmi Yoon, “Automated Video Description for Blind and Low Vision Users”. In Proceedings of the ACM SIGCHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI), 2021.
Govindu S, Guttula R, Swati Kohli, Poonam Patil, Kulkarni A, Yoon I. “Towards Intelligent Reading through Multimodal and Contextualized Word LookUp”. ICMLA. 2021.
Wong, M., Previde, P., Cole, J., Thomas, B., Laxmeshwar, N., Mallory, E., Lever, J., Petkovic, D., Altman, R.B. and Kulkarni, A., 2021. Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive. Journal of biomedical informatics, 117, p.103732. https://doi.org/10.1016/j.jbi.2021.103732
Govindu, S., Guttula, R. V., Kohli, S., Patil, P., Kulkarni, A., & Yoon, I. (2021). Towards Intelligent Reading through Multimodal and Contextualized Word LookUp. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1249-1252). IEEE.
Wong, M., Ghahghaei, S., Chandna, A. and Kulkarni, A. (2021) August. Scalable non-invasive pediatric cerebral visual impairment screening with the higher visual function question inventory (HVFQI). In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 1-1). (Poster presentation).
Wong, M., Laxmeshwar, N., Joshi, R. and Kulkarni, A. (2021) August. Browsing weighted interactome models using GeneDive. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 1-1). (Poster presentation).
T. Touati, K. Okada, I. Song, What makes a person obese?: An individual-level analysis of obesity, Accepted as 1-page extended abstract at IEEE International Conference on Biomedical and Health Informatics, (2021)
Grants and Awards
US Department of Energy, Office of Fusion Energy Sciences. Automated ML Model Development and Testing for Fusion Tokamak Devices Subaward from Massachusetts Institute of Technology. Nov. 2023. Sep. 2025. (PI: W. Bethel)
U. S. Department of Energy, Office of Fusion Energy Sciences. Accelerating Radio Frequency Modeling Using Machine Learning. Subaward from Lawrence Berkeley National Laboratory. Oct. 2022 – Sep. 2024. (PI: W. Bethel)
Google Research, Democratizing Artificial Intelligence: Fairness, Accountability, Transparency, and Ethics. August 2021. (PIs: P. Fazli, A. Gautam, D. Huang, and H. Yang)
Foundation for California Community Colleges, Enhancing Program Pathways Mapper with Large Language Models to Further Assist Transfer Students. January 2023–December 2023. (PIs: H. Yang and A. Puder)
Genentech Inc. Grant Renewal Award. Expansion of the gSTAR Professional Certificate Program “Data Science and Machine Learning for Biotechnology Professionals.” August 2023 – July 2026. (PI: A. Kulkarni, Co-PIs: I. Yoon, P. Pennings, and S. Ihorn)
Genentech Foundation Grant Renewal. Gen-PINC Scholarships Program. June 2024 – May 2029. (PI: A. Kulkarni, Co-PIs: I. Yoon, P. Pennings, and S. Ihorn)
Smith Kettlewell Eye Research Institute. Computational Solutions for the Study of Higher Visual Function Deficits. Sept 2022 – Aug 2027. (PI: A. Kulkarni)
Genentech Foundation Grant Award. Gen-PINC Scholarships Program. September 2020 – May 2024. (PI: A. Kulkarni, Co-PIs: I. Yoon, P. Pennings, and S. Ihorn)
Genentech Inc. Grant Award. gSTAR: New Certificate Program “Data Science and Machine Learning for Biotechnology.” August 2020 – July 2023. (PI: A. Kulkarni, Co-PIs: I. Yoon, P. Pennings, and S. Ihorn)
NSF S-STEM (Award # 2030581). Scholarships To Improve Undergraduate Students' Academic Achievement, Retention, and Career Success in Computer Science and Artificial Intelligence (AI-STAARS Program). March 2021 – February 2025. (PI: A. Kulkarni, Co-PIs: I. Yoon, P. and S. Ihorn)