Hajar Homayouni

Bio

Dr. Homayouni is an Assistant Professor at the Department of Computer Science at SDSU. Her research focuses on estimating and addressing existing issues with imbalanced and faulty data used for critical decision-making. Her lab includes a diverse group of undergraduate and graduate students working on multiple interdisciplinary projects to advance the quality of data used for critical scientific analysis, enhance interpretability of complex machine learning models, and understand the process behind decisions made by the models. These projects include but are not limited to anomaly detection, synthetic balanced data generation, and analysis of significant factors in impactful research. She submitted an NSF CAREER proposal on “Privacy-preserving Multimodal Conditional Generative Adversarial Network for Synthetic Data Generation”, and is the Co-PI for an under-review NSF proposal for a project on “Economical Solid-State Synthesis with AI”. During the past five years, Dr. Homayouni has published one book chapter, one journal paper, and eight peer-reviewed conference papers. Her most recent publications include “High-Resolution COVID-19 X-Ray Generation”, in the ACM Health Informatics and Knowledge Management Conference, 2023 and “Detecting Temporal Dependencies in Data”, in the British International Conference on Databases, 2022. She has received multiple awards including CAHSI LREU and STARS Computing Corps fellowships to support diverse cohorts of students, the Western Association of Graduate Schools (WAGS)/ProQuest Distinguished Master’s Thesis Award, 1st place at the Graduate ACM SRC in TAPIA, the Great Minds in Research, Computer Science Grad, and P. R. Mukherjee fellowships at Colorado State University. Her doctoral work was the basis of a grant she authored as the PI, which was funded by the Google Cloud Credit to Support COVID-19 Research (2020). She has been Program chair, associate editor, and committee in several academic journals and conferences. She is currently teaching introductory and advanced level data science courses.