Siamak Yousefi, PhD
Siamak Yousefi is an Assistant Professor at the Department of Ophthalmology and Department of Genetics, Genomics, and Informatics of the University of Tennessee Health Science Center (UTHSC) in Memphis. He is the director of the Data Mining and Machine Learning (DM2L) laboratory where he and his team develop state-of-the-art learning models to identify a wide range of eye conditions from ocular imaging data. Siamak received his PhD from the University of Texas in Dallas in 2012. He is an Electrical Engineer by degree and a Biomedical Engineer/Computer Scientist by research with vast vision and ophthalmology domain knowledge. He has completed postdoctoral trainings in vision research at the University of California Los Angeles (UCLA) and the University of California San Diego (UCSD). He was a visiting Assistant Professor at the Department of Information System and Technology and the Department of Ophthalmology of the University of Tokyo. He has published >100 peer-reviewed journal articles, conference papers, and abstracts, with over 50 in different applications of AI in vision and ophthalmology. He has published in Ophthalmology, JAMA Ophthalmology, American Journal of Ophthalmology, The Ocular Surface, PLOS One, IOVS, TVST, IEEE Transactions, KDD, and CVPR. He has been an invited guest speaker, moderator, or co-organizer of numerous Ophthalmology venues including The Glaucoma Foundation, Asia-Pacific Glaucoma Congress (APGC), International Society for Eye Research (ISER), and Iranian Society of Ophthalmology (IRSO). He is currently an Editorial Board Member of the TVST journal. He has been the recipient of several NIH/NEI and Bright Focus grant awards with total over $1.1M to develop AI models in vision and ophthalmology. He has also been invited to several NIH study sections and international funding agencies. His laboratory is among handful labs working on a wide range of data mining techniques applied on a broad spectrum of eye conditions. His lab is working on deep learning, manifold learning, conventional machine learning, unsupervised machine learning, and statistical approaches to address glaucoma, AMD, keratoconus, keratoplasty, and uveitis.