|Group||Multemedia Systems and Complex Networks|
Siamak Yousefi is 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 broad 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, numerous IEEE Transactions and conferences, 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), Iranian Society of Ophthalmology (IRSO), and Indian Computer Vision, Graphics, and Image Processing (ICVGIP) conference. He is currently an Editorial Board Member of the TVST journal. He has been the recipient of two NIH/NEI and one Bright Focus grant awards total > $1.1M to develop AI models in vision and ophthalmology. He has also been invited to several NIH study sections and international grant review meetings. He has taught advanced data mining, applied data mining, advanced mathematics, and statistics in undergraduate/graduate levels. His lab is working on deep learning, manifold learning, conventional machine learning, unsupervised machine learning, and statistical learning to address Glaucoma, AMD, Keratoconus, Keratoplasty, and Uveitis. His lab is always seeking talented and determined researchers in: Engineering/computer science: Machine learning, deep learning, and biostatistics. Medical: MD students, ophthalmology, glaucoma, retina, and cornea research fellows.