Hassan S. Salehi
Hassan S. Salehi, Ph.D. is an Associate Professor in the Department of Electrical and Computer Engineering at California State University, Chico. He earned his Ph.D. degree in Electrical and Electronics Engineering from the University of Connecticut in 2015, with funding from the National Institutes of Health (NIH). During Summer 2023, Dr. Salehi was with Harvard University as a Visiting Professor, conducting research on optical imaging and artificial intelligence/deep learning.
Dr. Salehi is the recipient of several teaching and research awards, including the International Outstanding Professor of the Year Award from CSU Chico (2022), the AAOMR Early-stage Faculty Innovative Research Award (2018), the UHart/CETA Professor of the Year Award (2017), and UConn Predoctoral Fellowship Awards (2014, 2015). In 2021, Dr. Salehi was recognized as an "SPIE Community Champion" for his significant contributions to the scientific community as an author, researcher, and reviewer. Dr. Salehi has served as a journal editor and reviewer, and he is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the Society of Photo-Optical Instrumentation Engineers (SPIE).
His research primarily focuses on artificial intelligence (AI), including machine learning and deep learning models, signal and image processing, and the development of novel optical imaging and sensing systems. His work spans various applications in bioimaging, healthcare, robotics, physical sciences, and industrial inspection, showcasing a multidisciplinary approach to advancing technology and innovation. Dr. Salehi's research has been funded by the National Science Foundation (NSF), CSU ARI, AAOMR grants and industry. He has published over 35 peer-reviewed journal and conference articles in the field, and has given talks at numerous international conferences. One of his research papers was featured on the cover of the Journal of Oral Radiology. In his research projects, he is closely collaborating with his colleagues at different universities.
In addition to his research program, Dr. Salehi has substantial experience in curriculum and program design at CSU Chico, serving for three consecutive years on the University Curriculum Committee, including one year as Chair of the Electrical/Electronics Engineering Curriculum Committee and two years as Chair of the Department Graduate Education Committee. He led major graduate-program initiatives as the lead proposer and primary architect of the MS in Artificial Intelligence Engineering (MS AI Eng) program, developing the program concept and authoring the degree projection and full program proposal for campus and system review; the program was approved by the CSU Chancellor’s Office and the California State University Board of Trustees in Spring 2026 with implementation in Fall 2026.
He also played a key leadership role in the successful reopening of the MS in Electrical and Computer Engineering (MS ECE) program, including designing and developing essential new graduate coursework such as EECE 664 (Machine Learning for Engineers; co-designed) and EECE 665 (Deep Learning; sole author and designer), which he launched as the first deep learning course at CSU Chico. In parallel, he led the proposal for the Blended BS+MS ECE programs, and developed the associated graduate application processes across Cal State Apply and university platforms. Following program reactivation, he served as the first Graduate Coordinator, leading graduate admissions and program operations, and he has continued to modernize the graduate curriculum by redesigning EECE 682 to integrate artificial intelligence into digital control systems course. He also co-created and co-developed an interdisciplinary Minor in Biomedical Engineering within the EECE Department, contributing to program structure, course sequencing, and integration.
Research Lab Website: Link
SPIE Profile: https://spie.org/profile/hsalehi
Selected Publications:
[1] C. Diaz and H. S. Salehi, “GUI-Based Deep Learning System for Real-Time Early Dental Caries Classification and Detection via OCT Images,” IEEE International Conference on Machine Learning and Applications (ICMLA), Boca Raton, FL, USA, in press, (2025).
[2] H. S. Salehi, M. Barchini, K. Braiser, and H. Zakeri, “Development of GUI-based deep learning and image processing system for legume nodule segmentation and classification,” Proc. SPIE DCS 12527, Pattern Recognition and Tracking XXXIV, 1252703 (2023).
[3] Ş.B. Duman, A.Z. Syed, D. Celik Ozen, İ.Ş. Bayrakdar, H. S. Salehi, A. Abdelkarim, Ö. Celik, G. Eser, O. Altun, K. Orhan, “Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images,” Diagnostics 12(9), 2244 (2022).
[4] H. S. Salehi, A. Granados, and M. Mahdian, “Comparison of deep convolutional neural network models with OCT images for dental caries classification,” Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1203613 (2022).
[5] Q. Chen, J. Huanga, H. S. Salehi, H. Zhu, L. Lian, X. Lai, and K. Wei, “Hierarchical CNN-based occlusal surface morphology analysis for classifying posterior tooth type using augmented images from 3D dental surface models,” Elsevier Journal of Computer Methods and Programs in Biomedicine 208, 106295 (2021).
[6] H. S. Salehi, M. Barchini, Q. Chen, and M. Mahdian, “Toward development of automated grading system for carious lesions classification using deep learning and OCT imaging,” Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1160014 (2021).
[7] Q. Chen, X. Jin, H. Zhu, H. S. Salehi, and K. Wei, “3D Distribution of dental plaque on occlusal surface using 2D-fluorescence-image to 3D-surface registration,” Elsevier Journal of Computers in Biology and Medicine 123, 103860 (2020).
[8] M. M. Murshid and H. S. Salehi, “Electromyography signal analysis with real-time support vector machine,” Proc. SPIE DCS 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, 1142313 (2020).
[9] H. S. Salehi, M. Barchini, and M. Mahdian, “Optimization methods for deep neural networks classifying OCT images to detect dental caries,” Proc. SPIE BIOS 11217, Lasers in Dentistry XXVI, 112170G (2020).
[10] Q. Chen, X. Jin, H. Zhu, and H. S. Salehi, “Classification of pit and fissure for caries risk based on 3D surface morphology analysis of tooth,” Proc. SPIE BIOS 11217, Lasers in Dentistry XXVI, 112170E (2020).
[11] R. J. Day, H. S. Salehi, and M. Javadi, “IoT Environmental Analyzer using Sensors and Machine Learning for Migraine Occurrence Prevention,” IEEE International Conference on Machine Learning and Applications (ICMLA), Boca Raton, FL, USA, pp. 1460-1465, (2019).
[12] H. S. Salehi, H. Li, A. Merkulov, P. D. Kumavor, H. Vavadi, M. Sanders, A. Kueck, M. A. Brewer, and Q. Zhu, “Co-registered photoacoustic and ultrasound imaging and classification of ovarian cancer: ex vivo and in vivo studies,” Journal of Biomed. Optics 21(4), 046006 (2016).
[13] H. S. Salehi, P. D. Kumavor, U. Alqasemi, H. Li, T. Wang, C. Xu, and Q. Zhu, “Design of optimal light delivery system for co-registered transvaginal ultrasound and photoacoustic imaging of ovarian tissue,” Elsevier Photoacoustics Journal 3(3), 114-122 (2015).
[14] H. S. Salehi, T. Wang, P. D. Kumavor, H. Li, and Q. Zhu, “Design of miniaturized illumination for transvaginal co-registered photoacoustic and ultrasound imaging,” Journal of Bio. Optics Express 5(9), 3074-3079 (2014).
[15] T. Wang, S. Nandy, H. S. Salehi, P. D. Kumavor, and Q. Zhu, “A low-cost photoacoustic microscopy system with a laser diode excitation,” Journal of Bio. Optics Express 5(9), 3053-3058 (2014).