I received my BS degree in 2014, from the Sharif University of Technology in Tehran, Iran. I earned my MS and PhD degrees in mechanical engineering from the State University of New York at Buffalo, in 2017 and 2019, respectively. My research interests include the intersection of data science, computer vision and health informatics. I was an Adjunct Faculty Member and an Instructor at the State University of New York at Buffalo, and a Teaching Fellow with the Department of Mechanical and Aerospace Engineering, University at Buffalo. I am currently a Research Affiliate with Roswell Park, where I utilized computer vision methods for the first time to model automated assessment of surgical performance during robot-assisted surgery. I also developed the first artificial intelligence framework for an automated kidney cancer type diagnosis through CT scans. I've received awards and fellowships for academic and entrepreneurship achievements including the NSF Travel Award, Best Paper Award in Silent Hoist and Crane Company Materials Handling Graduate Paper Competition and the Student Entrepreneur Fellowship from the School of Management at the University at Buffalo.
1) Baghdadi, A., Hussein, A. A., Ahmed, Y., Cavuoto, L. A., & Guru, K. A. (2018). A computer vision technique for automated assessment of surgical performance using surgeons’ console-feed videos. International journal of computer assisted radiology and surgery.
2) Baghdadi, Amir & Hussein, Ahmed & Cavuoto, Lora & Guru, Khurshid. (2018). Use of Automation and Computer Vision in Differentiating Benign Renal Oncocytoma from Chromophobe Renal Cell Carcinoma: Proof of Concept, 19th Annual Meeting of the Society of Urologic Oncology, 10.13140/RG.2.2.23494.32324/1.
3) Baghdadi, A., Cavuoto, L., Hussein, A. A., Ahmed, Y., & Guru, K. (2018). PD58-04 Modeling Automated Assessment of Surgical Performance Utilizing Computer Vision: Proof of Concept. The Journal of Urology, 199(4), e1134-e1135.
4) Baghdadi, A., Megahed, F. M., Esfahani, E. T., & Cavuoto, L. A. (2018). A machine learning approach to detect changes in gait parameters following a fatiguing occupational task. Ergonomics, 61(8), 1116-1129.
5) Baghdadi, A., Cavuoto, L. A., & Crassidis, J. L. (2018). Hip and Trunk Kinematics Estimation in Gait Through Kalman Filter Using IMU Data at the Ankle. IEEE Sensors Journal, 18(10), 4253-4260.