Alex Vakanski, Ph.D., P.E.

• Machine learning and artificial intelligence
• Computer vision and image processing
• Robotics, learning from demonstration, vision-based control
• Biomedical informatics

Alex Vakanski is an Associate Professor in Industrial Technology and Technology Management with the Department of Nuclear Engineering and Industrial Management (NEIM), and he is an Affiliate Faculty with the Department of Computer Science (CS). He teaches courses in the areas of robotics, manufacturing, CAD design, and quality control in the NEIM Department, and adversarial machine learning and data science in the CS Department. The scope of his research interests resides at the intersection of machine learning and mechatronics, with applications in biomedical informatics, computer vision and image processing, and observational robotic learning.

• Robust Approaches for Breast Tumor Segmentation, NIH – COBRE
• Modeling and evaluation of physical therapy movements using machine learning, Pilot Grant – CMCI
• Development and Commercialization of a Visual Learning System for Robot Programming by Demonstration, I2I – NSERC

• A. Vakanski, M. Xian, and P. Freer, "Attention enriched deep learning model for breast tumor segmentation in ultrasound images," Ultrasound in Medicine and Biology, vol. 46, no. 10, pp. 2819–2833, 2020.
• Y. Liao, A. Vakanski, and M. Xian, "A deep learning framework for assessing physical rehabilitation exercises," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 2, pp. 468–477, 2020.
• C. Williams, A. Vakanski, S. Lee, and D. Paul, "Assessment of physical rehabilitation movements through dimensionality reduction and statistical modeling," Medical Engineering & Physics, vol. 74, pp. 13–22, 2019.
• A. Vakanski, H-p. Jun, D. Paul, and R. Baker, "A data set of human body movements for physical rehabilitation exercises," Data, vol. 3, no. 2, pp. 1–15, 2018.
• A. Vakanski, and F. Janabi-Sharifi, Robot Learning by Visual Observation, John Wiley & Sons, ISBN-10: 1119091802, ISBN-13: 978-1119091806, 2017.
• A. Vakanski, J. M. Ferguson, and S. Lee, "Mathematical modeling and evaluation of human motions in physical therapy using mixture density neural networks," Journal of Physiotherapy and Physical Rehabilitation, vol. 1, no. 4, pp. 1–10, 2016.
• A. Vakanski, I. Mantegh, A. Irish, and F. Janabi-Sharifi, "Trajectory learning for robot programming by demonstration using Hidden Markov Model and Dynamic Time Warping," IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 42, no. 4, pp. 1039–1052, 2012.
• E. Nematollahi, A. Vakanski, and F. Janabi-Sharifi, "A second-order conic optimization-based method for visual servoing," Journal of Mechatronics, vol. 22, no. 4, pp. 444–467, 2012.

• ASNT Faculty Grant Award, awarded by the American Society for Nondestructive Testing, 2015