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

• Robotics, learning from demonstration, vision-based control
• Machine learning and artificial intelligence
• Image processing and computer vision

Alex Vakanski is an assistant professor in Industrial Technology. At the University of Idaho in Idaho Falls, he teaches courses in the areas of manufacturing, robotics, CAD design and quality control. His research efforts are focused on observational robotic learning from demonstrations, biomedical image processing, and modeling human movements.

• 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