Alexandros Lioulemes

Graduate Studies Online
Adjunct Teaching Professor
Education
- Ph.D. in Medical Robotics, University of Texas at Arlington (CSE@UTA), August 2017
- Research Fellow in Human-Robot Interaction, National Center for Scientific Research "Demokritos"‚Äč (Roboskel Lab), 2014
- M.Sc. in Robotic Vision, University of Ioannina, Greece (CS@UOI), July 2013
- B.Sc. in Computer Vision, University of Ioannina, Greece (CS@UOI), September 2011

Alexandros Lioulemes serves as the Lead Principal Investigator (Lead PI) and holds the position of Senior Engineer specializing in Artificial Intelligence (AI), Robotics, and Computer Vision (CV) at Barrett Medical. In this role, he is actively engaged in pioneering research and development aimed at computational methods within the realm of medical robotics. These endeavors are focused on advancing capabilities that empower individuals to achieve more in their pursuits. Over the course of his professional career, Alexandros Lioulemes has taken the helm in driving the creation of innovative medical products and robotic solutions. These solutions play a crucial role in assisting patients in harnessing their strengths, while also furnishing therapists with an expanded set of tools for medical assessments. Concurrently, he dedicates his efforts to the formulation and instruction of hands-on graduate-level courses offered through WPI's Graduate Studies Online. This multifaceted approach enables him to share his enthusiasm for mathematics, physics, and computing with the next generation of engineers and potential scientists.

Within the realm of robotics, the capacity to comprehend and construe the surrounding environment stands as a pivotal element driving autonomous decision-making and intelligent conduct. In his role as an educator responsible for guiding the Foundation of Robotics and Computer Vision courses, his pedagogical approach revolves around furnishing students with a robust grounding in both the theoretical underpinnings and pragmatic facets of computer vision, coupled with its practical deployment in the field of robotics. While theoretical understanding remains paramount, he firmly upholds the principle of affording students extensive opportunities for firsthand, experiential learning. He integrates laboratory sessions, coding assignments, and project undertakings as integral components of the learning process. These activities collectively empower students to translate theoretical insights into tangible, real-world contexts. The acquisition of hands-on expertise not only augments technical adeptness of the students but also fosters a sense of assurance in the application of their proficiencies to authentic challenges within robotic perception applications.

Teaching:
- Foundations of Robotics (RBE 500)
- Computer Vision (RBE/CS 549)