معرفی
Lydia Tapia is a Professor and the Department Chair of the Department of Computer Science at the University of New Mexico, School of Engineering. She is a leading researcher in motion planning, robotics, and computational biology, with a strong commitment to diversity and leadership in computing.
Education:
- PhD in Computer Science, Texas A&M University, 2009
- BS in Computer Science, Tulane University, 1998
Her research focuses on the development of efficient algorithms for high-dimensional motion simulation in both robotics and molecular systems. She applies adaptive learning techniques to solve computationally intensive problems in robotic navigation, molecular docking, and antibody aggregation. Her work bridges robotics and computational biology, with applications in immunology, neurodegenerative diseases, and human-automation systems.
Her recent publications highlight trends in reinforcement learning for motion planning, multi-agent navigation, human-robot collaboration, and the use of deep learning for geometric prediction. These works frequently appear in top venues such as IROS, ICRA, IEEE RA-L, and MIG, reflecting her interdisciplinary impact.
Scientific Awards:
- 2016 Denice Denton Emerging Leader ABIE Award
- 2016 NSF CAREER Award
- 2017 CRA-W Borg Early Career Award
She actively advises students and postdocs, leads the Tapia Lab, and contributes to major research centers including the Adaptive Motion Planning Research Group, the Center for Evolutionary & Theoretical Immunology, and the New Mexico Center for the Spatiotemporal Modeling of Cell Signaling. She has secured significant research funding and has been instrumental in organizing workshops and initiatives to promote diversity in robotics and computing.
Her lab, the Tapia Lab, focuses on complex robotic systems, including molecular robots like proteins, using adaptive learning to solve planning problems. Current projects include human-automation collaboration, resilient learning, robotic task learning, and molecular docking games that leverage human intuition through crowdsourcing.
