I am a computer scientist, educator, and research leader working at the intersection of intelligent motion, machine learning, and complex systems.
Professor · Director of the Tapia Lab · Former Chair of Computer Science
My work develops computationally efficient approaches for planning and decision making in high-dimensional or uncertain systems. I also teach artificial intelligence and reinforcement learning, mentor researchers across career stages, and contribute to national computing organizations.
Detailed projects, publications by research area, software, models, lab members, and outreach activities live on the Tapia Lab website. This page serves as my primary UNM faculty profile and a record of selected professional milestones.
At the 2018 Grace Hopper Celebration, I spoke with Hai Ah Nam about sustaining research, work, and life through graduate school and beyond. The Grace Hopper community also recognized my research and broadening-participation work with the 2015 Denice Denton Emerging Leader ABIE Award.
Elected to ACM Council as a Member-at-Large for 2026–2030 and to the Computing Research Association Board through 2029.
As department chair, I led six-year ABET reaccreditation, completed approved faculty growth across key computing areas, strengthened student advising and support, and advanced major national searches and external engagement.
I have mentored researchers from high school through postdoctoral training, with former advisees continuing into faculty, research, and doctoral positions. I extend that commitment nationally developing programs that support students, new faculty, and department chairs across the computing community.
My courses connect foundational ideas to systems students can implement, evaluate, and explain. Research advising extends that process from forming a question through communicating a result.
Search, knowledge representation, planning, probabilistic reasoning, and learning, with project-based evaluation of intelligent systems.
Supervised and unsupervised learning, neural networks, optimization, generalization, and rigorous empirical evaluation of learned models.
Markov decision processes, value-based and policy-gradient methods, actor–critic algorithms, exploration, and reproducible experimentation.
The Tapia Lab site contains the detailed research areas, projects, publications, people, code, models, workshops, and outreach. My work there spans motion planning and robot learning, human–AI teaming, safe and adaptive autonomy, and computational biology.
The undergraduate thesis examined the role of universities in expanding participation by women in engineering and computer science.
Co-authored an early framework using learned problem characterization to match regions of configuration space with suitable roadmap-based planners.
Completed a Ph.D. at Texas A&M, followed by a Computing Innovation Postdoctoral Fellowship at UT Austin studying protein folding.
Built an independent research and mentoring program spanning robotics, artificial intelligence, and computational biology.
The work combined learned local navigation policies with sampling-based roadmaps for long-range tasks. The year also included service as a Google Faculty in Residence and WAFR co-chair.
Led accreditation, faculty growth, student-support improvements, academic planning, national searches, and external engagement.
Contributed to national computing-research governance and to the consensus study setting a research agenda for machine learning in safety-critical applications.
Recognition spanning technical contributions, research leadership, mentorship, and work to broaden participation in computing and robotics.
New Mexico Technology Council
University of New Mexico
Texas A&M Computer Science and Engineering
IEEE International Conference on Robotics and Automation
Computing Research Association
National Science Foundation
Anita Borg Institute · Grace Hopper Celebration
Texas A&M University
Farris Engineering Center
Room 3400 B
Department of Computer Science
University of New Mexico
