Kristy Sakano

Hi! 👋🏼 I’m a 3rd year PhD student in Computer Science at the University of Maryland, College Park, where I’m advised by Dr. Mumu Xu. I’m broadly interested in formal verification and model checking for autonomous cyber-physical systems that operate in messy, real-world environments. My work combines human feedback, Signal Temporal Logic (STL), and vision-language models (VLMs) to encode informal human rules as formal specifications and enforce them at runtime. The goal is to make autonomous robots safe and trustworthy in unstructured outdoor environments

This past summer (2025), I completed an internship at the National Consortium for the Study of Terrorism and Responses to Terrorism (START) where I co-authored a white paper examining ideologically motivated cybercrimes and their intersection with modern conflict, psychology, and state involvement. My technical contributions included analyzing cyber-attack patterns in the 21st century, modeling state involvement in hacktivist cells, assessing emerging cyber threats, and developing defensive strategies.

Before my PhD, I worked as an Operations Research Analyst and Autonomous Systems Scientist in the AI & Autonomy Applications Division at NAWCAD Patuxent River (2018-2024). I also served as a visiting scientist at NASA Goddard (2021) and the Naval Research Laboratory (2019). I earned my MS in Computer Science with a focus on Computational Perception & Robotics at Georgia Tech through their Online Master of Science in Computer Science (OMSCS) program in 2023, and a BS in Physics with Astrophysics concentration from the University of North Carolina at Chapel Hill in 2018.

During my undergraduate studies, I participated in a Smithsonian-funded summer research program at the Harvard-Smithsonian Center for Astrophysics in 2018. To date, my research on pulsating binary and trinary star systems is my most cited work

news

Oct 16, 2025	The Xu Research Group participated in the AI for Good Media Day at UMD College Park!
Oct 09, 2025	MarylandToday wrote an article about our research! Check it out here.
May 14, 2025	We presented our work, titled A Framework for Black-Box Controller Design to Automatically Satisfy Specifications Using Signal Temporal Logic, a ICUAS 2025 in Charlotte, NC!

selected publications

ICUAS

A Framework for Black-Box Controller Design to Automatically Satisfy Specifications Using Signal Temporal Logic

Kristy Sakano, Joe Mockler, Alexis Chen, and 1 more author

In 2025 International Conference on Unmanned Aircraft Systems (ICUAS), 2025

Abs DOI Slides

We present a framework for including humanreadable specifications in the design of black-box autonomous systems, or systems whose construct are prohibitively complex to analyze or intuit. By integrating parametric Signal Temporal Logic (pSTL), we can systematically evaluate and refine deep reinforcement learning policies to ensure compliance with predefined system specifications. Our approach is tested in a simulated autonomous driving environment, where we train a deep reinforcement learning agent in Mario Kart SNES using Proximal Policy Optimization. The agent is evaluated based on its ability to navigate a structured driving course while satisfying a set of a priori requirements; this evaluation is performed by writing and solving the parameters in a pSTL statement. This work contributes to the broader effort of bridging formal methods and data-driven learning, providing insights for researchers and operators alike in developing AI based controllers for real-world autonomous systems.