Research
At the Parasol Lab, we study algorithms, systems, and computational frameworks for solving complex problems across robotics, high-performance computing, computational science, and interdisciplinary applications. Our work spans motion planning, multi-agent coordination, parallel computing, computational biology, and emerging efforts such as Mind in Vitro, with an emphasis on scalable algorithmic techniques that can drive both foundational advances and real-world impact. This research naturally falls into three connected areas: developing new algorithmic foundations, building systems and computational tools, and applying them to challenging problems in robotics, science, and human-centered domains.
We develop algorithmic foundations for robotics and apply them to real-world problems across engineering and interdisciplinary domains
We build scalable computational systems and tools that support high-performance computing and large-scale applications
We apply our methods to challenging problems in robotics, computational biology, Mind in Vitro, and other human-centered scientific domains
Together, these research directions connect fundamental algorithm design with practical systems, scientific discovery, and human-centered applications. By combining theoretical advances with scalable computational methods and real-world impact, we aim to develop technologies that are more capable, efficient, and broadly useful. Our work spans robotics, parallel computing, computational science, and interdisciplinary collaborations that bring algorithmic innovation into new domains.
Applied Algorithms
We apply our algorithms to real-world robotic systems—including autonomous vehicles, robotic manipulators, insect-scale robots, and manufacturing automation—as well as to domains beyond robotics such as computer-aided design, AR/VR, computational biology, and complex engineering design problems like wire routing!
Computational Biology
Our group is investigating applications of sampling-based motion planning methods to protein folding, ligand binding (i.e., drug docking, which arises in drug design), RNA folding, and neuroscience.
Computational Geometry
Our group is investigating applications of sampling-based motion planning methods to protein folding, ligand binding (i.e., drug docking, which arises in drug design), RNA folding, and neuroscience.
Human-Robot Interaction
We develop projects that enable natural, intuitive human-robot interaction. Using Extended Reality, we create immersive experiences that help users better understand robots’ capabilities and limitations. We also explore learning from demonstration and human-robot interaction to help robots learn from people and assist with everyday tasks.
Mind in Vitro
This NSF Expedition in Computing will develop the science and technology to fabricate, model and program systems based on living neurons. Interfacing with muscles and sensors, the behavior of these machines will evolve as they probe their environment, explore and respond to it.
Motion Planning
We develop sampling-based planners, roadmap methods, and geometric algorithms that enable robots and agents to navigate high-dimensional configuration spaces efficiently. Applications span autonomous manipulation, surgical robotics, game AI, and virtual prototyping.
Multi-Agent Systems
We present projects related to multi-agent systems, ranging from pure motion planning techniques for coordinating a team of robots to more complex problems involving task allocations and task-and-motion for multiple agents.
STAPL
The Standard Template Adaptive Parallel Library is a framework for developing parallel programs in C++. It is designed to work on both shared and distributed memory parallel computers and its core is a library of ISO Standard C++ components with interfaces similar to the (sequential) ISO C++ standard library.