Rate My Professor Brian Plancher

Brian Plancher is an Assistant Professor of Computer Science at Dartmouth College, where he has been since September 2025. His research centers on optimizing robotic systems at all scales by developing, optimizing, implementing, and evaluating next-generation algorithms and edge computational systems through algorithm-hardware-software co-design, such as MPCGPU, GRiD, and TinyMPC. This work lies at the intersection of robotics, computer architecture, embedded systems, numerical optimization, and machine learning. Plancher leads the Accessible and Accelerated Robotics Lab at Dartmouth, advancing real-time, high-performance control for resource-constrained robotic platforms.

Plancher received his BA magna cum laude in Computer Science with a minor in Economics from Harvard University in 2013, an MEng in Engineering Sciences with a focus in Electrical Engineering and Robotics in 2018, and a PhD in Engineering Sciences with a focus in Electrical Engineering and Robotics in 2022, all from Harvard. Before Dartmouth, he was Assistant Professor of Computer Science at Barnard College, Columbia University from July 2022 to June 2025, Research Scientist at Barnard since July 2025, and held affiliate faculty positions in Computer Science and Electrical Engineering at Columbia's Fu Foundation School of Engineering and Applied Science. He served as a Visiting Scholar at Dartmouth from April to August 2025. His contributions have earned major awards, including Best Paper in Automation at IEEE ICRA 2024 for 'TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers,' along with finalist positions for Best Conference Paper and Best Student Paper at ICRA 2024. Additional honors encompass the NSF Graduate Research Fellowship, IEEE Micro Top Picks Honorable Mention, Barnard College Teaching Excellence Award Finalist, and several best poster awards. Key publications feature 'MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU' (ICRA 2024), 'GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients' (ICRA 2022), and 'RoboPrec: Enabling Reliable Embedded Computing for Robotics by Providing Accuracy Guarantees Across Mixed-Precision Datatypes' in IEEE Robotics and Automation Letters (2025). As principal investigator, he has secured over $1.9 million in funding from NSF, Toyota Research Institute, and other sources to support his research on GPU-accelerated edge optimal control and uncertainty-aware optimization for intelligent control.