Research
Foundations advancing critical applications
The lab pursues a common set of theoretical foundations — control and systems theory, network structure and causal inference, distributed optimization, thermodynamics at the small scale — and puts them to work on critical applications in sustainable energy, nanoscience, single-molecule and cellular biophysics, and machine learning. Each area below is its own focused research program with its own people, publications, and industry partnerships.
Research areas
-
Theoretical Foundations
UmbrellaThe theoretical foundations that underpin every applied direction in this lab — control theory, learning of network structure and causation, distributed optimization, nonlinear dynamics, and thermodynamics at the small scale.
-
Machine Learning & Data-Driven Methods
Physics-augmented deep learning, reinforcement learning for security-critical control, and information-theoretically optimal learning of dynamical systems.
-
Sustainable Energy & Power Systems
Self-organizing power systems for the renewable grid — a plug-and-play architecture for photovoltaics, battery storage, and grid-edge devices.
-
Nanoscience & Instrumentation
Systems and control perspectives applied to atomic force microscopy, optical tweezers, and probe-based interrogation of matter at the small scale.
-
Single-Molecule and Cellular Biophysics
Single-molecule and small-ensemble investigations of molecular motors, cargo transport, and the mechanics of proteins relevant to human disease.