We study and engineer how living systems compute in time. Life depends on dynamic processes — cells must decide when to divide, proteins must switch states in response to signals, and neurons must coordinate activity across timescales to produce behavior. Yet, the physical principles that govern these processes remain poorly understood.

Our lab combines biophysical theory, machine learning, and systems and synthetic biology to reveal how timing, computation, and evolution shape biological networks. We investigate these questions across scales:

• Cellular decision-making: How do risk and speed interplay in biological error correction?

• Protein and cell engineering: How can we use evolution and machine learning to design switchable proteins and push the limits of cell physiology?

• Neural computation: Can we predict how neuronal circuits generate complex, time-dependent behaviors?

By integrating ideas from physics, biology, and engineering, we aim to uncover fundamental principles of biological dynamics and to create new tools for controlling and designing living systems.