Bharat Singhal

I am a third-year Ph.D. student in the Applied Mathematics Lab at Washington University in St. Louis. My primary research interests are in control theory, network science, and nonlinear dynamics. Specifically, my research focuses on understanding and controlling complex networks of nonlinear units using tools from control theory and optimization.

I received my bachelor’s degree (B.Tech) in Electrical Engineering and my master's degree (M.Tech) in Control Systems Engineering from IIT Kharagpur in 2017. After obtaining my master's, I worked as an engineer in the Design Methodology and Kit Development (DMKD) division at TSMC for three years.

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I am passionate about the following research problems that have potential applications in a variety of scientific disciplines, ranging from neuroscience to circadian biology.

• Data-driven Network Inference: Inferring the connectivity structure of a network of nonlinear oscillators from the measurement data.
• Control of Dynamic Patterns: Designing external stimuli to regulate the collective behavior, such as synchronization or desynchronization, for a network of nonlinear oscillators.

Designing optimal entrainment signals that entrain an ensemble of heterogeneous nonlinear oscillators at desired phases.

Designing time or energy-optimal controls for a network of coupled neurons.

A principled control technique for designing optimal stimuli that produce desired spatiotemporal patterns in a network of interacting neurons without requiring feedback information.

A data-driven inference technique that can identify the network structure reliably in the case of limited measurement data.

A system-theoretic approach for finding the most influential node for network stabilization.