Research

This letter introduces a safe information-theoretic learning model predictive control (SIT-LMPC) algorithm for iterative tasks.

We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) test-bed to diagnose, test, update and verify controls software.

My team has designed safe and efficient Learning-based decentralized collision avoidance for scalable urban air mobility.

We aim to develop a control scheme to reduce the peak power drawn from the source for hybrid energy systems with limited computation power and limited load forecasts.

My team designed a variety of Robust Anytime Model Predictive Controllers that instruct the perception pipeline.

This project aims to develop such methods by combining learning-based decision making and decentralized Model Predictive Control to perform on-the-fly, predictive Collision Avoidance (CA) between two (co-operative) drones.

In this work, we will delve into the problem of adaptive safe learning for multi-agent systems with CBF. We will show how emergent behavior can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design.

Process of patient-specific heart modeling is: extract patient data to tune heart model parameters, next, initiate arrhythmia (the heart's initial state is also extracted from patient data), then solve the heart model equations to simulate patient-specific arrhythmias.

Our heart model needs to be patient-specific. Therefore, we process clinical data and feed them into the heart model, so that we can tune the heart model’s parameters.

CACTs can be performed with very little cost before an actual clinical trial and the results can potentially provide useful insights for planning and conduction of the actual clinical trial.

Symbiotic Design for Cyber Physical Systems