I direct IntellEcT Systems Lab (Intelligent, Efficient, and Trustworthy Systems). We are primarily interested in (a) developing efficient and trustworthy machine learning systems, (b) adaptive and efficient robotics, (c) machine learning for health, and (d) designing and evaluating novel mobile systems and applications.
Embedded and Sustainable AI Systems:
Optimizing machine learning models for inference and learning (continual, federated learning) to work efficiently on embedded systems ranging from smartphones, and edge devices - Raspberry Pi/Jetson Nano to MCU.
Designing hardware accelerators by doing hardware-software co-optimizations.
Mobile Systems:
Develop novel systems utilizing off-the-shelf commodity sensors, hardware, and AI for various domains including healthcare.
Our Current Research