Research Overview

I am actively pursuing research in the following areas.

Stochastic Computing

Extreme Energy Efficiency

Exploiting Parallelism and Scalability

Ultra-low-power
IoT

Wearable Electronics

Optimizing Healthcare with ML

Stochastic Computing explores approaches for dramatically improving energy efficiency by re-designing processors and applications from the ground up to exploit software- or hardware-based noise tolerance. Current projects include development and automation of tools and methodologies for stochastic software and hardware design and architecture.

I am exploring unconventional approaches for achieving extreme energy efficiency, especially in the context of ultra-low-power embedded systems and exascale systems. Research directions include mitigation and elimination of traditional scalability and energy efficiency bottlenecks through approximate computing, application-aware optimization, peak power management, and variation- and application-aware energy management.

Some of my research involves exploiting parallelism and scalability to enable new science and optimizing architectures, algorithms, and data structures to achieve orders of magnitude improvements in performance scalability and energy efficiency. Current projects include enabling novel sensor networks and accelerating CAD tools by exploiting parallelism and scalability afforded by massively-parallel processors.

Designing and optimizing ultra-low-power embedded systems for the internet of things (IoT), focused on optimizing systems for their target applications and providing security guarantees.

Design of body-integrated wearable electronics, focused on design for manufacurability, enhancing human function and performance, and electronic design automation for wearable circuits.

Applying machine learning to optimize healthcare processes, focused on providing better experiences for patients and providers, improving efficiency, automation, and ultimately improving patient outcomes.