School of Electrical Engineering and Computer Science

The saturating scaling trends of CMOS technology have motivated the exploration of emerging Process-in-Memory (PIM) architectures as a promising alternative for accelerating data intensive Machine Learning (ML) workloads. To that effect, researchers have explored special-purpose accelerators based on Non-volatile Memory (NVM) crossbar primitives such as Resistive Random Access Memory (ReRAM)-based PIM accelerators.

Stress recognition and monitoring from wearable sensor data is an emerging area of research with significant implications for an individual’s physical, social, and mental health. Mobile health interventions that incorporate real-time monitoring of physiological and behavioral markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress.

Sustainable Defenses against Evolving Malware in the Android Ecosystem