Students

Ahish Shylendra joined AEON Lab in Fall 2017 as a Ph.D. student. He received a B.E. degree in Electronics and Communication Engineering from the Global Academy of Technology, Bengaluru, in 2014. Further, he obtained M.Tech. degree in VLSI from the National Institute of Technology Goa, Ponda, India, in 2016. His current research interests include hardware security, emerging devices, analog, and mixed-signal design.

Awards: Graduate Research Award in 2018 & 2020.

Summer Internships: Qualcomm (2020).

Shamma Nasrin joined AEON Lab in Fall 2017 as a Ph.D. student. Her research interests include Device Technologies and VLSI system Design with emerging Technologies. She completed B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET).

Awards: Computing Research Association’s Graduate Award, 2018.

Summer Internships: Global Foundries (2020).

Maram Sulimani completed her undergraduate degree from Michigan State University (MSU), in 2014; and her graduate degree from the University of Illinois at Chicago (UIC), in 2017. She joined the AEON lab in Fall 2017 as a Ph.D. student. Her research interest includes deep neural network and parallel processing.

Priyesh Shukla joined AEON lab in Fall 2018 as a PhD student. He received his B.E.(Hons.) in Electrical and Electronics Engineering, and M.E. in Microelectronics from Birla Institute of Technology and Science (BITS), Pilani, India in 2015 and 2017 respectively. He has worked with Qualcomm, Bangalore, India in 2017-18 and interned at Qualcomm, San Diego, USA in Summer 2020. His research interests are efficient hardware-software co-operated systems for probabilistic AI and nano-robotics.

Awards: Peter and Deborah Wexler Fellowship.

Summer Internships: Qualcomm (2020).

Nastaran Darabi joined AEON Lab in Spring 2021 as a Ph.D. student. She completed B.Sc. in Electrical Engineering with a specialization in Systems and Control at the Sharif University of Technology. Her current research interests include on-chip learning schemes such as reinforcement learning and quantum machine learning.