Tinoosh Mohsenin is currently an Associate Professor with the Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, where she is also the Director of the Energy Efficient High Performance Computing Lab. She received her PhD from University of California, Davis in 2010 and MSc degree from Rice University in 2004, both in Electrical and Computer Engineering. Prof. Mohsenin’s research focus is on designing energy efficient embedded processors for machine learning and knowledge extraction computing techniques used in autonomous systems, wearable smart health monitoring, and Internet of Things. She has over 150 peer-reviewed journal and conference publications and has directed over $5M research funding from NSF, ARL, DARPA, and various biomedical institutes and industrial sponsors. She is the recipient of NSF CAREER award in 2017, the best paper award in the ACM Great Lakes VLSI conference 2016, and the best paper honorable award in the IEEE Circuits and Systems Symposium 2017 for developing processors in biomedical and deep learning. She is currently the Corresponding Guest Editor JETCAS and has previously served as Associate Editor in TCAS-I and TBioCAS. She received ACM Service Award for her contributions as Program Chair and General Chair of 29th and 30th ACM Great Lake VLSI Symposium in 2019 and 2020, respectively. She was a recipient of the ISSCC 2020 Evening Session Award for co-organizing an evening session entitled “The Smartest Designer in The Universe”. She was the invited Keynote Speaker of the IEEE AI Circuits and Systems Conference (AICAS), 14th IEEE Dallas Circuits and Systems Conference (DCAS) and 27th IEEE International Conference on Electronics Circuits and Systems (ICECS) in 2020. She is Currently serving as Technical Program Co-Chair of tinyML Research Symposium 2022. She also serves as Tutorial Co-Chair and Special Session Co-Chair of IEEE NEWCAS and AICAS conferences and is a Co-organizer and Moderator of an Evening Session titled "The Bright and Dark Side of Artificial Intelligence" in the ISSCC 2022 conference.

For Updated list of Alumni and Current students please refer to my research lab EEHPC Team

Graduated Students

Ph.D. Students:

For Updated list of Alumni and Current students please refer to my research lab EEHPC Team

3. Ali Jafari, PhD, December 2017
Senior Embedded Systems Engineer, Intel Inc.
Thesis title: “An Embedded Multi-Modal Deep Neural Network Processor for Time Series Data Classification”
2. Amey Kulkarni, PhD, February 2017
Senior Embedded Systems Engineer, Velodyne LiDAR Inc.
Thesis title: “Heterogeneous and Scalable Sketch-based Framework for Big Data Acceleration on Low Power Embedded Cores”
1. Adam Page, PhD, November 2016
Senior R&D Engineer, Samtec Inc.
Thesis title: “Deploying Deep Neural Networks in Embedded Real-Time Systems”

M.S Students:
9. Puranik, Abhilash, “Embedded Low-Power Processor Analysis for Stress Detection,” University of Maryland, Baltimore County, August 2017
8. Kumar Konuru, Sri Harsha, “An EEG Artifact Identification Embedded System using ICA and Multi-Instance Learning,” University of Maryland, Baltimore County, August 2017
7. Abtahi, Tahmid Syed, “Accelerating Convolutional Neural Network with FFT on Embedded Hardware,” University of Maryland, Baltimore County, July 2017
6. Attaran, Nasrin, “Architecture Exploration for Low-Power Wearable Stress Detection Processor,” University of Maryland, Baltimore County, April 2017
5. Sagedy, Christopher, “Development of an Architecture Simulator for the EEHPC Many-Core Processor,” University of Maryland, Baltimore County, December 2015
4. Smith, Emily, “The Design and Implementation of a Scalable Bus-based Cluster with Shared Memory for a Programmable Many-Core Platform,” University of Maryland, Baltimore County, December 2015
3. Viseh, Sina, “A Low Power On-board Processor for a Tongue Assistive Device,” University of Maryland, Baltimore County, August 2014
2. Korde, Asmita, “Detection Performance and Computational Complexity of Radar Compressive Sensing for Noisy Signals,” University of Maryland, Baltimore County, July 2014
1. Chandler, James Darin, “An Efficient Network on Chip Targeted to a Parallel, Low Power, Low-area Homogenous Many-Core DSP Platform,” University of Maryland, Baltimore County, May 2012