- Ph.D., Computer Engineering, Clemson University, 2020
- M.S., Computer Engineering, Clemson University, 2014
- B.E., Electronics & Telecommunications Engineering, University of Mumbai, India, 2012
- AI for Medical Diagnosis, Deeplearning.ai, April 2020, Credential ID AMZJRYV59HQW
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Deeplearning.ai, April 2018, Credential ID YE68399LVPN2
- Neural Networks and Deep Learning, Deeplearning.ai, April 2018, Credential ID EV9S2MNJ5XQT
- Graduate Teacher of Record, Basic Electrical Engineering, Clemson University, 2020
- Graduate Teacher of Record, Basic Electrical Engineering, Clemson University, 2014
- Graduate Teacher of Record, Programming in MATLAB, Clemson University, 2014
- Teaching Assistant, Basic Electrical Engineering, Clemson University, 2013
- Member, IEEE
- Member, ACM
Dr. Sharma is a computer scientist with expertise in machine learning, embedded computing, system design, data science, and computer vision. He has industry experience creating defect inspection systems for manufacturing plants, as well as experience working with human factors researchers, clinicians and nutritionists in applying machine learning and data science in the domains of healthcare and biomedical engineering.
During these projects, he has developed sensors, camera systems, and software that collect large-scale data sets. His programming language experience includes, but is not limited to, C, C++, MATLAB and Python.
Prior to joining Exponent, Dr. Sharma worked on a camera based real-time defect inspection system for Samsung Electronics Home Appliances and assisted the Fraunhofer USA Center for Experimental Software Engineering (CESE) in implementing computer vision algorithms through easy to use software interfaces.
Dr. Sharma received his Ph.D. in Computer Engineering from Clemson University, where he researched and developed low-power wearable sensors to track wrist motion in everyday life, and later published the largest wrist motion dataset in the field of automated dietary monitoring. He analyzed and visualized the collected data to develop machine learning models and convolutional neural networks using Tensorflow and Keras that recognize everyday human activities. These algorithms are now deployed to Android and Apple wearable devices and used by clinicians in diabetes research.