About Me
I am a PhD Student in Computer Science at Clemson University with a demonstrated history of working in the area of IoT, Data Science and Engineering. Skilled in intermittent and ultra-low-power computing for embedded systems, wireless sensor networks, energy harvesting techniques for emerging batteryless devices, and resource-efficient machine learning for embedded and mobile platforms. I work in the PERSIST Research Laboratory under Dr. Jacob Sorber.
Research Interests
My research lies in the area of batteryless computing and wireless sensor networks, particularly on investigating efficient communication mechanisms for emerging battery-free, energy harvesting devices and sensors that are plagued with frequent power failures and resource constraints. My research interests span the following areas:
Education
Ph.D. Computer Science
Clemson University, Clemson, SC, USA. 2022 (Anticipated)B.Sc. Computer Engineering
Obafemi Awolowo University, Ile Ife, Osun, Nigeria. 2014OND Electrical and Electronics Engineering
Federal Polytechnic Ilaro, Ogun, Nigeria. 2008News
(08/2019) I'll be participating in the National Society of Blacks in Computing (NSBC) Conference 2019, Atlanta, GA.
(03/2019) I was selected to participate in the Computing Research Association (CRA) Grad Cohort Workshop in Hawaii, March 2019.
(08/2018) I'll be going to Northwestern University, Evanston for the Cross-layer Computing Summer School: Circuits to System 2018 workshop.
(03/2018) I will be attending the Computing Research Association Grad Cohort Workshop in San Diego, California.
(08/2017) I started my graduate program at Clemson University and joined the PERSIST Research Lab.
Recent Projects
Python 2.7.x Interpreter
In this project, I implemented an interpreter for python 2.7.x using flex (scanner) and bison (parser). The interpreter was written in C/C++ and is capable of interpreting most of Python 2.7.x language constructs.
UV Risk Detection/Prevention
This system is a set of wearable devices that inform the user about UV exposure rate. One device senses UV radiation and the other displays the UV radiation levels. The display device shows the UV index.