Faculty and Staff
Ismail Uysal, PhD
RESEARCH INTERESTS
Dr. Uysal conducts impactful and interdisciplinary research on AI & machine learning and related IoT applications using radio-frequency-identification (RFID) & wireless sensor technologies specifically in healthcare, smart agriculture, and brain-computer interfaces. He was the PI/CoPI in research awards with a combined budget of more than $5 million over the last decade sponsored by U.S. National Science Foundation, U.S. Department of Defense, U.S. Department of Agriculture, Walmart Foundation, Florida Department of Transportation, Florida High Tech Corridor, and major industry including pharmaceutical and IoT technology companies.
He has written more than 40 peer-reviewed publication in reputable international journals and conferences and holds 3 U.S. Patents for his contributions to science and technology.
BIOGRAPHY
Dr. Uysal got his Bachelor of Science in Electrical and Electronics Engineering degree from Orta Dogu Teknik Universitesi in Ankara, Türkiye in 2003 followed by a Ph.D. degree in Electrical and Computer Engineering from the University of Florida (UF), Gainesville, FL, USA in 2008. Since 2012, he has been with the University of South Florida, Tampa, FL USA where he is currently an Associate Professor and the Undergraduate Director of the department of Electrical Engineering.
Honors and Awards
- IEEE – Senior Member
- National Academy of Inventors – Member
- SCEEE – Young Investigator Research Award
- University of Florida Alumni Fellowship
Teaching
- EEL 4102 – Signals and Systems (offered every Spring)
This undergraduate course helps the students learn about the fundamental concepts of signals and systems in both continuous and discrete time and use techniques to analyze and understand the operations of linear systems in both time and frequency domain. Coupled with its lab course – Computer Tools Lab - the students learn how to work with signals using software and understand linear system responses.
- EEL 6789 – Deep Learning (offered every Spring)
This graduate course focuses on both the basics of machine learning theory (including regression, gradient descent, supervised & unsupervised learning) and its conventional and modern applications using neural networks & deep learning case studies specifically for classification and feature learning with convolutional neural networks and autoencoders.
- EEE 6542 – Random Processes (offered every Fall)
This graduate course introduces the concepts of a random variable, singular and multiple functions of a random variable and joint probability distributions. Students learn the fundamentals of random processes including autocorrelation and power spectra, estimation, filtering, and prediction and how linear systems react to random processes. Finally, the course covers the fundamental principles of Markov processes.
Current and Recent Grants
Agency: U.S. National Science Foundation (NSF)
Title: IUSE/PFE:RED: Breaking Boundaries: An Organized Revolution for the Professional Formation
of Electrical Engineers
Role: Co-principal Investigator
Funding Amount: $2,000,000 between 2020 – 2025
Agency: U.S. Department of Agriculture (USDA) Specialty Crop Federal Award Program
Title: Smart Strawberry Monitoring and Logistics
Role: Principal Investigator
Funding Amount: $151,582 between 2019 – 2022
Agency: DeltaTrak Corporation
Title: Algorithmic Prediction and Estimation of Product Temperatures Using Wireless Sensors
Role: Principal Investigator
Funding Amount: $187,871 between 2016 – 2020
Agency: U.S. Department of Agriculture (USDA) Specialty Crop Federal Award Program
Title: Sensor-assisted Sustainable and High Quality Strawberry Production with Wireless
Real-time Field Monitoring
Role: Principle Investigator
Funding Amount: $139,891 between 2018 – 2020
Agency: Florida High Tech Corridor (FHTC) Matching Research Award
Title: Algorithmic Prediction and Recognition of Human Activity and Falls from Wireless Accelerometer Data
Role: Principle Investigator
Funding Amount: $124,989 between 2018 – 2019