Faculty and Staff

Yasin Yilmaz, PhD

Associate Professor

Office: ENB 251

Phone: 813-974-4788 

Email

Yasin Yilmaz

RESEARCH INTERESTS

Dr. Yilmaz directs the Secure & Intelligent Systems Lab. His team conducts research on the design and security of machine learning algorithms for various applications, including computer vision, cybersecurity, and environmental monitoring. His recent research focuses on real-time anomaly detection, adversarial machine learning, video understanding, and multimodal data fusion. 

BIOGRAPHY

Dr. Yilmaz joined 最新天美传媒 in 2016 as an assistant professor. He received his PhD degree in electrical engineering from Columbia University, New York, NY in 2014. He completed his Bachelor's and Master's studies in Turkey at the Middle East Technical University, Ankara, in 2008 and Koc University, Istanbul, in 2010, respectively. Between 2014 and 2016, Dr. Yilmaz worked as a postdoctoral research fellow at the University of Michigan, Ann Arbor. 

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Honors and Awards

  • Best Paper Award, IEEE Conference on Dependable and Secure Computing, 2023
  • Outstanding Research Achievement Award, USF, 2023
  • Outstanding Dissertation Award, Faculty Advisor, USF, 2023
  • Chih Foundation Research & Publication Award, Faculty Advisor, 2022
  • Finalist, Florida Blue Health Innovation Challenge, 2022.
  • Winner, Continual Learning Challenge, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
  • Winner, NIST Automated Streams Analysis for Public Safety Challenge, 2020
  • IEEE BigData Cup Second Rank Award, Global Road Damage Detection Challenge, IEEE International Conference on Big Data (IEEE BigData), 2020
  • AI City Challenge Second Rank Award, NVIDIA, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020
  • IEEE Senior Member, 2020
  • Best Paper Honorable Mention Award, ACM Conference on Recommender Systems (RecSys), 2019
  • Young Investigator Award, Southeastern Center for Electrical Engineering Education, 2017
  • Collaborative Research Award, Electrical Engineering Department, Columbia University, 2015

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Teaching

  • EEL 4102 Signals & Systems: A first course in the analysis of signals and linear systems. Includes time and frequency domain points of view such as Laplace and Fourier analysis as well as convolution.
  • EEE 4774 & 6777 Data Analytics: This course aims to teach the fundamentals of Machine Learning and Statistical Data Analysis. It covers the related theory in probabilistic inference and learning, as well as several applications in various fields.
  • EEL 6945 Advanced Data Analytics: A research-oriented course that focuses on complex data challenges by teaching useful probabilistic and statistical methods and providing hands-on experience. The topics of interest include nonlinear dimensionality reduction, mixture models, graphical models, variational inference, and deep learning. 

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