Steven Fernandes

Steven Fernandes began his postdoctoral research at the University of Alabama at Birmingham, where he worked on NIH-funded projects. He also conducted postdoctoral research at the University of Central Florida. This research included working on DARPA, NSF, and RBC funded projects. His publications include research articles in highly selective artificial intelligence venues. He received his Ph.D., M.Tech, and B.E. in Electronics and Communication Engineering from Karunya Institute of Technology and Sciences, Manipal Institute of Technology, and Visvesvaraya Technological University respectively. His current area of research is focused on using artificial intelligence techniques to extract useful patterns from big data. This includes robust computer vision applications using deep learning and computer-aided diagnosis using medical image processing.

Recent Publications

  • Pannu, J.S., Raj, S., Fernandes, S.L., Chakraborty, D., Rafiq, S., Cady, N. and Jha, S.K., 2020. Design and Fabrication of Flow-Based Edge Detection Memristor Crossbar Circuits. IEEE Transactions on Circuits and Systems (TCAS) II: Express Briefs, 67(5), pp.961-965.

  • Raj, S., Pannu, J.S., Fernandes, S.L., Ramanathan, A., Pullum, L.L. and Jha, S.K., 2020. Attacking NIST Biometric Image Software using Nonlinear Optimization. Pattern Recognition Letters (PRL), 131, pp.79-84.

  • Fernandes, S., Raj, S., Ewetz, R., Singh Pannu, J., Kumar Jha, S., Ortiz, E., Vintila, I. and Salter, M., 2020. Detecting Deepfake Videos using Attribution-Based Confidence Metric. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (pp. 308-309).

  • Jha, S., Raj, S., Fernandes, S., Jha, S.K., Jha, S., Jalaian, B., Verma, G. and Swami, A., 2019. Attribution-Based Confidence Metric For Deep Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS) (pp. 11826-11837).


  • Spring 2021:
    • CSC 221 - Introduction to Programming
    • CSC 414 - Introduction to Computer Organization
    • CSC 590 - Neural Networks and Deep Learning (Special Topics). This class is supported by DataCamp, the most intuitive learning platform for data science and analytics. Learn any time, anywhere and become an expert in R, Python, SQL, and more. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 350+ courses by expert instructors on topics such as importing data, data visualization, and machine learning. They’re constantly expanding their curriculum to keep up with the latest technology trends and to provide the best learning experience for all skill levels. Join over 6 million learners around the world and close your skills gap.