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Shashi Ja 211

Dr. Shashi Bhushan Jha

Biography

Dr. Shashi Bhushan Jha, Assistant Professor in the Department of Computer Science, received his Ph.D. in Electrical Engineering and Computer Science from Embry Riddle Aeronautical University in Daytona Beach, Florida. His research interests include automated defect detection, computer vision, deep learning, machine learning, and optimization. Dr. Jha is actively publishing peer-reviewed journal articles. His previous work was published in high-impact factor journals such as Computers in Industry, Computers & Industrial Engineering, and the Journal of Intelligent Manufacturing. In addition, Dr. Jha serves as a reviewer for various journals, including CAIE, Information Sciences, and IEEE SMCHe, teaching graduate and undergraduate computer science courses and mentoring students on their capstone projects. Dr. Jha's industry experience includes a role as a research intern at Cash App @ Block Inc., where he collaborated with cross-functional teams to analyze large datasets and develop advanced machine-learning models.

Degrees & Institutions

  • Ph.D., Embry Riddle Aeronautical University in Daytona Beach, 2023
  • M.S, Indian Institute of Technology Kharagpur, India, 2018
  • B.E., Nagpur University, India, 2013

Research

Machine vision inspection, deep learning, machine learning, graph machine learning, data mining, sustainable computing, cybersecurity, and optimization.

Current Courses

  • COP 3530: Data Structures and Algorithms I
  • CAP 4770: Data Mining

Publications

  1. Jha, S. B., Babiceanu, R. F.(2023). Deep CNN-Based Visual Defect Detection: Survey of Current Literature. Computers In Industry, 148, 103911. (Impact Factor: 11.24)
  2. Jha, S. B., Pandey, V., Jha, R. K., & Babiceanu, R. F. (2020). Machine Learning Approaches to Real Estate Market Prediction Problem: A Case Study. arXiv preprint arXiv:2008.09922.
  3. Jha, S. B., Babiceanu, R. F., Pandey, V., & Jha, R. K. (2020). Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study. arXiv preprint arXiv:2006.10092.
  4. Pandey, V., & Jha, S. B. (2020). Incorporating Image Gradients as Secondary Input Associated with Input Image to Improve the Performance of the CNN Model. arXiv preprint arXiv:2006.04570.