Ms. Aayushi Bansal

Assistant Professor

Interest Area(s)

  • Machine Learning
  • Image Processing
  • Evolutionary Computing.

Aayushi Bansal is an Assistant Professor in Computer Science with expertise in machine learning, computer vision, and data-driven intelligent systems. She is currently pursuing her Ph.D. at J.C. Bose University of Science and Technology, with research focused on vision-based fall detection using keypoint analysis, deep learning, and optimization techniques. She has published in reputed SCIE journals with high impact factor, including Computers and Electrical Engineering and ACM Computing Surveys. 

She holds an M.Tech. with a Gold Medal and is UGC-NET qualified. With several years of teaching experience, she has taught core subjects such as DBMS, Data Structures, and Programming, while actively mentoring student projects and research. She is committed to integrating innovative, outcome-based teaching with practical and research-oriented learning. 

  • B.Tech. Computer Science and Engineering from Kurukshetra University, Kurukshetra (2022- 2015), 
  • M.Tech. Gold Medalist Computer Science and Engineering from Guru Jambeshwar University of Science and Technology, Hisar (2015-2017), 
  • Ph.D. Machine Learning from J.C. Bose University of Science and Technology, YMCA Faridabad (2019-2026) 
  • Assistant Professor at J.C. Bose University of Science and Technology, YMCA Faridabad (4 years) 
  • A. Bansal, R. Sharma, and M. Kathuria, “A systematic review on data scarcity problem in deep learning: Solution and applications,” ACM Computing Surveys, vol. 54, no. 10s, pp. 1–29, 2022, doi: 10.1145/3502287. (Impact Factor: 28)
  • A. Bansal, R. Sharma, and M. Kathuria, “Diversity-regularized multi-objective optimized random forest for fall detection using cost-sensitive and key point feature-based learning,” Computers and Electrical Engineering, vol. 128, pt. B, p. 110684, 2025, doi: 10.1016/j.compeleceng.2025.110684. (Impact Factor: 4.9) 
  • A. Bansal, R. Sharma and M. Kathuria, "A Comparative Study of Object Detection and Pose Detection for Fall Detection using Detectron2," 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), New Delhi, India, 2024, pp. 1-8, doi: 10.1109/DELCON64804.2024.10866659. (Scopus Indexed) 
  • A. Bansal, R. Sharma and M. Kathuria, "A Vision-Based Approach to Enhance Fall Detection with Fine-Tuned Faster R-CNN," 2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech), Banur, India, 2023, pp. 678-684, doi: 10.1109/ICACCTech61146.2023.00114. 
  • Qualified GATE (2017, 2018),
  • Qualified UGC-NET (Nov 2017/July 2018/Dec 2018),
  • Gold Medalist (Sitaram Jindal Foundation)