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Academic Experts

Ashi Agarwal

Biography

Ms. Ashi Agarwal is an Assistant Professor in the Department of Computer Science & Engineering and Information Technology (CS&E and IT) at Jaypee Institute of Information Technology, Noida. She is pursuing her Ph.D in the Department of Information Technology at Delhi Technological University (DTU), Delhi. Her core research interests lie in the domains of Computer Vision and Deep Learning, with a focus on developing intelligent systems capable of solving real-world problems through advanced AI techniques. 

She has published more than 15 Research papers in SCIE, E-SCIE and Scopus journals, conferences. Ahe has actively participates in FDPs, conferences organizing committee. Her academic journey reflects a strong foundation in engineering and a passion for innovation. Through her work, Ms. Agarwal aims to bridge the gap between theoretical advancements in AI and practical applications that benefit society.

Research Highlights

Ashi Agarwal’s research lies at the intersection of computer vision, deep learning, and next generation communication systems, with a strong emphasis on solving real world challenges in face recognition, facial emotion recognition under occlusion, image fusion, and 6G technologies. 

Her research address critical issues of recognition accuracy in resource constrained environments, making them highly relevant for applications in healthcare, security, and human–computer interaction. Beyond vision systems, she has extended her expertise to 6G communication networks, co authoring surveys and book chapters on vehicular technologies, digital twin architectures, and security frameworks. 

These contributions highlight her ability to bridge AI driven perception systems with next generation communication infrastructures, positioning her research at the forefront of interdisciplinary innovation. 

Areas of Interest
  • Deep Learning
  • Computer Vision
  • Machine Learning
Publications

1.  A. Agarwal and S. Susan, “Attention-augmented squeeze-and-excitation enhanced mobile network for occluded facial expression recognition in resource-constrained environments,” Signal, Image and Video Processing, vol. 19, no. 9, p. 687, 2025.
2.   A. Agarwal and S. Susan, “Unified deep learning framework with two-way transfer learning for occluded faces: A soft decision fusion approach,” Pattern Recognition and Image Analysis, vol. 35, no. 3, pp. 269–274, 2025.
3.  A. Agarwal and S. Susan, “Two-way transfer learning using mobile-dense network for occluded face emotion recognition: A soft decision fusion approach,” in Proc. Int. Conf. Pattern Recognition, Cham, Switzerland: Springer Nature, Dec. 2024, pp. 63–71.
4.  A. Saxena, A. Agarwal, B. K. Pandey, and D. Pandey, “Examination of the criticality of customer segmentation using unsupervised learning methods,” Circular Economy and Sustainability, pp. 1–14, 2024.
5. A. Agarwal and S. Susan, “Emotion recognition from masked faces using Inception-v3,” in 2023 5th Int. Conf. Recent Advances in Information Technology (RAIT), Mar. 2023, pp. 1–6, IEEE.