Research

Current Research Interests

→ Applied Mathematics

→ Computational Mathematics

→ Artificial Intelligence

→ Internet of Things

Current Projects

→ Artificial Intelligence for supporting integrated sensing and secured Communication in 6G assisted Edge Networks ; Guide: Dr. Mainak Adhikari; Description: I initially worked on an RFID-based asset tracking system using ESP32. After completing that project, I started developing a more advanced asset tracking model that integrates cameras with a Raspberry Pi. This system is planned to be further enhanced using federated learning and is intended for deployment in the Central Library of IISER Thiruvananthapuram. The project is funded by the Department of Telecommunications, Government of India, and the Indian Institute of Science Education and Research Thiruvananthapuram.

Past Projects

→ Understanding and analysis Physics-Informed Neural Networks ; Guide: Dr. Sivaram Ambikasaran; Description: Firstly, I thoroughly went through all the chapters of Tariq Rashid's Make Your Own Neural Network. Following that, I studied the paper Artificial Neural Networks for Solving Ordinary and Partial Differential Equations by Isaac Elias Lagaris, Aristidis Likas and Dimitrios I. Fotiadis, along with several related references, and implemented the core concepts.

→ Analysis and synthesis of feature maps for kernel-based quantum machine learning ; Guide: Dr. Dhinakaran Vinayagamurthy and Dr. Chandrashekar Lakshminarayanan; Description: Firstly, I thoroughly went through all the chapters of Mikio Nakahara and Tetsuo Ohmi's Quantum Computing: From Linear Algebra to Physical Realizations. Following that, I studied the paper Quantum tangent kernel by Norihito Shirai, Kenji Kubo, Kosuke Mitarai, Keisuke Fujii along with several related references, and implemented the core concepts.

→ Understanding and analysis Partial Differential Equations based Image Processing ; Guide: Dr. Mahipal Jetta; Description: Firstly, I studied the paper A Diffusion Equation for Improving the Robustness of Deep Learning Speckle Removal Model by Li Cheng, Yuming Xing, Yao Li and Zhichang Guo, along with several related references, and implemented the core concepts. Following that, I developed a model based on the Implicit Prior–Nonlinear Fractional Diffusion Equation to effectively remove multiplicative noise from images.