Experience

Teaching Assistant

IIT Indore

August 2025 – Present • Education / Communication Systems

  • TA under Prof. Dibbendu Roy for the course EE319 - Design and Analysis for Communication Systems
  • Assisted in designing and grading challenging quizzes
  • Conducted tutorials on topics including Random Variables, Random Processes, and Queuing Theory

Tech Stack:

Teaching Communication Systems Probability Theory

Developer Intern

Samsung R&D Institute India

May 2025 – July 2025 • 6G Communications / Machine Learning

  • Collaborated with the 6G Standards Team to develop novel machine learning–based methods for CSI-RS (Channel State Information Reference Signal) processing
  • Designed and implemented UNet-inspired deep learning architectures, achieving a Normalized Mean Squared Error (NMSE) in the order of 1e-3
  • Work under patent filing process for its potential impact on future 6G physical layer standards
  • Received return offer from Samsung for the Advanced Developer Role

Tech Stack:

Python PyTorch Deep Learning 6G Communications Signal Processing

Research Intern

Prof. M. Tanveer | OPTIMAL Research Group, IIT Indore

May 2024 – Present • Deep Learning / Audio-Visual Speech Enhancement

  • Developed LSTMSE-Net, an audio-visual speech enhancement model to isolate and enhance speaker audio in noisy environments
  • Engineered a temporal feature extraction pipeline using RNN and LSTM units to jointly model audio-visual dependencies
  • Achieved a 3× reduction in inference time compared to the baseline model with improvements in speech quality
  • Original paper accepted in InterspeechW 2024. Currently working on an advanced version using ConvNeXtV2 based video pipeline and an audio decoder inspired from deep state space modelling

Tech Stack:

Python PyTorch Deep Learning LSTM RNN Audio Processing Video Processing

Research Intern

Prof. Nagendra Kumar | LIPG, IIT Indore

May 2023 – Present • Deep Learning / Medical Image Segmentation

  • Developed novel U-Net based deep learning models for liver tumor segmentation, handling data pre-processing and creating custom callbacks, metrics, and loss functions
  • Implemented advanced architectures like Squeeze-and-Excitation Networks and Atrous Spatial Pyramid Pooling, achieving a state-of-the-art accuracy of 98%
  • Co-authored a research paper detailing the methods and results. Published in Elsevier Biomedical Signal Processing and Control

Tech Stack:

Python PyTorch TensorFlow Deep Learning U-Net Medical Imaging Computer Vision

Research Contributor

Dr. Debesh Jha | Northwestern University

May 2024 – July 2024 • Deep Learning / Medical Image Segmentation

  • Implemented various liver tumor segmentation models including DeepLabv3+, UNet, and HiFormer-L on the LiTS dataset using PyTorch and TensorFlow
  • Engineered custom, modular PyTorch data loaders and transformation pipelines for the LITS dataset
  • Source code available at GitHub

Tech Stack:

Python PyTorch TensorFlow Deep Learning Medical Imaging Computer Vision