Currently pursuing Master of Technology in Machine Learning & Artificial Intelligence at Lovely Professional University (Final Semester, 8.91 CGPA). Passionate about applying deep learning to real-world problems, particularly in medical imaging, environmental forecasting, and computer vision.
M.Tech in ML & AI from Lovely Professional University — Final Semester with 8.91 CGPA
4 IEEE publications spanning plant disease detection, groundwater prediction, and pulmonary embolism
Deep Learning, Computer Vision, NLP, Medical Imaging, Time-Series Analysis & Hybrid ML-DL Models
Computer vision system utilizing InceptionNetV3 and EfficientNetB3 on 87,000+ leaf images across 38 classes. Implemented robust preprocessing including Gaussian blur and real-time augmentation.
Developed a hybrid model for time-series groundwater forecasting. Preprocessed real-world data with lag features, normalization, and complex 3D reshaping for temporal analysis.
Applied a transformer-based TransUNet model on the FUMPE medical imaging dataset. Trained with Dice loss to achieve stable convergence across 50 epochs for high-precision mask generation.
Custom NLP Transformer built with a proprietary Emotion-Embedding and Modified Attention layer. Optimized stability via dropout tuning and label smoothing.
IEEE ICRITO 2025
DOI: 10.1109/ICRITO66076.2025.11241941IEEE STCR 2025
DOI: 10.1109/STCR62650.2025.11019322IEEE ICRITO 2025
DOI: 10.1109/ICRITO66076.2025.11241237IEEE ICRITO 2025
DOI: 10.1109/ICRITO66076.2025.11241562IBM / Coursera
Issued Jan 2026
IBM / Coursera
Issued Nov 2025
IBM / Coursera
Issued Nov 2025
CipherSchools
Issued Jul 2025 · ID: CS2025-13820
IEEE Madras Section (STCR 2025)
Presented May 2025