Hello, I'm
CSE-AIML Senior @ PESU · ML · Full Stack · Knight @ LeetCode · 3x Published


About Me
Hey folks! I'm Musadiq, a CSE-AIML Senior at PES University, Bangalore. As a curious and driven learner, I'm passionate about all things AI and Machine Learning, with a growing interest in deep learning, reinforcement learning, and explainable AI.
I enjoy diving into new tech, picking up hands on experience, and learning through real world applications. Along the way, I love connecting with like minded individuals and sharing knowledge as we grow together in this ever evolving field.
I'm always on the lookout for exciting opportunities to upskill and make meaningful contributions.
- PES University – B.Tech in Artificial Intelligence and Machine Learning (2022–2026)
- Grade: 8.12
- BGS PU College – Pre-University (PCMC, 2020–2022)
- Grade: 98.5% (State 8th Rank, 591/600)
- Vidyanidhi Public School – ICSE (2010–2020)
- Grade: 93%
Experience
Research Intern
CDSAML, PES University
Jan 2026 – Present
Bengaluru, Karnataka · On-site
Building an end-to-end Legal Judgment Prediction system for Indian Supreme Court cases using RAG and fine-tuned LLaMA 3.1 8B. Structured a dataset from 700+ cases and achieved BERTScore F1 of 0.85 on held-out evaluations.
Skills & Technologies
Programming Languages
Databases & Tools
ML Libraries & Frameworks
Web Development
My Projects
Stats & Activity
Research Papers
Comparative Analysis of Traffic Accident Detection with Emphasis on Explainability of Dl Models
Computer Vision
Utilized traditional ML and deep learning models including CNN, LSTM, and YOLOv8 to classify traffic accident images, with SHAP-based interpretability for enhanced decision transparency.
Beyond Loyalty and Betrayal: A Knowledge Graph-Enhanced RAG System for Crime Film Insights and Analysis
Natural Language Processing
Developed a Knowledge Graph and RAG system for analyzing crime films, enabling nuanced question answering and thematic exploration beyond factual retrieval.
HyperGNNs for Multi-Modal Classification and Severity Analysis of Neurodegenerative Disorders
Graph Neural Networks
Designed a Hypergraph Neural Network (HyperGNN) framework for multi-modal classification and severity prediction of neurodegenerative disorders, integrating heterogeneous clinical data to improve diagnostic accuracy and interpretability.