Int. M.Sc. Data Science student at Amrita Vishwa Vidyapeetham building production-grade AI systems in Sports Analytics, Explainable AI, RAG pipelines, and Graph Neural Networks — 3 live deployed apps, 9 public repos.
I'm an Integrated M.Sc. Data Science student at Amrita Vishwa Vidyapeetham, Coimbatore (2022–2027). My work sits at the intersection of deep learning, sports analytics, and explainable AI.
I build systems that don't just predict — they explain. From graph-based cricket outcome prediction to RAG-powered UCL match summaries, every project is designed around novelty, rigour, and real-world utility — with 3 live deployed applications to prove it.
My passion for cricket and football drives my research domains, where I apply IoT sensor fusion, Graph Attention Networks, Transformer architectures, LLMs, and SHAP explainability to extract actionable insights from raw data.
Backed by 9 public GitHub repositories, I combine publication-quality research with production-ready deployment using Streamlit and HuggingFace Spaces.
Spatio-temporal graph learning for IPL T20 match outcome prediction. GAT player-interaction graph + BiLSTM + cross-attention Transformer. Ball-by-ball win probability, run forecasting & Player Impact Score.
Multi-task RoBERTa with novel Sentiment Incongruence Auto-Labeler. Dataset-agnostic labeling from semantic mismatch between surface sentiment and underlying emotion. F1-Macro 0.977, AUC 0.997.
Classifies football players into Attacker, Midfielder, Defender from PAMAP2 IoT wearable data. Activity-to-role mapping. M3 TCN+Transformer achieves 99.24% accuracy. LOSO 98.89%±0.42%.
Three-class fatigue prediction from PAMAP2 wearable IoT data. Novel Karvonen heart rate labeling (leakage-free). Personalized Random Forest: 97.87% accuracy, LOSO 97.96%. Coach substitution-alert dashboard.
Factually grounded UEFA Champions League match summaries using RAG + LLaMA 3.1 via Groq API + SHAP. Self-curated UCL-2025 dataset (189 matches, 142 cols). Sentence-BERT + FAISS retrieval: cosine similarity 0.903 vs 0.373 baseline.
Multi-league framework across 5 European leagues. ELO ratings + rolling form + betting market probabilities + H2H stats. 33 features, 3-layer anti-leakage architecture. Gradient Boosting: 0.55 acc, 0.91 xPts MAE. 259 teams available.
AI goalkeeper trained via PPO in a 2D Pygame football simulation. Rule-based threat assessment + shot prediction + state machine (IDLE→TRACKING→READY→DIVING→RECOVERING). Improves with every game.
Agentic AI study assistant for college students. Observe→Think→Act loop using Groq API + LLaMA 3.3-70B. Finds curated resources and generates personalized exam study plans with live progress tracking.
AI-powered web platform for students with ADHD, dyslexia & learning disabilities. T5 PDF summarization, spaCy quiz generation, text-to-speech, multilingual translation (7 Indian languages). Built with Django + PostgreSQL.
Open to research collaborations, internships, and exciting projects in AI, sports analytics, and deep learning. Feel free to reach out!
GitHub Profile →