- Built AI reporting engine → 75% workflow automation
- Threat classification pipeline → 92% accuracy
- Multi-source Airflow ingestion system
- CI/CD automation → 35% detection improvement
Focus: scalable intelligence pipelines.
| Area | Result |
|---|---|
| NLP Mental Health | 88.10% accuracy |
| EEG Neurological AI | 90% classification |
| Optimization | 40% training efficiency gain |
Depression Detection (ICICC 2024, Springer LNNS)
LIWC-based linguistic modeling + large social dataset
EEG Signal Classification — SVNIT
Distributed training for neurological disorder detection
Research direction: brain analytics + applied AI systems
Every project is designed with lifecycle thinking.
- AI Report Generator → automated intelligence reporting
- Depression Detection NLP → mental health classifier
- EEG Neurological Classifier → brain signal ML pipeline
Pinned repos contain architecture breakdowns.
- Coding Club President (50+ engineers)
- International conference speaker
- Environmental initiative lead
- Competitive basketball vice-captain
Systems thinking applies to teams too.
