Problem: International and non-native English speaking students face steep cognitive hurdles during fast-paced academic lectures filled with dense jargon, lack of contextual scaffolding, and absent live feedback loops for educators.

Role: Co-Founder & AI/Backend Lead: Designed the core concept simplification pipelines, prompt orchestration logic, and high-concurrency FastAPI microservice. Built the prototype in a 36-hour sprint at TetraTHON 2026 (6th place / 160 teams) and currently directing the transition to multi-agent architecture.

Problem: Conventional municipal waste bins rely only on fill-level monitoring, ignoring waste segregation, density anomalies, and hazardous decomposition gases like methane and ammonia.

Role: Co-Author & System Architect: Fine-tuned the 4-class ResNet classifier (90 mAP at 1.7s CPU latency), programmed UART communication between Raspberry Pi and Arduino Uno, and designed the classification-aware route optimization algorithm.

Problem: Spatial CNN deepfake classifiers overfit to pixel-level patterns and degrade rapidly when media undergoes lossy social media compression (JPEG recompression, resizing, filtering).

Role: Solo Developer & ML Researcher: Engineered the multi-domain forensic feature extraction pipeline, fine-tuned the Vision Transformer model, built Grad-CAM heatmap hooks, and developed prototype FGSM adversarial immunization scripts.

Problem: Chronic dermatological disorders require continuous tracking over months, yet existing AI diagnostic tools operate as isolated, single-point image classifiers that ignore temporal disease progression and patient clinical histories.

Role: Research Lead: Conceptualized the multi-modal longitudinal decision support framework, defined clinical NLP symptom timeline schemas, and designed the sequential lesion tracking workflow.

Problem: Traditional bag-of-words text classifiers evaluate crisis statements in isolation, ignoring temporal behavioral transitions and contextual relationship chains critical for understanding distress patterns.

Role: Data Specialist Intern (CSRBOX): Cleaned and preprocessed 101,110 textual records, engineered linguistic features, trained Scikit-Learn classification baselines, and modeled entity-relationship schemas in Neo4j.

Problem: Computer vision models trained on clean laboratory datasets experience severe performance degradation when exposed to real-world farm and market conditions with harsh shadows, occlusions, and variable lighting.

Role: Solo Developer & Researcher: Curated and labeled 1,200+ regional crop photos (Karela, Parwal, etc.), implemented OpenCV background segmentation, trained six CNN variants in PyTorch, and built a Flask evaluation UI.

Problem: Logistics managers often rely on static spreadsheets that fail to dynamically synthesize sparse operational metrics into an interpretable risk scorecard.

Role: Solo Hackathon Developer: Built the data cleaning pipeline in Pandas, trained multivariable regression models, and deployed a Streamlit dashboard for dynamic carrier scoring.