Projects
Research-grade systems combining intelligent algorithms with production engineering
Research Agent
Multi-agent system for high-quality automated research with evidence validation
• 6 specialized agents for research workflow
• 95%+ accuracy on fact-checking validated evidence
• Zero hallucinations in Judge-validated outputs
• 100% citation coverage on final reports
• Adaptive research depth based on quality scores
FaceCode
Adaptive coding platform with emotion recognition and physiological confidence metrics
• Real-time emotion recognition at 30 fps
• Keystroke dynamics for cognitive load detection
• Adaptive difficulty with skill rating
• Sub-100ms latency for intervention triggers
• 40% faster problem solving with adaptation
Fourier Neural Network
FNO-inspired architecture for bearing fault detection with ~96% accuracy
• ~96% accuracy on bearing fault classification
• O(N log N) complexity via FFT spectral convolution
• Superior to CNN (~92%) and LSTM (~88%) baselines
• Global receptive field for periodic pattern detection
• High noise robustness through spectral filtering
Graph Neural Networks
GCN/GAT models for capturing cascading failures in industrial systems
• GAT achieves ~7% RMSE vs 15% LSTM baseline
• 21-node sensor graphs with learned correlations
• Attention visualization reveals critical sensors
• Models fault propagation pathways explicitly
• Transferable sensor relationships across engines
Physics-Informed Neural Networks
PINNs for solving engineering PDEs with automatic differentiation
• Learns heat equation solutions with ~1e-4 MSE
• PDE residual < 1e-3 across domain
• Generalizes to unseen thermal diffusivity values
• Exact derivative computation via autograd
• Interactive dashboard with 3D surface visualization
Research Mind
RAG-powered research assistant for multi-paper comparison and semantic analysis
• Indexed thousands of research paper chunks
• Hybrid BM25 + semantic search for retrieval quality
• Reduced hallucinations using evidence-grounded generation
• Citation support for all answers with paper references
• Multi-paper comparison and synthesis
Medic AI
Explainable AI assistant for medical report analysis and disease prediction
• High prediction accuracy on medical datasets
• Explainable outputs with reasoning chains
• Real-time inference capability for clinical use
• NLP-powered symptom analysis and extraction
• Privacy-compliant architecture
Elo Learn
Adaptive learning platform with knowledge tracing and personalized recommendations
• Personalized learning recommendations based on knowledge state
• Adaptive difficulty adjustment for optimal challenge level
• Explainable learning paths using knowledge graphs
• Student performance prediction with high accuracy
• Instructor analytics for class-wide insights
Research Agent
Multi-agent AI research assistant with autonomous evidence collection, quality validation, and citation-backed reports.
Problem
Methodology
Multi-stage pipeline: Planning → Search → Quality Scoring → Synthesis → Citation. Judge agent filters low-quality sources before synthesis.
Results
Produces publication-ready reports with verified citations. 70% reduction in manual research time. Enables rapid knowledge synthesis across domains.
Architecture
FastAPI backend with LLM orchestration, React frontend with real-time WebSocket updates, SQLite persistence
Tech Stack
Key Metrics
- ✓6 specialized agents for research workflow
- ✓95%+ accuracy on fact-checking validated evidence
- ✓Zero hallucinations in Judge-validated outputs
Future Work
Multi-modal research, collaborative spaces, automatic literature review generation
FaceCode
Adaptive AI coding platform with real-time emotion recognition, keystroke analysis, and intelligent difficulty adjustment.
Problem
Methodology
Multi-modal telemetry fusion: emotion recognition + head-pose tracking + keystroke dynamics. Skill rating from solve times, errors, and emotional distress.
Results
35% increase in student confidence. 90% of interventions rated as helpful. Beautiful glassmorphism UI with real-time analytics dashboard.
Architecture
React + Monaco Editor frontend, FastAPI backend with DeepFace + MediaPipe, Groq API for hints
Tech Stack
Key Metrics
- ✓Real-time emotion recognition at 30 fps
- ✓Keystroke dynamics for cognitive load detection
- ✓Adaptive difficulty with skill rating
Future Work
Multi-language support, peer learning features, ML-powered progress tracking
Fourier Neural Network
Spectral learning for predictive maintenance using frequency-domain neural operators on bearing vibration data.
Problem
Methodology
Spectral convolution using learnable complex weights on k_max Fourier modes. Directly captures fault-specific frequencies (outer race ~107 Hz). Butterworth filtering + windowed segmentation.
Results
96% accuracy, faster training (45s vs 60s CNN), fewer parameters (85K). Excellent noise robustness. 7 research notebooks + interactive dashboard.
Architecture
PyTorch with custom SpectralConv1d layers, FFT/IFFT operations, Streamlit dashboard for real-time predictions
Tech Stack
Key Metrics
- ✓~96% accuracy on bearing fault classification
- ✓O(N log N) complexity via FFT spectral convolution
- ✓Superior to CNN (~92%) and LSTM (~88%) baselines
Future Work
Multi-scale spectral analysis, dynamic mode selection, transfer learning across bearing types
Graph Neural Networks
Remaining Useful Life prediction via sensor relationship modeling on NASA CMAPSS turbofan engines.
Problem
Methodology
Correlation-thresholded adjacency matrices. Node features from time-windowed sensor values. Attention weights show critical sensors during degradation.
Results
Superior RUL prediction accuracy. GAT attention reveals High-Pressure Compressor outlet temp + bypass pressure as critical indicators. Published research-quality findings.
Architecture
PyTorch Geometric with GCN, GAT, MPNN layers. NetworkX for graph construction. Streamlit dashboard for RUL visualization.
Tech Stack
Key Metrics
- ✓GAT achieves ~7% RMSE vs 15% LSTM baseline
- ✓21-node sensor graphs with learned correlations
- ✓Attention visualization reveals critical sensors
Future Work
Dynamic edge generation, spatio-temporal graph convolutions, Bayesian uncertainty quantification
Physics-Informed Neural Networks
Learning PDE solutions by embedding differential equations as loss constraints in neural networks.
Problem
Methodology
Automatic differentiation computes exact ∂u/∂t and ∂²u/∂x². Network learns to satisfy heat equation: ∂u/∂t = α·∂²u/∂x². Verified against analytical Fourier series solutions.
Results
Learns smooth solutions to heat equation. Maintains physical validity (no impossible temperatures). Generalizes beyond training domain unlike pure data-driven models.
Architecture
Feed-forward network with Tanh activations, PyTorch autograd for derivative computation, Streamlit for real-time parameter tuning
Tech Stack
Key Metrics
- ✓Learns heat equation solutions with ~1e-4 MSE
- ✓PDE residual < 1e-3 across domain
- ✓Generalizes to unseen thermal diffusivity values
Future Work
Wave equation, Burgers equation, Navier-Stokes, inverse problem solving
Research Mind
AI-powered Research Assistant built using Retrieval-Augmented Generation. Compare papers, extract insights, and get evidence-grounded answers with multi-paper analysis.
Problem
Researchers struggle to extract insights from large collections of papers. Manual review is time-consuming, and existing search tools often miss relevant connections or produce hallucinated insights.
Methodology
Hybrid retrieval combining lexical (BM25) and semantic (embedding-based) search. Evidence-grounded generation with citation tracking.
Results
Successfully indexed and queried thousands of research paper chunks with improved recall through hybrid search. Significantly reduced hallucinations compared to baseline LLM approaches.
Architecture
FastAPI backend with FAISS vector search, React frontend with real-time streaming responses
Tech Stack
Key Metrics
- ✓Indexed thousands of research paper chunks
- ✓Hybrid BM25 + semantic search for retrieval quality
- ✓Reduced hallucinations using evidence-grounded generation
Future Work
Multi-modal paper support, collaborative research spaces, automatic literature review generation
Medic AI
Explainable AI-powered Medical Intelligence Assistant. Analyzes medical reports, predicts diseases, and provides clinical recommendations with transparent reasoning.
Problem
Healthcare professionals need AI systems they can trust. Black-box predictions are unsuitable for medical domains where explainability is critical for clinical adoption and liability.
Methodology
Transformer-based architecture for NLP task. Explainability through attention visualization and reasoning chains. Trained on medical datasets with privacy preservation.
Results
High accuracy on disease prediction tasks. Explainable predictions enable clinical review and trust. Real-time inference suitable for clinical workflows.
Architecture
PyTorch model with attention mechanisms for interpretability, FastAPI backend for REST API, FastText embeddings for medical NLP
Tech Stack
Key Metrics
- ✓High prediction accuracy on medical datasets
- ✓Explainable outputs with reasoning chains
- ✓Real-time inference capability for clinical use
Future Work
Multi-lingual support, integration with EHR systems, uncertainty quantification
Elo Learn
Research-grade Adaptive Learning Platform with Knowledge Tracing. Personalized learning paths, intelligent recommendations, and student performance prediction.
Problem
Educational institutions lack personalized learning systems. One-size-fits-all curricula fail to adapt to individual student needs, leading to disengagement and suboptimal learning outcomes.
Methodology
Knowledge tracing using Bayesian Networks. Recommendation system combining collaborative filtering with item-based KT. Spaced repetition scheduling.
Results
Improved student learning outcomes through personalized recommendations. Accurate performance prediction enabling proactive intervention. Explainable learning paths building student confidence.
Architecture
Python backend with NetworkX for knowledge graphs, Streamlit dashboard for student and instructor interfaces, SQLite for persistence
Tech Stack
Key Metrics
- ✓Personalized learning recommendations based on knowledge state
- ✓Adaptive difficulty adjustment for optimal challenge level
- ✓Explainable learning paths using knowledge graphs
Future Work
Integration with learning management systems, advanced reinforcement learning for path optimization, gamification elements