Projects

Featured Projects

Scientific Literature RAG System

AI-powered introduction generator for scientific papers

Live demonstration of the Scientific Literature RAG system generating introductions with semantic search and citation extraction.

A production-ready RAG system that automatically generates well-structured, literature-informed introductions for scientific papers. The system indexes research papers and leverages LLMs to synthesize relevant literature into comprehensive introductions with properly formatted citations.

Key Features:

• Multi-Provider LLM Support: Claude (Anthropic), OpenAI GPT-4/GPT-4o, Google Gemini
• SPECTER2 Embeddings: Domain-optimized scientific embeddings for superior semantic understanding
• GPU Acceleration: Metal GPU support for Apple Silicon, CUDA for NVIDIA, automatic CPU fallback
• Smart PDF Processing: Semantic chunking (500-word chunks with 50-word overlap)
• ChromaDB Vector Search: Persistent local storage with cosine similarity search
• Automatic BibTeX Citations: Extracts and formats references with sophisticated metadata extraction
• Interactive Dash UI: User-friendly web interface for literature exploration
• ROUGE Evaluation: Built-in evaluation metrics for assessing generation quality (F1 scores >0.5)

Performance:

• Embedding: 50-100 chunks/second on Apple Silicon
• Generation: 10-30 seconds per introduction
• Indexing: ~1 minute per 100 PDFs

Technologies: Python 3.11+, GPU, LLM, ChromaDB, SPECTER2, RAG, Dash
Status: Production Ready | Apache 2.0 License
→ View on GitHub

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Speech-to-Text with Sentiment Analysis and Translation

Real-time multilingual processing pipeline

Detailed architecture of the Transformer model showing encoder-decoder structure with multi-head attention mechanisms.

A comprehensive end-to-end system integrating speech recognition, sentiment analysis, and neural machine translation. Built with from-scratch Transformer implementation demonstrating deep understanding of attention mechanisms and encoder-decoder architectures.

Key Features:

• Real-time Speech-to-Text: Audio capture and transcription using Vosk library (English)
• From-Scratch Transformer: Complete encoder-decoder architecture without pre-trained models
• Custom Multi-Head Attention: Manually implemented attention mechanisms with configurable heads
• Positional Encoding: Hand-crafted sinusoidal position embeddings
• Sentiment Classification: Bidirectional LSTM with 95% test accuracy
• Interactive Dash Web App: Real-time processing with visual feedback and export functionality
• Hyperparameter Optimization: Automated tuning with Optuna for both models
• BLEU Score Evaluation: Translation quality metrics and model assessment

Performance Metrics:

• Sentiment Analysis (BiLSTM): 95.00% test accuracy
• Translation (Transformer): 67.26% test accuracy, 0.52 BLEU score
• Real-time processing with instant speech recognition and translation

Architecture Highlights:

• Educational implementation demonstrating core concepts
• Full control over attention mechanisms and positional encodings
• Research-grade code suitable for experimentation
• Clean, well-documented implementation

Technologies: Python 3.11+, Transformer, Bidirection LSTM, TensorFlow, Keras, Vosk, Dash, Optuna
Status: Research/Educational | Apache 2.0 License
→ View on GitHub

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Active Particles in 3D Confinement

GPU-accelerated molecular dynamics simulation

Performance comparison across CPU and GPU implementations showing up to 27× speedup on Apple Silicon.

Simulation of 1000 active Brownian particles under 3D cylindrical confinement, demonstrating collective motion and particle-particle interactions with Lennard-Jones repulsion.

A high-performance C++ simulation framework for modeling active Brownian particles (ABPs) under cylindrical confinement in 3D space. Implements Euler-Maruyama algorithm for Langevin dynamics with dual CPU/GPU implementations.

Key Features:

• Dual Implementations: CPU-only (OpenMP) and GPU hybrid (Metal) versions
• GPU Acceleration: Up to 27× speedup on Apple Silicon for large systems (N ≥ 1000)
• Langevin Dynamics: Proper stochastic noise generation with Euler-Maruyama integration
• 3D Cylindrical Confinement: Reflective boundary conditions for realistic physical constraints
• Lennard-Jones Interactions: Repulsive particle-particle forces with configurable strength
• OpenMP Parallelization: Multi-threaded CPU optimization for all particle counts
• Automatic CPU/GPU Selection: Smart threshold-based switching for optimal performance
• Numerical Stability: Robust safeguards prevent NaN overflow at high particle densities

Performance Benchmarks (Apple M2, 1000 timesteps):

Particles	CPU (1 thread)	CPU (OpenMP 6)	GPU (Metal)	GPU vs 1 CPU	GPU vs OpenMP
100	0.026s	0.103s	0.096s	0.3×	0.9×
200	0.075s	0.125s	0.115s	0.7×	1.1×
500	0.401s	0.269s	0.225s	1.8×	1.2×
1,000	1.514s	0.747s	0.445s	3.4×	1.7×
2,000	5.638s	2.389s	0.601s	9.4×	4.0×
5,000	34.393s	13.903s	1.262s	27.3×	11.0×

Physical Model:

• Active Brownian particles with self-propulsion
• Translational and rotational diffusion
• Repulsive Lennard-Jones potential
• Applications: bacterial suspensions, cell motility, colloidal systems

Technologies: C++17, OpenMP, Metal GPU Status: Research-Grade | Apache 2.0 License
→ View on GitHub

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Personality Classification with Ensemble Learning

Production ML pipeline achieving Kaggle top 5%

Interactive dashboard for real-time personality classification with model explainability and performance metrics.

A production-ready machine learning pipeline for personality classification using ensemble learning, achieving top 5% (200/4,329) in the Kaggle Personality Classification Competition. Features modular architecture, automated hyperparameter optimization, and interactive visualization.

Key Features:

• Six Ensemble Stacks: Complementary ML algorithms (Random Forest, XGBoost, LightGBM, CatBoost, Neural Networks)
• Advanced Data Augmentation: SDV Copula-based synthetic data generation with quality control
• Automated Hyperparameter Optimization: Optuna-powered tuning for each stack (200+ trials for blending)
• Interactive Dash Dashboard: Real-time predictions, model explainability, and performance visualization
• Meta-Learning Approach: Logistic Regression ensemble combiner with optimized blending weights
• Out-of-Fold Predictions: Unbiased ensemble training with stratified cross-validation
• Production-Ready: Full test coverage, CI/CD pipeline, comprehensive logging

Ensemble Architecture:

Stack	Focus	Algorithms	Special Features
A	Traditional ML (Narrow)	RF, LR, XGBoost, LightGBM, CatBoost	Stable baseline
B	Traditional ML (Wide)	Same as A	Extended search space
C	Gradient Boosting	XGBoost, CatBoost	Tree specialists
D	Sklearn Ensemble	Extra Trees, Hist GB, SVM, Gaussian NB	Diverse mix
E	Neural Networks	MLPClassifier, Deep architectures	Non-linear patterns
F	Noise-Robust	Same as A + label noise	Improved generalization

Competition Results:

• Rank: 200/4,329 (Top 5%)
• Dataset: ~18,000+ training samples (with augmentation), 8 personality dimensions
• Cross-Validation: Stratified 5-fold CV for robust evaluation

Technologies: Python 3.11+, scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, Dash, Plotly Status: Production Ready | Apache 2.0 License
→ View on GitHub

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Open Source Contributions

All public projects available on GitHub