About Me

Hello! I am a Computer Science graduate from the University of Southern California.

I am passionate about leveraging data-driven solutions to solve complex problems and drive innovation. With a solid foundation in statistical models, deep learning, and hands-on projects experience, I excel at transforming raw data into actionable insights. Let's connect and explore how we can harness the power of data to create impactful change!

What I'm Doing

Data Science

2-4 years of experience in Data Science, focusing on predictive modeling, statistical analysis, and machine learning

Machine Learning Engineering

2-4 years of experience in MLE, working on deploying and optimizing machine learning models

Data Engineering

0-2 years of experience in Data Engineering, building and maintaining data pipelines and ETL processes

Data Analysis

0-2 years of experience in Data Analysis, providing actionable insights through data visualization and reporting

Domain Experience

Healthcare

Experience in leveraging data for healthcare analytics and predictive modeling to improve patient outcomes.

Automobile

Worked on projects involving vehicle data analysis, predictive maintenance, and automation techniques.

Education

Developed educational tools and analytics platforms to enhance learning experiences and outcomes.

Agriculture

Implemented data-driven solutions for crop prediction, soil health analysis, and precision farming.

Education

  1. University of Southern California

    Jan 2023 — Dec 2024

    Master of Science, Computer Science

    Analysis of Algorithms, Database Systems, Machine Learning for Data Science, Natural Language Processing, Deep Learning, Information Retrieval and Web Search Engines

  2. KLS Gogte Institute of Technology

    Aug 2018 — Jul 2022

    Bachelor of Engineering, Computer Science

    Statistical-Numerical-Fourier Techniques, ML & AI, Artificial Neural Networks, Embedded Systems and IoT, Advanced Algorithms, Software Engineering, Database Management, Computer Networks, UNIX System programming, Formal Language and Automata Theory

Experience

  1. USC Keck School of Medicine (Los Angeles, CA)

    Apr 2023 — Dec 2024

    Data Science & Analytics Intern

    • > Led the conversion of QlikSense dashboards to Tableau, significantly boosting analytics and data processing speed
    • > Implemented and automated an email subscription system via Tableau, providing regular performance reports to healthcare providers and enhancing communication
    • > Validated data ingestion across platforms (HeA, AWS Redshift, Snowflake) using SQL, ensuring high dashboard accuracy and reducing discrepancies
    • > Processed and analyzed extensive patient records of EHR data to build ML models predicting Length of Stay (LOS) and mortality rates
    • > Enhanced ICU predictive analytics using NLP for care escalation forecasts
    • > Created one-pagers for dashboards and documentation for new orients on the department's Data Portal
    • > Presented these initiatives to the Executive Leadership Team, showcasing improvements in data accuracy, processing speed, and operational efficiency

  2. Mercedes Benz Research and Development India Pvt. Ltd. (Bangalore, India)

    Jul 2022 — Dec 2022

    Data Engineer

    • > Optimized the existing ETL by performing data analysis, using PySpark and SQL, significantly improving efficiency and time
    • > Developed an ML pipeline with high classification accuracy for patent classification, automating the patent classification process using LLMs
    • > Conducted a signal processing session for professionals, discussing how signal processing in ML can be applied in the automotive domain and addressing its nuances
    • > Participated in mini projects, such as predicting the employee number for a particular day to minimize food wastage

  3. EYESEC Cyber Security Solutions Pvt. Ltd. (Belagavi, India)

    Dec 2021 — Jul 2022

    Machine Learning Research Intern

    • > Devised an innovative ML pipeline for industry-aligned university syllabi generation using web-scraped job descriptions, significantly improving relevancy
    • > Analyzed numerous universities, aligning syllabi with market trends to ensure graduates meet industry demands
    • > Constructed an integrated ML and Deep Learning course with projects and internships, substantially raising job opportunities

  4. Cognius.ai Pte. Ltd. (Remote)

    Sep 2021 — May 2022

    Machine Learning Research Intern

    • > Spearheaded NLG research, refining conversational AI capabilities
    • > Achieved optimal NLG module performance, significantly reducing response time and increasing accuracy
    • > Conducted exhaustive NLU literature review, advancing AI comprehension
    • > Empowered conversational AI with diverse datasets, effectively handling grammatical complexities

  5. Smart Sampark Pvt. Ltd. (Belagavi, India)

    Aug 2021 — Mar 2022

    Machine Learning Research Intern

    • > Piloted an extensive Deep Learning model study, evaluating multiple models, and revolutionized an agricultural chatbot with advanced insights
    • > Corroborated the Seq2Seq Deep Learning model's potential through meticulous testing, achieving a high success rate
    • > Pioneered state-of-the-art Transformer architectures, reducing response time and boosting NLU
    • > Constructed a granular dataset with a million data points for localized agricultural insights
    • > Integrated UX design principles to enhance user interaction with the agricultural chatbot

Projects

Artificial Intelligence

Dunes of Influence : Quantitative Prediction of Influencers

This project builds a deep learning framework to predict engagement metrics—likes, comments, retweets—for new tweets. Using data from platforms like Twitter and Reddit, it applies advanced NLP and multi-modal analysis to forecast content performance.

Key Features
  • Twitter: Collected 4,000 original tweets from Elon Musk, excluding replies.
  • Reddit: Curated posts from top 5 subreddits (e.g., r/ElonMusk, r/Tesla) linking Reddit discussions to tweets using a BERT-based relevance model within a 48-hour window.
  • Semantic Embeddings: Used mxbai-embed-large-v1 to convert tweet text into 1024-dimensional semantic vectors.
  • Sentiment Analysis:
    • Tweets: twitter-roberta-base-sentiment-latest
    • Reddit: distilbert-base-uncased-emotion (6 emotion categories)
  • Sector Classification: Classified tweets into 18 topic sectors using tweet-topic-latest-multi.
  • Calculated positive/negative sentiment scores of Reddit comments to derive an overall sentiment bias for each post linked to a tweet.
  • Compression Layer: Reduces high-dimensional input vectors for more efficient learning.
  • Positional Encoding: Adds temporal context to sequence data.
  • Transformer Encoder: Captures complex dependencies across semantic, sentiment, and sector features.
  • Output Layer: Predicts engagement metrics (likes, comments, retweets) using a fully connected layer and activation function.
  • Compared performance across:
    • MLP (baseline)
    • LSTM
    • Transformer (with and without compression)
  • Transformer outperformed LSTM and MLP on all metrics.
  • Loss Functions Evaluated: MAE, MSE, Huber, Quantile
  • Best Performing: MAE and Quantile loss for skewed data.
  • Scaling Techniques:
    • Logarithmic Scaling: Improved validation performance, reduced overfitting.
    • Robust Scaling: Effective for training but prone to overfitting on validation.
  • Forecasts 3 key user engagement metrics:
    • Comments
    • Likes
    • Retweets
  • Included graphs for training/validation error convergence.
  • Tables comparing metrics across models and loss functions.
Tools & Technologies
Python Pandas Scikit-learn Twitter Reddit Baseline PyTorch TensorFlow
Workflow
Workflow
Output / Result
Model Comparison Loss Comparison

Urban Development

Spatiotemporal Data Scraping & Deep Learning Forecasting System
This project automates the extraction of temporal web-based data and leverages deep learning to model patterns in the visual data stream.

Spatiotemporal Data Scraping & Deep Learning Forecasting System
This project automates the extraction of temporal web-based data and leverages deep learning to model patterns in the visual data stream.

Key Features
  • Dynamic Data Extraction: Uses JavaScript injection (webScript.txt) via Selenium to pull structured data from dynamically rendered web elements.
  • Screenshot Capturing: Captures timestamped visual records of web content for downstream image-based analysis.
  • Multi-Timestamp Scraping: scrapRunner.py handles batch scraping for different dates using historical references (data.txt).
  • URL Mapping Support: Uses loaction.txt to define location-specific scraping targets.
  • Historical Anchoring: Aligns current data snapshots with previously recorded entries for temporal modeling.
  • Sequential Image Modeling: Learns from ordered image sequences using a CNN-LSTM architecture in mainScript.py.
  • Visual Pattern Forecasting: Predicts future states or trends based on temporal visual data inputs.
  • WandB Integration: trainingPipeline.py tracks training metrics, visualizes learning curves, and supports hyperparameter tuning.
  • Reproducible Training Runs: Standardized pipelines ensure consistent model behavior and traceability.
Tools & Technologies
Python Selenium JavaScript (webScript.txt) Chromedriver / WebDriver Pillow / OpenCV (optional) Pandas JSON / TXT (data.txt, loaction.txt) TensorFlow / Keras NumPy CNN + LSTM Weights & Biases (wandb) Jupyter / VS Code Git Python Virtualenv / conda (optional)
Workflow
Data Acquisition
1
Automated Scraping: Use Selenium to download satellite imagery from ArcGIS Wayback.
↓
2
Metadata Extraction: Collect and store timestamps, coordinates, and source info for each image.
↓
Data Preparation
3
Image Preprocessing: Standardize format, resize, crop, and normalize images.
↓
4
Temporal Sequencing: Organize images into chronological sequences for each location.
↓
Dataset Construction
5
Sequence Loader: Build input/target pairs and split into train/val/test sets.
↓
6
Augmentation: Apply random rotations, flips, and brightness changes.
↓
Model Development
7
Architecture Design: Implement a hybrid CNN + ConvLSTM2D model with regularization.
↓
8
Loss Functions: Combine MSE with perceptual loss for sharper predictions.
↓
Training & Monitoring
9
Model Training: Train with early stopping and learning rate scheduling.
↓
10
Experiment Tracking: Log metrics, loss curves, and checkpoints with Weights & Biases.
↓
Evaluation
11
Performance Assessment: Evaluate using MAE, PSNR, SSIM, and visualize predictions.
↓
Inference & Output
12
Batch Inference: Generate future frames for new sequences using the trained model.
↓
13
Result Packaging: Compile generated frames into GIFs and store with metadata.
Output / Result
Output 1 Output 2

NutriGenie: AI Recipe Recommender

Veg Fitness Recipe Recommender, an AI-powered system that provides personalized meal recommendations to help you meet your fitness and nutrition goals. It combines user profile analysis, nutritional calculations, dietary/allergen filtering, and advanced recipe generation using open-source large language models (LLMs).

Veg Fitness Recipe Recommender, an AI-powered system that provides personalized meal recommendations to help you meet your fitness and nutrition goals. It combines user profile analysis, nutritional calculations, dietary/allergen filtering, and advanced recipe generation using open-source large language models (LLMs).

Key Features
  • BMI & TDEE Calculation: Automatic BMI computation based on height and weight inputs. TDEE calculation uses Mifflin-St Jeor equation for accurate daily calorie needs. Activity level support (1.2 - 1.9 multipliers). Fitness goal targeting (deficit, maintenance, bulking).
  • Macronutrient Optimization: Macro splits based on fitness goals:
    • Deficit: 40% protein, 40% carbs, 20% fat
    • Maintenance: 30% protein, 45% carbs, 25% fat
    • Bulking: 30% protein, 50% carbs, 20% fat
  • Fiber Recommendations: Smart fiber calculation (14g per 1000 calories) and dietary compliance for health.
  • Supported Diet Types: High-Protein Vegetarian, High-Protein Vegan, Low-Carb Vegetarian, Keto Vegetarian, Athlete/Bodybuilder Plant-Based, Whole-Food Plant-Based (WFPB), Fruitarian.
  • Allergen & Restriction Support: Gluten, dairy, nuts, soy, eggs, shellfish, fish. Dietary restrictions: Lacto-vegetarian, ovo-vegetarian, lacto-ovo vegetarian. Automatic filtering for real-time allergen detection and avoidance.
  • Mistral-7B-Instruct Integration: Advanced LLM for nutritional targeting, ingredient intelligence, and recipe variations.
  • Recipe Analysis Features: Nutritional analysis, improvement suggestions, and macro optimization.
  • TheMealDB Integration: 500+ categorized recipes with rich metadata and images.
  • ChromaDB Vector Search: Semantic search, embedding storage, intelligent caching, and real-time updates.
  • USDA FoodData Central Integration: Comprehensive nutritional database, per-100g values, and real-time ingredient lookup.
  • Unit Conversion System: Supports weight, volume, common, and size conversions (g, kg, mg, lb, oz, cup, tbsp, tsp, pinch, clove, slice, piece, large, medium, small).
Tools & Technologies
FastAPI Uvicorn Pydantic SQLAlchemy Alembic PostgreSQL ChromaDB Transformers (Hugging Face) PyTorch Mistral-7B-Instruct SentencePiece Einops Scikit-learn Protobuf Streamlit Custom CSS Interactive Components Pandas NumPy Requests TheMealDB API USDA FoodData Central API Hugging Face Model Hub Python 3.x Virtual Environment Environment Variables Git Docker Support Requirements.txt
Workflow
User Profile Input
1
User enters fitness goals, dietary preferences, and restrictions.
↓
Nutrition Calculation
2
System computes BMI, TDEE, calorie/macro targets, and fiber needs.
↓
Dietary Filtering
3
Filters out recipes/ingredients based on diet type and allergens.
↓
Recipe Search & Retrieval
4
Searches TheMealDB for matching recipes and uses ChromaDB for semantic, vector-based search.
↓
AI Recipe Generation
5
If no suitable recipe is found, Mistral-7B-Instruct generates a new recipe matching nutrition targets.
↓
Nutritional Analysis
6
Estimates nutrition for each recipe using USDA FoodData Central API and performs unit conversions as needed.
↓
Recommendation Delivery
7
Presents interactive recipe cards in the Streamlit frontend with step-by-step instructions and nutrition breakdown.
User Profile Input
1
User enters fitness goals, dietary preferences, and restrictions.
↓
Nutrition Calculation
2
System computes BMI, TDEE, calorie/macro targets, and fiber needs.
↓
Dietary Filtering
3
Filters out recipes/ingredients based on diet type and allergens.
↓
Recipe Search & Retrieval
4
Searches TheMealDB for matching recipes and uses ChromaDB for semantic, vector-based search.
↓
AI Recipe Generation
5
If no suitable recipe is found, Mistral-7B-Instruct generates a new recipe matching nutrition targets.
↓
Nutritional Analysis
6
Estimates nutrition for each recipe using USDA FoodData Central API and performs unit conversions as needed.
↓
Recommendation Delivery
7
Presents interactive recipe cards in the Streamlit frontend with step-by-step instructions and nutrition breakdown.

CiteMind: Agentic Research Engine

Research Paper Search and Analysis Tool (v1.2.0) - A modern, AI-powered app to search, filter, and analyze research papers with advanced vector search, metadata filters, and evaluation metrics.

Research Paper Search and Analysis Tool (v1.2.0) - A modern, AI-powered app to search, filter, and analyze research papers with advanced vector search, metadata filters, and evaluation metrics.

Key Features
  • Vector Database Options: Chroma, Pinecone, Weaviate, Milvus, Qdrant, FAISS, Elasticsearch, Redis, PostgreSQL, MongoDB
  • Embedding Model Options: Multiple HuggingFace models (all-MiniLM-L6-v2, BGE-Small, MPNet, E5, GTE), OpenAI embeddings, Cohere, Jina, Mistral
  • LLM Provider Options: Various HuggingFace models (Mistral-7B, Llama-2 variants, Mixtral), OpenAI GPT models, Anthropic Claude, Cohere Command
  • Semantic Search: Vector-based similarity search using embeddings
  • Metadata Filters: Publication year range, research category filtering (50+ categories from arXiv), similarity threshold control, number of results control
  • ArXiv Integration: Direct search through arXiv API with category mapping
  • Intelligent Chunking: Text is split into semantic chunks with overlap for better context
  • Metadata Enrichment: Each chunk includes paper metadata (title, authors, date, category, etc.)
  • Vector Embeddings: Automatic embedding generation using sentence transformers
  • ChromaDB Storage: Persistent vector storage with optimized HNSW indexing
  • Paper Summarization: Automatic generation of summaries, key findings, and methodology
  • Question Answering: RAG-based responses using retrieved paper context
  • Similar Paper Discovery: Find related papers based on semantic similarity
  • Search Quality Metrics: Precision, Recall, F1 Score calculation
  • Per-Query Metrics: Relevance scores, response times, chunk counts
  • Search History: Track previous queries and their performance
  • Real-time Evaluation: Automatic evaluation of search results
  • Research Agent: AI agent with tools for searching and fetching papers
  • Workflow Management: StateGraph for complex research workflows
  • Tool Integration: Structured processing of research queries
Tools & Technologies
FastAPI Streamlit ChromaDB HuggingFace Transformers Sentence Transformers OpenAI API Anthropic Claude Cohere Langchain Langraph PostgreSQL Pinecone Weaviate Milvus Qdrant FAISS Elasticsearch Redis MongoDB ArXiv API Docker REST API Vector Search HNSW Indexing RAG Multi-Agent Systems
Workflow
Paper Search Workflow
1
User enters research query and system generates query embedding
↓
2
Vector search finds similar papers in ChromaDB and filters by metadata
↓
3
Papers ranked by similarity score and results displayed with evaluation metrics
↓
Paper Analysis Workflow
4
User selects paper and system retrieves content and metadata
↓
5
LLM generates summary, key findings, and methodology
↓
6
Analysis stored with embeddings and results presented in structured format
↓
Q&A Workflow
7
User asks question and system searches for relevant papers
↓
8
Context extracted from top papers and LLM generates answer using retrieved context
↓
9
Answer provided with citations to source papers

Morning Update System

An intelligent system that generates personalized morning updates using AI to analyze and prioritize information from various sources including weather, stocks, news, and sports.

Key Features
  • Smart Priority Analysis: Uses AI to analyze and prioritize information from different categories (weather, stocks, news, sports)
  • Personalized Reports: Generates customized morning reports based on user preferences and location
  • Adaptive Learning: The AI agent learns from interactions to improve future updates
  • Intelligent Content Ordering: Automatically determines the most important information to display first
  • Weather Data: Real-time weather information using Open-Meteo API
  • Stock Market Data: Real-time stock prices and market movements using yfinance
  • News Headlines: Latest news from NewsAPI
  • Sports Updates: Game results and upcoming matches (currently using mock data)
  • Direct Delivery: Sends updates directly to your Telegram chat
  • Formatted Messages: Rich text formatting with HTML support
  • Chunked Messages: Automatically splits long updates to comply with Telegram's character limits
  • Error Handling: Graceful fallback when delivery fails
  • Daily Updates: Scheduled to run automatically at 7:00 AM
  • Windows Notifications: Desktop notifications when updates are ready
  • Background Processing: Runs continuously in the background
  • Logging: Comprehensive logging of all operations
Tools & Technologies
OpenAI GPT Models Facebook OPT-350M Transformers Library PyTorch Open-Meteo API NewsAPI yfinance Telegram Bot API requests pyyaml python-dotenv schedule win10toast python-telegram-bot JSON YAML Context Management Agent System Context Manager Data Fetcher Report Generator Telegram Bot Scheduler Priority Decider
Workflow
Data Collection & Analysis
1
System fetches data from various sources (weather, stocks, news, sports)
↓
2
AI analyzes each category for importance and relevance
↓
3
Categories are assigned priorities (1-5, where 1 is highest)
↓
Report Generation & Delivery
4
A personalized report is generated with sections ordered by priority
↓
5
The report is sent to your Telegram chat with rich formatting
↓
6
The system learns from interactions to improve future updates
Data Collection & Analysis
1
System fetches data from various sources (weather, stocks, news, sports)
↓
2
AI analyzes each category for importance and relevance
↓
3
Categories are assigned priorities (1-5, where 1 is highest)
↓
Report Generation & Delivery
4
A personalized report is generated with sections ordered by priority
↓
5
The report is sent to your Telegram chat with rich formatting
↓
6
The system learns from interactions to improve future updates

Edge-to-Cloud Wildfire Detection System

This project implements a real-time wildfire detection pipeline optimized for edge-to-cloud deployment, leveraging Sentinel-2 satellite imagery from the CEMS Wildfire Dataset. The system uses a custom U-Net model for semantic segmentation, trained and evaluated on preprocessed satellite images. The pipeline is designed for scalable inference, distributed training, ONNX Runtime deployment, and performance profiling, with simulation of edge-based image streaming to evaluate latency and memory usage.

Key Features
  • Data Source: Uses open-source Sentinel-2 satellite imagery from the CEMS Wildfire Dataset.
  • Preprocessing Pipeline: Converts raw GeoTIFF data into normalized, resized NumPy arrays with binary masks. Reads multi-band satellite images using rasterio. Resizes and normalizes images with opencv-python and numpy. Splits data into train/validation/test sets using scikit-learn. Stores processed data as .npy files for efficient loading.
  • Script: data/preprocess.py — Handles all preprocessing steps, including directory management and mask binarization.
  • Architecture: Implements a U-Net model in models/unet.py for pixel-level wildfire region detection, with encoder, bottleneck, decoder, and skip connections.
  • Framework: Built with torch and torch.nn modules.
  • Distributed Data Parallel (DDP): Utilizes PyTorch's DDP for multi-GPU training, enabling scalable model training across multiple devices. Data loading is distributed using torch.utils.data.DistributedSampler.
  • Loss & Optimization: Uses binary cross-entropy loss (torch.nn.BCELoss) and Adam optimizer.
  • Script: models/train_ddp.py
  • ONNX Export: Converts trained PyTorch models to ONNX format for framework-agnostic, accelerated inference. Export script: models/export_onnx.py.
  • ONNX Runtime Inference: Runs inference using ONNX models with onnxruntime for high-throughput, low-latency deployment. Supports hardware-accelerated backends (CPU, CUDA, TensorRT).
  • Input Preprocessing: Images are resized, normalized, and converted to the appropriate tensor format before inference.
  • Profiling Utilities: utils/profiler.py provides function-level profiling using Python's cProfile and pstats, and deep profiling of PyTorch models using torch.profiler.
  • Benchmarking: Logs inference latency and memory usage per frame, with summary metrics. Benchmarks across multiple input resolutions and hardware targets. Structured .txt reports for reproducibility and system-level evaluation.
  • Stream Simulator: Simulates real-time image streaming from edge devices, enabling frame-by-frame inference and system evaluation. Evaluates trade-offs in latency and memory usage for different resolutions and hardware.
  • Edge Logs: Inference logs in edge/ directory document performance across various hardware and input sizes.
  • Streamlit Dashboard: Interactive web dashboard for monitoring, visualization, and demonstration. Built with streamlit, matplotlib, and pillow. Enables real-time display of inference results, performance metrics, and system status.
Performance Highlights
  • Achieved a 3.2× speedup in inference throughput (from 28.01 ms/frame on CPU to 8.74 ms/frame on GPU TensorRT, and 5.10 ms/frame with TensorRT FP16 at 128×128 resolution).
  • Benchmarked across multiple input resolutions (64×64 to 256×256) and hardware targets (CPU, CUDA, TensorRT).
  • Reduced memory bottlenecks by up to 42% via optimized operator execution, resolution scaling, and frame-wise stream processing.
  • Logged frame-by-frame inference latency and memory usage to structured .txt reports for system-level evaluation and reproducibility.
Workflow
Workflow
Output / Result

Achieved 5.10 ms/frame with TensorRT FP16 at 128×128 resolution. See Streamlit dashboard for real-time monitoring and visualization.

Robotic Arm Path Planning

A reinforcement learning-based path planning system for a 2-DOF robotic arm that learns to reach target positions in 3D space using PyBullet physics simulation and Stable-Baselines3.

Key Features
  • 2-DOF (Degrees of Freedom): Two revolute joints for planar motion
  • Realistic physics: Mass, inertia, damping, and friction modeling
  • Joint limits: Configurable position and velocity constraints
  • End-effector tracking: Real-time position monitoring
  • Continuous action space: 2-dimensional actions for joint control
  • Rich observation space: 10-dimensional state vector including joint positions/velocities, target coordinates, and end-effector position
  • Reward shaping: Multi-component reward function with distance-based penalties, progress rewards, success bonuses, and joint limit violations
  • Early termination: For invalid states and boundary conditions
  • PPO Algorithm: Advanced policy optimization with configurable hyperparameters (learning rate: 3e-4, steps per update: 1024, batch size: 64)
  • Neural network architecture: [128, 128] MLP for policy and value networks
  • Model persistence: Save/load trained models with automatic checkpointing
  • Training monitoring: Real-time progress tracking and performance metrics
  • GUI visualization: Real-time 3D rendering with PyBullet GUI
  • Performance metrics: Success rate, average distance, motion smoothness
  • Episode analysis: Detailed per-episode statistics and trajectory visualization
  • Camera controls: Configurable viewing angles and distances for analysis
  • C++ implementation: High-performance IK solver for real-time execution
  • Target reaching: Automatic joint angle calculation for desired end-effector positions
  • Distance optimization: Minimize end-effector to target distance with fast convergence
  • Cross-language integration: Python-C++ communication via subprocess for optimal performance
Tools & Technologies
PyBullet URDF Real-time Physics Stable-Baselines3 PPO PyTorch Gymnasium Python C++ Subprocess NumPy Matplotlib 2-DOF Arm Inverse Kinematics Joint Control Reinforcement Learning Neural Networks Collision Detection Gravity Modeling 3D Visualization Performance Metrics
Workflow
Environment Setup & Training
1
Initialize 2-DOF robotic arm environment with PyBullet physics simulation
↓
2
Configure RL environment with continuous action space and 10D observation space
↓
3
Train PPO agent with reward shaping for target reaching and joint limit compliance
↓
Path Planning & Execution
4
Agent receives target position and current joint states as observations
↓
5
PPO policy generates joint actions to minimize end-effector to target distance
↓
6
Real-time 3D visualization shows arm movement and performance metrics

Web Technologies

Show Time: Full-Stack Ticket Booking Platform

A modern, production-ready ticket booking application similar to Ticketmaster, built with React, Node.js, and PostgreSQL. Features real-time seat selection, secure payments, comprehensive search and filtering, Ticketmaster API integration, personalized user experience, and beautiful dark mode interface.

Key Features
  • Advanced Search: Real-time search by event name, artist, venue, or location
  • Smart Filtering: Date filters (Today, This Week, This Month, All Dates), price filters, category filters, location-based filtering
  • Sorting Options: By date, price, or name with real-time results and instant filtering
  • Category Navigation: Easy switching between event types (Concert, Theater, Sports, All)
  • User Profiles: Complete profile management with preferences and account settings
  • Favorites System: Save favorite artists and venues for personalized experience
  • Smart Recommendations: AI-powered event suggestions based on favorites and booking history
  • Personalized Dashboard: Custom homepage with upcoming events and recommendations
  • Dark Mode: Toggle between light and dark themes with smooth transitions
  • Event Previews: Video and audio previews for events with enhanced event cards
  • Responsive Design: Optimized for all device sizes with mobile-first approach
  • Interactive Elements: Dynamic UI components with smooth animations and hover effects
  • Ticketmaster API: Real event data with live pricing and availability
  • Stripe Integration: Secure payment processing with comprehensive error handling
  • Real-time Updates: Live seat availability and booking status with Socket.IO
  • Media Services: Unsplash for stock images and W3Schools for sample media files
  • JWT Authentication: Secure token-based authentication with session management
  • Role-based Access Control: Admin and user roles with comprehensive permissions
  • Input Validation: Comprehensive request validation and SQL injection prevention
  • Security Headers: Rate limiting, CORS protection, and XSS protection with helmet
Tools & Technologies
React 18.2.0 Vite Tailwind CSS JavaScript ES6+ React Hooks Context API Node.js 18+ Express.js Socket.IO JWT bcrypt cors helmet express-rate-limit PostgreSQL 15+ Prisma ORM npm nodemon ESLint Prettier Git Docker Docker Compose Nginx Ticketmaster Discovery API Stripe API Unsplash Dark Mode Responsive Design Real-time Updates Role-based Access
Workflow
User Authentication & Discovery
1
User registers/logs in with JWT authentication and accesses personalized dashboard
↓
2
Advanced search and filtering system queries Ticketmaster API for real event data
↓
3
Real-time results display with smart filtering by date, price, category, and location
↓
Booking & Payment Process
4
User selects event and chooses seats with real-time availability via Socket.IO
↓
5
Secure payment processing through Stripe API with comprehensive validation
↓
6
Booking confirmation stored in PostgreSQL database and user profile updated

Others

Robotic Arm Path Planning

A reinforcement learning-based path planning system for a 2-DOF robotic arm that learns to reach target positions in 3D space using PyBullet physics simulation and Stable-Baselines3.

Key Features
  • 2-DOF (Degrees of Freedom): Two revolute joints for planar motion
  • Realistic physics: Mass, inertia, damping, and friction modeling
  • Joint limits: Configurable position and velocity constraints
  • End-effector tracking: Real-time position monitoring
  • Continuous action space: 2-dimensional actions for joint control
  • Rich observation space: 10-dimensional state vector including joint positions/velocities, target coordinates, and end-effector position
  • Reward shaping: Multi-component reward function with distance-based penalties, progress rewards, success bonuses, and joint limit violations
  • Early termination: For invalid states and boundary conditions
  • PPO Algorithm: Advanced policy optimization with configurable hyperparameters (learning rate: 3e-4, steps per update: 1024, batch size: 64)
  • Neural network architecture: [128, 128] MLP for policy and value networks
  • Model persistence: Save/load trained models with automatic checkpointing
  • Training monitoring: Real-time progress tracking and performance metrics
  • GUI visualization: Real-time 3D rendering with PyBullet GUI
  • Performance metrics: Success rate, average distance, motion smoothness
  • Episode analysis: Detailed per-episode statistics and trajectory visualization
  • Camera controls: Configurable viewing angles and distances for analysis
  • C++ implementation: High-performance IK solver for real-time execution
  • Target reaching: Automatic joint angle calculation for desired end-effector positions
  • Distance optimization: Minimize end-effector to target distance with fast convergence
  • Cross-language integration: Python-C++ communication via subprocess for optimal performance
Tools & Technologies
PyBullet URDF Real-time Physics Stable-Baselines3 PPO PyTorch Gymnasium Python C++ Subprocess NumPy Matplotlib 2-DOF Arm Inverse Kinematics Joint Control Reinforcement Learning Neural Networks Collision Detection Gravity Modeling 3D Visualization Performance Metrics
Workflow
Environment Setup & Training
1
Initialize 2-DOF robotic arm environment with PyBullet physics simulation
↓
2
Configure RL environment with continuous action space and 10D observation space
↓
3
Train PPO agent with reward shaping for target reaching and joint limit compliance
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Path Planning & Execution
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Agent receives target position and current joint states as observations
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PPO policy generates joint actions to minimize end-effector to target distance
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Real-time 3D visualization shows arm movement and performance metrics

Edge-to-Cloud Wildfire Detection System

This project implements a real-time wildfire detection pipeline optimized for edge-to-cloud deployment, leveraging Sentinel-2 satellite imagery from the CEMS Wildfire Dataset. The system uses a custom U-Net model for semantic segmentation, trained and evaluated on preprocessed satellite images. The pipeline is designed for scalable inference, distributed training, ONNX Runtime deployment, and performance profiling, with simulation of edge-based image streaming to evaluate latency and memory usage.

Key Features
  • Data Source: Uses open-source Sentinel-2 satellite imagery from the CEMS Wildfire Dataset.
  • Preprocessing Pipeline: Converts raw GeoTIFF data into normalized, resized NumPy arrays with binary masks. Reads multi-band satellite images using rasterio. Resizes and normalizes images with opencv-python and numpy. Splits data into train/validation/test sets using scikit-learn. Stores processed data as .npy files for efficient loading.
  • Script: data/preprocess.py — Handles all preprocessing steps, including directory management and mask binarization.
  • Architecture: Implements a U-Net model in models/unet.py for pixel-level wildfire region detection, with encoder, bottleneck, decoder, and skip connections.
  • Framework: Built with torch and torch.nn modules.
  • Distributed Data Parallel (DDP): Utilizes PyTorch's DDP for multi-GPU training, enabling scalable model training across multiple devices. Data loading is distributed using torch.utils.data.DistributedSampler.
  • Loss & Optimization: Uses binary cross-entropy loss (torch.nn.BCELoss) and Adam optimizer.
  • Script: models/train_ddp.py
  • ONNX Export: Converts trained PyTorch models to ONNX format for framework-agnostic, accelerated inference. Export script: models/export_onnx.py.
  • ONNX Runtime Inference: Runs inference using ONNX models with onnxruntime for high-throughput, low-latency deployment. Supports hardware-accelerated backends (CPU, CUDA, TensorRT).
  • Input Preprocessing: Images are resized, normalized, and converted to the appropriate tensor format before inference.
  • Profiling Utilities: utils/profiler.py provides function-level profiling using Python's cProfile and pstats, and deep profiling of PyTorch models using torch.profiler.
  • Benchmarking: Logs inference latency and memory usage per frame, with summary metrics. Benchmarks across multiple input resolutions and hardware targets. Structured .txt reports for reproducibility and system-level evaluation.
  • Stream Simulator: Simulates real-time image streaming from edge devices, enabling frame-by-frame inference and system evaluation. Evaluates trade-offs in latency and memory usage for different resolutions and hardware.
  • Edge Logs: Inference logs in edge/ directory document performance across various hardware and input sizes.
  • Streamlit Dashboard: Interactive web dashboard for monitoring, visualization, and demonstration. Built with streamlit, matplotlib, and pillow. Enables real-time display of inference results, performance metrics, and system status.
Performance Highlights
  • Achieved a 3.2× speedup in inference throughput (from 28.01 ms/frame on CPU to 8.74 ms/frame on GPU TensorRT, and 5.10 ms/frame with TensorRT FP16 at 128×128 resolution).
  • Benchmarked across multiple input resolutions (64×64 to 256×256) and hardware targets (CPU, CUDA, TensorRT).
  • Reduced memory bottlenecks by up to 42% via optimized operator execution, resolution scaling, and frame-wise stream processing.
  • Logged frame-by-frame inference latency and memory usage to structured .txt reports for system-level evaluation and reproducibility.
Workflow
Workflow
Output / Result

Achieved 5.10 ms/frame with TensorRT FP16 at 128×128 resolution. See Streamlit dashboard for real-time monitoring and visualization.