About me

Computer Science Graduate: Stevens Institute of Technology

As a Computer Science graduate passionate about IoT, Cloud Computing, and AI/ML. I specialize in building intelligent, data-driven applications. I have developed a home automation system integrating IoT devices with AWS, conducted research on multiple time series forecasting resulting in a published paper, and created ML-powered tools on AWS to predict data accuracy for CSV, Excel, and image files.

My technical expertise spans full-stack web development, cloud integration, and database-driven applications. One of my key projects includes a grocery dashboard application with user and server-side functionality, enabling item management, and real-time updates. I thrive in team-oriented environments and enjoy solving complex problems with practical, innovative solutions.

What I'm Doing

  • IoT & Cloud Integration

    Building IOT-powered applications integrated with AWS (cloud) to enable automation and cloud-based control.

  • AI & Machine Learning

    Developing ML solutions for data analysis, forecasting, and accuracy prediction across diverse datasets.

  • Full Stack Web Development

    Creating database-driven applications and interactive dashboards using modern web frameworks.

  • Research and Data Science

    Conducting research in timeseries forecasting, resulting in published work and practical data-driven insights.

Developer Badges

Resume

Education

  1. Stevens Institute Of Science And Technology

    2024 — 2026
    • Masters of Science in Compter Science

  2. CHARUSAT University

    2020 — 2024
    • Bachelor of Technology in Information Technology

Experience

  1. Research Assistant

    Information And Library Network
    Jan 2024 — July 2024
    • • Developed an intelligent search system using BERT and Sentence Transformers to retrieve highly relevant results from a large database.
    • • Enhanced search effeciency by nearly 40% by displaying the closest matching queries and improving user experience.
    • • Applied NLP and machine learning techniques to solve real-world search and data retrieval challenges.

  2. Computer Vision Intern

    Jekson Vision
    May 2023 — July 2023
    • • Optimized image processing workflows to improve annotations and segmentations for diverse datasets.
    • • Implemented advanced filtering techniques on masked and unmasked images, achieving 15% higher image clarity and 25% faster post-processing.
    • • Automated annotation and segmentation tasks with Python scripts, reducing manual effort by 90% and boosting workflow efficiency.

  3. Database Management Intern

    Charly Computers
    May 2022 — July 2022
    • • Developed a visual desktop grocery store application using VB.NET and MySQL with both user and admin interfaces.
    • • Implemented user-side features for adding, editing, and managing carts and wishlists.
    • • Built an admin dashboard to monitor, manage, and update products in real time.

My Skills

  • AI/ML (TensorFlow, Sci-kitlearn, Pytorch)
    80%
  • Cloud Computing
    70%
  • IoT and Edge Integration(AWS IoT Core)
    80%
  • Python Development & Automation
    85%
  • Database Management (MySQL, PostgreSQL)
    75%
  • Datascience and Forecasting(TimeSeries, Pandas, Numpy)
    80%

Projects

IntelliML: Automated Machine Learning Dashboard (Ongoing)


  • • Goal: Build an AutoML dashboard for end-to-end model development and evaluation.
  • • Technologies Used: Python, Auto EDA, Scikit-learn, Streamlit, GitHub.
  • • Project Highlights: Automated pipeline handles cleaning, feature engineering, training, and evaluation; interactive dashboard provides insights and recommendations.

Developed: Present

SpaceX Launch Outcome Prediction and Reusability Impact Analysis
  • • Goal: Predict SpaceX launch success and analyze booster reusability impact.
  • • Technologies Used: Python, Scikit-learn, XGBoost, LightGBM, SMOTE, Pandas, Matplotlib, StreamLit.
  • • Project Highlights: Balanced data with SMOTE, ensemble models performed best, reusable boosters had 100% success, lighter payloads, rising use post-2017.

Developed: July 2025

Search Result Enhancement
  • • Goal: Improve website search efficiency by mapping metadata and associating relevant keywords with specific content.
  • • Technologies Used: Python, BERT, Sentence Transformers, Jupyter Notebook.
  • • Project Highlights: ompared BERT and Sentence Transformer for semantic search; optimized query–content matching through dense embeddings.
  • • Conclusion:Semantic embedding models significantly enhance search relevance, leading to a more intuitive user experience.

Developed: Sep 2024

Multiple Time Series Forecasting
  • • Goal: Forecast multiple time series using deep learning and statistical models.
  • • Technologies Used: Python, TensorFlow, Scikit-learn, N-BEATS, Time Series Analysis.
  • • Project Highlights: Achieved 0.18 MAPE with N-BEATS; benchmarked models on diverse datasets; published research paper on findings.
  • • Conclusion: Deep learning models are highly effective for time series prediction when paired with the right architecture and input strategy.

Developed: Apr 2023

NYC Taxi Trip Duration
  • • Goal: Predict total ride duration of NYC taxi trips using historical trip and location data.
  • • Technologies Used: Python, XGBoost, LightGBM, Scikit-learn, Pandas, PCA.
  • • Project Highlights: Built end-to-end pipeline covering EDA, feature engineering, model tuning, and ensemble predictions; used K-Fold cross-validation and hyperparameter optimization.
  • • Integrated multiple regression models with automated feature selection and dimensionality reduction; unit-tested core components for robustness.

Developed: Sep 2022

Home Automation using ALexa Skills Kit
  • • Goal: Develop a low-cost home automation system using Alexa Skill Kit to control cloud-connected devices via voice commands.
  • • Technologies Used: Alexa Skill Kit, AWS Lambda, IoT Kit, Relay Module, Voice Processing
  • • Project Highlights: Integrated Alexa with smart devices using predefined utterances and AWS-hosted skills; enabled real-time control, status updates, custom responses, and security alerts via automation triggers.

    • Developed: July 2022

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