About me

A detail-oriented software engineer with a Bachelor of Science in Computer Science from Purdue University, specializing in Machine Intelligence and Machine Learning. Experienced in designing and deploying scalable applications using technologies like Python, Django, React, and cloud infrastructure. Skilled in data structures, algorithms, and AI model development. Has contributed to multiple projects involving predictive analytics, automation, and intelligent systems. Brings a blend of strong academic foundation and practical experience through internships, research, and open-source contributions. Passionate about building impactful tech solutions, solving complex problems, and continuously exploring advancements in AI, machine learning, and modern software engineering practices.

What I'm Doing

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    Azure

    Other

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    Dart

    Language

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    Database Management Systems

    Concepts

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    Database Management Systems

    Concepts

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    Deep Learning

    Concepts

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    Google Cloud Platform

    Other

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    JavaScript

    Language

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    Kubernetes

    Other

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    MATLAB

    Language

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    MongoDB

    Other

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    MySQL

    Other

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    Perl

    Language

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    PyTorch

    Other

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    Python

    Language

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    R

    Language

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    RESTful APIs

    Other

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    Ruby

    Language

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    Rust

    Language

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    Swift

    Language

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    TensorFlow

    Other

Testimonials

  • Reference available upon request

    Reference available upon request

Reference available upon request
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Reference available upon request

Companies

Resume

Education

  1. Purdue University

    Jan 2000 – Apr 2025

    Degree: Bachelors Field of Study: Computer Science Grade: 3.68

Experience

  1. Software Development Apprentice

    Dec 2024 – Jan 2025

    Deployed a python package to reduce the manual effort of converting Material Safety Data Sheets to cover sheets that can comply with UN GHS Standards Achieved ∼5000 hours/year in time savings (parsing time reduced from 40 mins to 2 mins per data sheet) for Merck Scientists by automating and streamlining data sheet parsing Project involved cross-geographical collaboration with 7 members

  2. ML Research Apprentice

    Dec 2024 – Jan 2025

    The project focused on improving the performance of ML models that produce knowledge graphs on drug-related health records to aid research by health professionals Used BERT transformer model to perform Information Extraction tasks, including Named Entity Recognition and Relation Extraction from a Harvard Medical School dataset focused on Adverse Drug Events Fine-tuned a bioBERT model for Named Entity Recognition using hyper parameter optimization techniques like Hyperband and Population-based training, leading to significant training result improvements of 5%

  3. Solutions Architect Intern

    May 2024 – Jan 2025

    Saved ∼7 hours per week by developing web scraping automations using Python and Power Automate for the forklift comparison project, integrated Angular widgets into the internal website to enhance functionality Projected annual savings of approximately $500,000 by training machine learning models for OCR and document analysis of Statement of Work (SOW) documents Streamlined marketing ads data retrieval by optimizing API calls and building efficient data pipelines on Palantir Developed a CI/CD pipeline on Azure Cloud for deploying the SOW document analysis application

My skills

  • Azure
    beginner
  • Dart
    beginner
  • Database Management Systems
    beginner
  • Database Management Systems
    beginner
  • Deep Learning
    beginner
  • Google Cloud Platform
    beginner
  • JavaScript
    beginner
  • Kubernetes
    beginner
  • MATLAB
    beginner
  • MongoDB
    beginner
  • MySQL
    beginner
  • Perl
    beginner
  • PyTorch
    beginner
  • Python
    beginner
  • R
    beginner
  • RESTful APIs
    beginner
  • Ruby
    beginner
  • Rust
    beginner
  • Swift
    beginner
  • TensorFlow
    beginner

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