About me

I am a recent (May '25) Data Science Graduate from Indiana University–Bloomington, with a Bachelor's degree in Information Technology from Dwarkadas J. Sanghvi College of Engineering in Mumbai, India. I am enthusiastic about applying machine learning and data-driven solutions to real-world problems, combining analytical rigor and problem-solving skills.

As an ML intern at AMD, I optimized image preprocessing pipelines to enhance real-time lane detection performance. At Swan Solutions and Services, I developed an ensemble model for aspect-based customer segmentation, enabling more targeted business improvements. My notable projects include creating quantized models for NSFW classification, which achieved faster inference speeds and more efficient model architectures. I also designed an end-to-end ML pipeline for a network security dataset, employing MLOps tools like MongoDB, MLFlow, DagsHub, GitHub CI/CD, Docker, AWS S3, ECR, and EC2. Additionally, I implemented cataract detection using Explainable AI for improved transparency and interpretability and developed a privacy-preserving federated learning framework for medical diagnosis to ensure robust data security. Other key projects include a speech emotion recognition system, a Natural Language SQL query generator for an e-commerce database powered by GCS and Gemini 1.5, and a GCP-based analysis of IPL data. Proficient in machine learning and deep learning frameworks, as well as statistical methods, I am actively advancing my skills in MLOps. My skill set encompasses programming languages, databases, and essential software and cloud tools for data analysis and model development, enabling me to effectively tackle complex problems.

What i'm doing

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    Data Science Graduate

    Graduated with an MSc in Data Science from Indiana University-Bloomington in May 2025.

  • Web development icon

    Currently Learning...

    Learning and working with MLOps tools to build end-to-end ML pipelines.

Resume

Education

  1. Indiana University-Bloomington

    Aug 2023 — May 2025

    Master of Science in Data Science

    GPA: 3.92/4.0

  2. Dwarkadas J. Sanghvi College of Engineering

    Aug 2019 — May 2023

    Bachelor of Technology in Information Technology

    GPA: 9.01/10.0

Experience

  1. Indiana University–Bloomington

    Machine Learning Researcher

    May 2024 — Aug 2024
    • Developed an elephant recognition pipeline by fine-tuning VGG-19 for top-5 retrieval, achieving 93% accuracy through generalized wrinkle-pattern learning.
    • Boosted identification accuracy by 15% using SIFT local feature matching to verify CNN-ranked candidates via wrinkle keypoints.
    • Improved real-world recognition robustness by 18% through targeted data augmentations (random brightness, synthetic blocking), enhancing model reliability in variable lighting and occlusions.
  2. AMD (Advanced Micro Devices)

    Machine Learning Intern

    July 2022 — Oct 2022
    • Devised a real-time lane detection for autonomous driving systems through an efficient image preprocessing pipeline integrating weighted-average filtering, Gaussian Blur, Canny Edge Detection, ROI selection, and Hough transformation.
    • Accelerated preprocessing pipeline performance by 2.5x through parallelized computations on AMD GPUs using PyOpenCL, significantly exceeding traditional CPU-only execution.
    • Enhanced detection reliability by reducing false positives by 15% using adaptive thresholding and optimized parameter tuning for Gaussian Blur and Canny Edge algorithms, resulting in higher accuracy in varied lighting conditions.
  3. Swan Solutions and Services

    Data Science Intern

    July 2021 — Oct 2021
    • Developed an ensemble model combining LDA for aspect extraction, TextBlob for sentiment identification, and a Logistic Regression ML model with 96.6% peak accuracy to facilitate aspect-based customer segmentation.
    • Conducted root cause analysis utilizing LDA to identify critical improvement areas, reducing customer complaints by 20% and increasing positive reviews by 15%.

My skills

    Overview

    Machine Learning, Deep Learning, Explainable AI, NLP, Computer Vision, Data Analysis, Statistical Analysis

    Programming Languages

    Python, R (Statistical Analysis), C, Java, JavaScript, SQL

  • Python
    90%
  • SQL
    80%

    Frameworks & Tools

    PyTorch, TensorFlow, Keras, scikit-learn, PySyft, OpenCV, NLTK, AWS, GCP, Spark, Docker, MLFlow, DagsHub, Databricks, Tableau, Power BI, Looker, Git, GiHub Actions, Streamlit

  • scikit-learn
    90%
  • PyTorch
    90%
  • NLTK
    75%

    Databases

    Firebase, Microsoft SQL Server, MySQL, PostgreSQL, SQLite, BigQuery, MongoDB, Neo4j

    (* "%" denotes the most frequently used , not proficiency)

  • PostgreSQL
    50%
  • MySQL
    30%

    Libraries

    PyTorch, TensorFlow, Keras, scikit-learn, NLTK, PySyft, OpenCV, PyOpenCL, Pillow, Librosa, Gensim, TextBlob, langdetect, multiprocessing, NumPy, Pandas, Matplotlib, Plotly, Streamlit

Projects & Publications

Contact

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