About me

I am pursuing a Master of Science in Data Science at Indiana University-Bloomington, following a Bachelor of Technology in Information Technology from Dwarkadas J. Sanghvi College of Engineering in India. I enjoy developing my creative and analytical abilities and believe Data Science is the perfect medium to channel my creativity and energy into finding innovative solutions for real-world challenges.

As an ML intern at AMD, I optimized image preprocessing pipelines to improve real-time lane detection. At Swan Solutions and Services, I developed an ensemble model for aspect-based customer segmentation to help drive targeted improvements. My projects include cataract detection with Explainable AI for improved transparency and interpretability, a privacy-preserving federated learning framework for medical diagnosis for enhanced data security, a speech emotion recognition system, and GCP-based analysis of IPL data. Proficient in machine learning, NLP, and deep learning frameworks as well as statistics, I excel at tackling complex challenges and possess expertise in programming languages, databases, and essential software tools for data analysis and model development.

What i'm doing

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    Grad Student

    Pursuing a MSc in Data Science from Indiana University-Bloomington.

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    Research Assistant @ IU

    Working on an elephant identification problem using computer vision under Dr. Daniella Chusyd and Dr. David J. Crandall.

Resume

Education

  1. Indiana University-Bloomington

    Aug 2023 — May 2025

    Master of Science in Data Science

  2. Dwarkadas J. Sanghvi College of Engineering

    Aug 2019 — May 2023

    Bachelor of Technology in Information Technology

Experience

  1. Indiana University–Bloomington

    Graduate Research Assistant

    May 2024 — Aug 2024
    • Developed non-invasive elephant recognition methods using optimized VGG-19 architectures to detect elephants based on distinctive wrinkle patterns, resulting in a 20% improvement over baseline accuracy.
    • Implemented SIFT for feature matching of complex wrinkle patterns in elephant images, resulting in an increase of 15% accuracy.
  2. AMD (Advanced Micro Devices)

    AI/ML Software Engineering Intern

    July 2022 — Oct 2022
    • Devised an image preprocessing pipeline using PyOpenCL, integrating weighted-average and Gaussian Blur, followed by Canny Edge Detector, Region of Interest selection, and Hough transformation for real-time lane detection.
    • Accelerated pipeline processing by 12% with PyOpenCL on AMD hardware, outperforming CPU-only execution.
  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

    PyTorch, TensorFlow, Keras, scikit-learn, PySyft, OpenCV, NLTK, GCP, Spark, Databricks

  • scikit-learn
    90%
  • PyTorch
    80%
  • NLTK
    75%

    Databases

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

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

  • PostgreSQL
    50%
  • MySQL
    30%

    Software

    Excel, Jupyter, Tableau, Power BI, Looker Studio, Git, Android Studio

    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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