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Education

Experience

  • 2023 - 2023
    Johnson & Johnson, Titusville, New Jersey, USA

    R & D Data Sciences Intern

    • Utilized Detectron2 with preloaded Panoptic FPN weights, a robust deep learning model for object

    detection, to perform nuclei segmentation in pan-cancer images by training on panoptic segmented whole-
    slide images from the TCIA dataset.

    • Optimized nuclei segmentation pipeline by employing distributed data parallelism technique, leveraging
    the Ray Tune Python library to parallelize the predictions across multiple GPUs. This approach led to a
    linear improvement in computational time with the increasing number of GPUs.

Skills

A/B Testing Asana (Agile) Convolutional Neural Networks (CNN) Data Preprocessing Decision Trees Exploratory Data Analysis (EDA). Supervised learning: linear and logistic regressions Feature Engineering Fundamentals of Azure ML pipelines deployment git GitHub Jupyter k-means clustering LaTeX Matlab Matplotlib Microsoft Power Bi MS office Numpy Pandas principal component analysis (PCA); Statistical Learning python Pytorch Random Forests Scikit-learn Seaborn sql Statistical Modeling Support Vector Machines (SVM)) VS Code XGBoost
 
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