Hameed Hasan, Machine Learning Developer in Atlanta, GA, United States
Hameed Hasan

Machine Learning Developer in Atlanta, GA, United States

Member since February 9, 2020
Hamid is a data scientist with a Ph.D. in computer science/bioinformatics, with a minor in machine learning. He has a strong background in deep learning, algorithms, and programming. His past works have focused on designing predictive models both in industry and academia. His expertise lies in the healthcare domain.
Hameed is now available for hire




Atlanta, GA, United States



Preferred Environment

Python, TensorFlow, Keras, C++, Java, Perl

The most amazing...

...project I've developed was a deep convolutional neural network for the prediction of threats (i.e., persons carrying guns), in real-time.


  • Senior Machine Learning Engineer

    2020 - 2020
    Liberty Defense
    • Designed and showcased a deep convolutional neural network for the prediction of threats.
    • Involved state-of-the-art image segmentation and detection such as Mask-RCNN for the segmentation of threats (e.g., in cases hiding guns or having guns with them).
    • Achieved remarkable accuracy of (95%) in detecting cases carrying guns. Used the TensorFlow library for implementation. Trained on CUDA GPUs.
    Technologies: Python, Keras, TensorFlow, Deep Learning
  • Senior Software Engineer

    2018 - 2019
    Home Depot
    • Designed and implemented deep models for search and personalization. The task was to rank items returned by a search engine for different search phrases with respect to their relevance and satisfaction of users.
    • Trained NLP models implemented with TensorFlow and trained on GPU. Used recurrent neural nets along with siamese networks. Integrated multiple modalities such as user behavior.
    • Required preprocessing scripts written in the Spark framework to generate and preprocess large datasets.
    Technologies: Python, TensorFlow, Deep Learning
  • Software Engineer Intern

    2018 - 2018
    Verizon Connect
    • Developed an app using advanced recommender systems for recommending the best matching shopping places for drivers. Used and sorted through a large amount of data accumulated in data clusters.
    • Utilized driver behaviors as well as their personalities and demographics to train an integrated deep recommender system. The data was accumulated from a large number of vehicles consuming the product (dongle).
    • Integrated two types of recommender systems; the content-based filtering methods, and the collaborative filtering method. Content-based modeled individual personal information, while the collaborative modeled driving behaviors and habits.
    • Used TensorFlow and Python to achieve the task using both collaborative and content-based filtering approaches. Trained end-to-end.
    • Achieved significant performance in predicting the preference of drivers for their shopping center of interest.
    Technologies: Python, TensorFlow, Deep Learning
  • Data Analyst Intern

    2015 - 2015
    UCB Pharma
    • Developed a deep learning pipeline based on auto-encoders to predict Parkinson's disease from claims data. The goal was to predict whether the person has Parkinson's based on past visits at different doctors.
    • Utilized the H2O library in R to implement a deep network from features describing the patient's past medications and diagnosed codes. Achieved an impressive prediction performance of about 90%.
    • Identified cases in the early stages of the disease (to receive a more successful treatment), by using the trained model to find trial cases sooner.
    Technologies: R, Deep Learning, Medical Claims


  • Prediction of Threats from Radar Generated Images (Development)

    Developed a deep convolutional neural network for the prediction of threats (i.e., persons carrying guns), in real-time. This project was the analytics part of a platform developed by the company I had collaborated with. The overall platform included using a multi-static radar system that generated 3D volumetric images and a Kinect camera to find the distance to the camera. The radar phased data were then preprocessed and combined with the Kinect image to generate a 3D volume. We used state-of-the-art deep image segmentation and detection to uncover threats. This project involved the use of popular deep architectures such as R-CNN, Fast/er R-CNN, and Mask RCNN.


  • Languages

    Python 3, Java, C++
  • Libraries/APIs

    TensorFlow, PySpark
  • Industry Expertise

  • Other

    Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Machine Learning, Computer Vision, Natural Language Processing (NLP)
  • Tools



  • Ph.D. in Computer Science, Bioinformatics, Machine Learning
    2012 - 2020
    Georgia Institute of Technology - Atlanta, Georgia, USA

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