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

Verified Expert  in Engineering

Machine Learning Developer

Atlanta, GA, United States
Toptal Member Since
April 29, 2020

Hamid is a data scientist as well as a professional full-stack developer. He has a Ph.D. in CS from Georgia Tech and a strong background in NLP/NLU, mainly when applied to the healthcare domain. The focus of Hamid's past works has been the design of interpretable predictive models in diverse areas, both in industry and academia. He is also involved in neuroscience research, especially when combined with recent deep neural-based models.


United Health Group
GPT, Generative Pre-trained Transformers (GPT)...
Disney Streaming Services
Chatbot Conversation Design, Deep Learning, GPT...
USC/ISI (Information Science Institute)
Kubernetes, Deep Learning, GPT, Natural Language Processing (NLP)...




Preferred Environment

TensorFlow, Python, PyTorch, Bioinformatics, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), GPT, Web, React, Next.js, Firebase, SAP Sales and Distribution (SAP SD)

The most amazing...

...project I've worked on involves the summarization of understanding patient-doctor conversations and information extraction in that domain.

Work Experience

Machine Learning Scientist

2022 - PRESENT
United Health Group
  • Acted as a researcher trying to push the state of the art in NLP applied to healthcare.
  • Worked routinely with PyTorch, Hugging Face, NLP, GPT3, S4, Transformer-based models, many other language models (LMs), etc.
  • Improved state of the art in healthcare. My role mainly was researching with NLP.
Technologies: Generative Pre-trained Transformers (GPT), GPT, Natural Language Processing (NLP), Artificial Intelligence (AI)

Data Scientist

2020 - PRESENT
Disney Streaming Services
  • Designed and implemented advanced NLP pipelines for the Disney Streaming Chatbot for customer services.
  • Managed text summarization and topic modeling on survey data.
  • Deployed a designed chatbot using cloud services.
Technologies: Chatbot Conversation Design, Deep Learning, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), GPT, TensorFlow

Machine Learning Developer

2020 - PRESENT
USC/ISI (Information Science Institute)
  • Developed machine learning algorithms for event prediction in news corporations, using different technologies, models (e.g., TensorFlow, BERT).
  • Turned developed codes into deployable products using containers and Kubernetes.
  • Assisted the integration team responsible for delivering a multi-faceted product comprised of different analytics engines.
Technologies: Kubernetes, Deep Learning, GPT, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT)

Machine Learning Researcher

2019 - PRESENT
  • Focused the research on improving NLP pipelines that are used for the summarization of patient-doctor conversations.
  • Adapted recent advances in deep learning (applied to the NLP domain) to the company's internal domain to improve the deployed pipeline.
  • Worked on a variety of healthcare-related NLP tasks. Used technologies and libraries such as deep learning, NLP, transformers, PyTorch, PyTorch Lightning, Hugging Face, etc.
Technologies: Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), GPT, Deep Learning, PyTorch

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: Deep Learning, TensorFlow, Keras, Python

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: Deep Learning, TensorFlow, Python

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: Deep Learning, TensorFlow, Python

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: Deep Learning, R

Prediction of Threats from Radar Generated Images

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.

Using NLP for Improving Alignments of High Throughput Reads

Worked on a pipeline based on Siamese networks to expedite the read alignment score, which can improve and accelerate the assembly process. The readings can potentially belong to different variants and genomes, and we were interested in making sure contaminated samples will not lead to inferior contig quality.

Using Large Language Models for Analysis of Adherence to Clinical Guidelines

The objective of this project was to assess the compliance of submitted documents from healthcare providers with the required criteria and clinical guidelines. These guidelines serve as curated medical documents developed by doctors and clinicians to ensure that treatments are delivered according to the standards of care. The primary focus was to verify whether the providers had completed all the medically necessary tasks for any performed medical procedure, emphasizing the importance of adherence to these tasks.

Developing Advanced Question Answering Models for Information Retrieval from Medical Documents

The objective of this project was to design, develop, and train advanced transformer-based models capable of accurately answering medical questions. The approach employed retrieval-based techniques followed by question answering to extract relevant answers for a given query. By leveraging these methods, the project aimed to enhance the accuracy and efficiency of obtaining precise medical information in response to specific inquiries.


Python, Java, C++, Perl, R


PyTorch, TensorFlow, React, Java Natural Language Processing (JNLP), PySpark, Keras


Data Science


Firebase, Kubernetes, Web, Amazon Web Services (AWS)

Industry Expertise



Predictive Modeling, Deep Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs), Machine Learning, Computer Vision, Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT), Chatbot Conversation Design, SAP Sales and Distribution (SAP SD), Artificial Intelligence (AI), Deep Neural Networks, Biotechnology, Language Models





2012 - 2020

Ph.D. in Computer Science, Bioinformatics, Machine Learning

Georgia Institute of Technology - Atlanta, Georgia, USA

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