
Hameed Hasan
Verified Expert in Engineering
Machine Learning Developer
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.
Portfolio
Experience
Availability
Preferred Environment
TensorFlow, Python, PyTorch, Bioinformatics, Generative Pre-trained Transformers (GPT), GPT, Natural Language Processing (NLP), Web, React, Next.js, Firebase, 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
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.
Data Scientist
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.
Machine Learning Developer
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.
Machine Learning Researcher
3M/MModal
- 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.
Senior Machine Learning Engineer
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.
Senior Software Engineer
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.
Software Engineer Intern
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.
Data Analyst Intern
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.
Experience
Prediction of Threats from Radar Generated Images
Using NLP for Improving Alignments of High Throughput Reads
Using Large Language Models for Analysis of Adherence to Clinical Guidelines
Developing Advanced Question Answering Models for Information Retrieval from Medical Documents
Skills
Languages
Python, Java, C++, Perl, R
Libraries/APIs
PyTorch, TensorFlow, React, Java Natural Language Processing (JNLP), PySpark, Keras
Paradigms
Data Science
Platforms
Firebase, Kubernetes, Web, Amazon Web Services (AWS)
Industry Expertise
Bioinformatics
Other
Predictive Modeling, Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks (RNN), Machine Learning, Computer Vision, Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT), Chatbot Conversation Design, SAP SD, Artificial Intelligence (AI), Deep Neural Networks, Biotechnology, Language Models
Tools
MATLAB
Frameworks
Next.js
Education
Ph.D. in Computer Science, Bioinformatics, Machine Learning
Georgia Institute of Technology - Atlanta, Georgia, USA