Building an embeddings data platform for visualizing and analyzing unstructured data using NextJS, Python, and Terraform.

Some Highlights:

• Built a vector search tool that lets you visualize queries in real time

• Prototyped and developed RAG interface in Atlas

• Developed a domain-specific language for scalable dataset annotation and state management

• Rearchitectured backend data model for core performance improvements and modular builds

• Developed team's data science platform and practices using Python libraries and GCP (BigQuery)

• Built full-stack features in Django and VueJS for telehealth web application.

• Designed a workflow management system in Python for monitoring patient onboarding and experience.

Commercial Software Engineering Team

Building cloud/ML applications for customers in healthcare and financial service industries.

• Developed data engineering workflows with PySpark on EMR data.

• Built model training and deployment pipelines with AzureML and MLflow.

• Adapted a static analysis library to work with C++ projects as part of a CI/CD pipeline.

• Developed SQL queries and PowerBI reports for tracking worker safety during pandemic.

Research and developing machine learning tools for computational neuroscience.

• Implemented an open-source Python package for multiview learning.

• Developed novel random forest algorithm for estimating mutual information.

• Built Python pipeline that detects and classifies different synapses in brain images.

• Deployed web service that launches pipeline jobs in the cloud using AWS Batch and Docker.

• Used MongoDB to store and format MRI scans; developed RESTful API to handle queries.

Technologies Used: Python, Scikit-Image, NumPy, Plot.ly, Jupyter, AWS, Docker, Flask, MongoDB