Center for Translational Data Science

Mentors: Student: Abhijith M S

This project develops an Operations Center dashboard for centralized deployment, monitoring, and management of multiple Gen3 data commons. It features a microservice-based backend in Go, using gRPC to communicate with Kubernetes-native agents across deployments. A Next.js frontend provides a unified interface for real-time observability and control. The system offers live insights into service health, logs, metrics, and deployment states. It enhances the reliability, scalability, and maintainability of the Gen3 ecosystem.

Mentors: Student: Andriana Sielli

This project addresses a key challenge in biomedical data management: enabling secure, standards-compliant access to clinical FHIR data while integrating with the Gen3 platform. The goal is to develop an open-source, containerized FHIR proxy that enforces Gen3 access control via Arborist and supports asynchronous API interactions for high-performance data access. The solution includes: 1) A proof-of-concept FastAPI proxy that routes FHIR requests to a HAPI FHIR JPA server, adding Gen3 security tags for each resource. 2) Scripted ingestion of synthetic NCPI FHIR resources to facilitate testing and reproducibility. 3) Comprehensive unit tests and example configurations for local and cloud deployment. 4) Full containerization with Docker and Docker Compose, with plans for Kubernetes deployment via Helm. By providing a secure, FAIR-aligned, and standards-based framework for FHIR data, this project empowers researchers and developers to access and manage biomedical data efficiently while maintaining strict compliance with security and privacy requirements.

Mentors: Student: Fei Xiang Peng

This project addresses the critical challenge of GPU resource management in ML/AI research environments where researchers often face delays due to inefficient resource allocation. I'll develop a comprehensive CPU-based control plane that efficiently orchestrates GPU resources through four key components: an advanced job scheduler with priority management and preemption capabilities, a comprehensive observability platform with real-time dashboards, a streamlined model deployment pipeline with CI/CD integration, and robust infrastructure automation. By implementing technologies like Slurm for scheduling, Prometheus/Grafana for monitoring, Terraform/Ansible for infrastructure automation, and integrating ML-specific tools like MLflow and NVIDIA Triton, the system will deliver significant benefits to researchers: reduced wait times for GPU access, transparent visibility into resource availability, priority access for time-sensitive research, and a seamless deployment pipeline from training to serving. Deliverables include a functioning scheduler with priority queue support, a comprehensive monitoring system with custom dashboards, infrastructure-as-code modules, a model deployment pipeline with CI/CD integration, and an intelligent alerting system. The complete solution will serve as a reference implementation for academic GPU cluster management, providing reusable components and best practices for similar infrastructure requirements in the ML research community.

Mentors: Student: Dhiren-Mhatre

This project aims to enhance the data download capabilities of the Center for Translational Data Science's Python SDK & CLI by implementing robust, efficient, and user-friendly functionality for researchers to download and stream large biomedical datasets. By integrating with Globus for secure data transfer and optimizing performance through asynchronous processing, this implementation will significantly improve the research workflow for scientists working with large-scale biomedical data. The proposed solution will focus on maintaining high code quality through comprehensive testing, ensuring a seamless user experience, and optimizing performance for handling large datasets. The end result will be a reliable tool that enables researchers to efficiently transfer data to their workspaces for analysis, ultimately accelerating scientific discovery in the biomedical field

Mentors: Student: Sanskriti Mathuria

This project tries to integrate data download client functionality into the Gen3 Python SDK & CLI, to provide a single tool for interacting with Gen3. The project is implemented by using python modules such as multithreading or multiprocessing to enable asynchronous download of multiple files.