Technical Arsenal
Cloud & Infrastructure
Amazon AWS (EC2, S3, CloudFormation, SageMaker), Azure Cloud, Docker, Kubernetes
ML/AI Frameworks
PyTorch, TensorFlow, Scikit-learn, Matplotlib, Seaborn, Pandas
Languages
Python, Java, C, C++, Shell scripting
DevOps
CI/CD, Github, Ansible, Jenkins, Puppet, MLOPs, LLMOps
Certifications
Databricks Fundamentals, Databricks Advanced Machine Learning Operations, MLOps/LLMOps
Patents & Innovation
Professional Service & Outreach
Technical Committees
- Technical Program Committee Member: IPDPS 2023/2024 (System Software Track)
- Poster Session Judge: CAPWIC 2022
- Technical Judge: Virginia State Science and Engineering Fair (VSSEF) 2021
- Invited Panelist: "Machine Learning in Computing Systems," MLCS Workshop at SC’20
Mentorship & Community
- Coach: First Lego League (FLL) for middle and elementary school students (2024–2025)
- Mentor: Mentoring Ph.D. candidate, University of Virginia (2021–2022/2023-2026)
- Representative: Society of Women Engineers (GradSWE) at UVA (2017)
- Volunteer: Charlottesville Girls Geek Day & UVA Open House (2016)
Professional Experience
Technical Director & AI Advisor
2024—PresentAI/ML & Digital Product Development
- Architecting resilient, full-stack website and app for real-estate solutions.
- Establishing robust CI/CD pipelines and automated testing frameworks for the website and app.
- Advising on Generative AI model fine-tuning, benchmarking, evaluation, and metric development.
Postdoctoral Researcher
2023—2024Biocomplexity Institute, University of Virginia
- Deployed a distributed orchestration platform, on-prem and cloud, for 10,000+ concurrent epidemiological simulations.
- Benchmarked Generative AI surrogate models for High-Energy Physics simulation.
Intelligent Infrastructure Optimization
2016—2023Ph.D. Research, University of Virginia
- Developed RL-based schedulers for power-modulated datacenters, increasing profit by 6%-18%.
- Built a modular simulator for datacenter resource management that supports heuristic and ML scheduling policies and renewable energy sources.
Distributed Systems Software Intern
May-2019 — Aug-2019Lancium
- Engineered a seamless scheduling pipeline by configuring the cloud backend and implementing custom software to intelligently match user job requests with available GPU resources.
- Accelerated product release cycles by developing an automated testing and CI/CD pipeline, facilitating the beta release and maintenance of Lancium’s cloud platform.
Senior Applications Programmer Ananlyst
2011—2016University of Virginia
- National Grid Orchestration: Administered and optimized the Cross Campus Grid (XCG) and XSEDE grid infrastructure, architecting the integration of compute and storage resources across multiple national supercomputing facilities.
- Systems Integration & Tooling: Engineered the GenesisII software ecosystem by developing grid command-line tools and web services, while establishing a comprehensive unit and regression testing framework to ensure platform stability.
- Technical Leadership & Incident Response: Served as the primary technical lead and point of contact for nationwide grid users, directing incident response and troubleshooting efforts for complex production issues within high-performance computing environments.
Member Technical Staff 2
2005—2008Center for Development of Advanced Computing
- Ported 32-bit Message Passing Interface (MPI) implementation to 64-bit architecture, implementing and testing MPI for TCP/IP and Virtual Interface Architecture (VIA) communication protocols across distributed systems.
- Designed and implemented Parallel File System using NFS3 protocol and MPICH, supporting AIX, Linux, and SunOS platforms with multithreaded IO servers for high-performance parallel processing.
- Built grid computing infrastructure for Garuda Grid project, implementing Storage Resource Broker (SRB) and iRODS testbed for distributed data management across compute clusters.
- Integrated Cluster Management System (CMS) with LoadLeveller for distributed job scheduling and workload management.
Selected Projects & Research
CaloBench: Generative Models for Calorimeters
A comprehensive benchmark study evaluating Generative AI surrogate models (GANs, VAEs, Diffusion) against Geant4 simulation samples for high-energy physics. Focuses on data correlation and performance metrics for the ATLAS Calorimeter.
Launchpad & RARE: RL-Based Scheduling
Developed Reinforcement Learning agents for intelligent resource management in datacenters. Using offline and online RL to optimize for power awareness and renewable energy utilization, achieving up to 18% profit increase.
Parallel Compute Engine (PaCE)
Implemented a distributed parallel computing system designed to execute computationally intensive tasks across multiple nodes. It utilizes a master-worker architecture to manage task distribution, synchronization, and resource allocation to improve processing efficiency.
Selected Scholarly Contributions
Bench 2025 PDF
CaloBench: A Benchmark Study of Generative Models for Calorimeter Showers
JSSPP 2024 PDF
Launchpad: Learning to Schedule Using Offline and Online RL Methods
Ph.D. Dissertation, UVA 2023 PDF
Scheduling to Ensure Performance and Cost Effectiveness in Power-Modulated Datacenters Project Page
JSSPP 2022 PDF
RARE: Renewable Energy Aware Resource Management in Datacenters
MLCS 2020 PDF
Scheduling in Data Centers Running on Renewable Energy with Deep Reinforcement Learning