Tanmai Mukku

1) Reengineered a data processing pipeline for analyzing genomic sequences, saving $100-$450 per run by transitioning from AWS to a hybrid cloud environment, with potential savings of up to $22,500 for large-scale data analysis. 2) Integrated Docker containerized applications with an on-prem High-Performance Computing (HPC) cluster using Bash scripts, Apptainer, and Slurm, enabling the processing of up to 200 concurrent jobs. 3) Developed REST API endpoints in Java and improved the web portal’s UI/UX (using React and CSS Grid), enabling researchers to automate retrieval of large datasets (up to 1.5 TB) and trigger single-click pipeline runs on the HPC cluster.

1) Designed and implemented database architecture using PostgreSQL for Medicaid data, utilizing distributed storage, indexing, partitioning, and query optimization techniques to improve query performance. 2) Developed a Python-based fuzzy matching system to accurately identify and consolidate unique patient records in large scale healthcare databases, processing and standardizing millions of data entries.

1) Led a team of 3 in developing Java/Spring Boot microservices to transition TradeEdge's rule engine to a BPM-powered workflow engine, reducing approval time by ~95% with flexible, automated workflows via an intuitive drag-and-drop UI. 2) Ensured scalability to manage 100+ parallel requests through Kafka, asynchronous programming, and multithreading. 3) Orchestrated the microservices architecture using Docker, Kubernetes, and nginx as an Ingress load balancer. Set up SSL certificates with Let's Encrypt for secure, encrypted communication using mutual TLS within the service mesh. 4) Investigated workflow engines (Camunda, Netflix Conductor) to implement redundancy and failover mechanisms for microservice orchestration, increasing uptime to 99.9% and ensuring high availability and fault tolerance. 5) Optimized legacy microservices by integrating telemetry, identifying bottlenecks, introducing caching, and refining database queries, resulting in a ~40% reduction in API response time and enhanced system metrics with in-depth logging. 6) Implemented GitLab CI/CD pipelines, conducted pull requests, mentored 2 interns, performed code reviews, and conducted unit/integration testing to ensure reliable and scalable deployments on Kubernetes in AWS.

Teaching Assistant for IoT, Embedded Systems, Computing Ethics (Value Education in Computer Science)

1) Co-engineered an IoT air pollution monitoring device, achieving a ~35% cost reduction compared to existing solutions. 2) Developed a distributed IoT system using edge computing for real-time data reduction and processing. 3) Publication: Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors (IEEE 2020)

Cloud Computing, Machine Learning, Data Mining, Natural Language Processing, Data Processing at Scale

Computer Programming, Data Structures, Algorithms, Operating Systems, Database Systems, Statistical Methods in Artificial Intelligence, Computer Vision, Optimization