KRISH MOODBIDRI

Open OnDemand Deployment: customized and rolled out the Open OnDemand web portal—complete with graphical job-submission forms, file manager, and interactive shell access—lowering the barrier for new HPC users and cutting basic support tickets.

Automated User Onboarding: built a Flask + Celery self-registration service backed by RabbitMQ and S3-stored metadata, shrinking manual account-provisioning from ~48 hours to under 5 minutes per user.

SSO & Security Integration: integrated Shibboleth/SAML single-sign-on across multiple clusters, automating IdP metadata ingestion and certificate rotation to align with UAB cybersecurity policies.

Zero-Code Cluster Templates: authored Terraform modules and Ansible playbooks for “zero-code” instantiation of specialized clusters (GPU, high-memory, MPI), enabling researchers to spin up tailored environments in minutes.

Researcher Training & Support: led monthly workshops and office hours on Slurm job submission, Rclone cloud-storage workflows, and JupyterLab analytics, empowering interdisciplinary teams to adopt HPC tools confidently.

Automated Monitoring Pipeline: deployed XDMoD performance monitoring via a nightly CI/CD workflow (Packer image builds + Terraform deployments), delivering up-to-date dashboards for capacity planning and resource-use analysis.

JobArchiver Utility: developed a Python-based tool that captures submitted job scripts along with environment snapshots and logs, ensuring reproducibility and simplifying grant-report audits.

Seamless Migration Routing: implemented SSHPiper rules to dynamically route user connections during the GPFS4→GPFS5 filesystem migration, achieving zero-downtime cutover for all research groups.

Load Testing & Benchmarking: conduct regular load and stress tests on Cheaha—especially after introducing new services like SSHPiper routing—to validate performance under peak demand and identify bottlenecks for proactive tuning.

• Curriculum Design & Instruction: Designed, taught, and graded CAC 210 (Data Structures & Media Computation) and CAC 310 (Programming Languages), developing lectures, hands-on labs, and assessments to reinforce both foundational principles and advanced concepts.
• Fall 2018 Semester: Delivered CAC 210 and CAC 310 courses with an emphasis on computational thinking and practical applications, integrating real-world examples to deepen student understanding.
• Spring 2019 Semester: Continued instruction in CAC 210 and CAC 310, enhancing the syllabus with media-computation projects and collaborative coding exercises to boost engagement.
• Data Science Club Advisor (Fall 2019): Mentored and guided student-led workshops and projects, fostering hands-on experience with Python, data analysis libraries, and best practices in exploratory data science.

• Developed expression analyzer form using JavaScript and jQuery, enhancing data processing capabilities through Entity Framework.
• Implemented SQL-based data transfer mechanisms to boost system performance and data management.
• Collaborated with clients to gather and analyze requirements, translating business needs into technical specifications.
• Contributed to development of research resources for Department of Surgery, focusing on healthcare-related projects.
• Gained comprehensive experience in full-stack development, from UI design to back-end data handling.

• Contributed to the modernization of legacy software by writing new code in Java, enhancing system functionality and performance.
• Designed and developed a custom Logging Framework using Node.js, improving the system's ability to track and analyze operations.
• Implemented and tested the Logging Framework using the MeanBean package in Java, ensuring robust and reliable performance.
• Collaborated with other interns on bug fixing initiatives for the existing system, adhering to weekly deliverable schedules.

• Contributed to a high-priority research project focused on developing algorithms to identify and track potential security threats on social media platforms.
• Implemented and optimized the Pregel+ package to run large-scale graph algorithms in parallel, specifically applying the PageRank algorithm to analyze Twitter user networks comprising 18 million records.
• Conducted extensive data cleansing and preprocessing using C and C++, improving data quality for more accurate analysis.
• Developed and implemented code for training, testing, and cross-validation of the PageRank algorithm, experimenting with various damping factors to optimize performance.
• Achieved an impressive 0.934 accuracy rate while reducing processing time to approximately 1.5 hours for the entire dataset.

• Gained comprehensive exposure to various operational departments, with a primary focus on Web Development and Marketing.
• Developed and maintained web applications using platforms such as Bootstrap and Joomla, ensuring responsive and user-friendly interfaces.
• Collaborated with cross-functional teams to gather requirements, design solutions, and implement features that meet client needs.
• Utilized Bootstrap to create responsive web designs, enhancing user experience across different devices and screen sizes.