Linux infrastructure, automation, observability, and platform engineering for research computing, scientific systems, and data-intensive discovery.
High-availability system architectures built for continuous data acquisition.
Declarative state using Infrastructure as Code (IaC) to eliminate drift.
End-to-end performance visibility through multi-layered telemetry pipelines.
Least-privilege permission models and isolated networking by default.
Optimized specifically for intensive, high-throughput, and research computing.
Developing and maintaining hands-on homelab simulations to build foundational, repeatable competencies in Linux systems administration, service routing, and scientific data environments.
Docker-based home server environment used to practice Linux administration, service operations, networking, reverse proxying, monitoring, and infrastructure documentation.
A collection of self-hosted services managed with Docker Compose, organized service directories, log rotation, and repeatable operational patterns.
Tailscale-based private access model for reaching internal services securely without exposing the homelab directly to the public internet.
AdGuard Home configuration for internal DNS rewrites, service-friendly hostnames, and cleaner access to local infrastructure.
Nginx Proxy Manager setup for routing internal service names to Docker services through a central reverse proxy layer.
Local Git and Gitea workflow for tracking infrastructure notes, website changes, configuration experiments, and future deployment automation.
Building practical monitoring habits with service health checks, logs, dashboards, uptime tracking, and operational review.
Developing Bash and Python scripting practices for repeatable maintenance, checks, backups, and small infrastructure tasks.
Hands-on Linux infrastructure study covering filesystems, permissions, processes, services, logs, networking, storage, and troubleshooting.
Planned practice environment for learning HPC and research-computing concepts such as schedulers, data pipelines, reproducibility, and scientific workflows.
Detailed breakdowns of system layout exercises, service deployment procedures, and learning plans compiled in private simulation environments.
A practical writeup on structuring Docker Compose services, service directories, log rotation, container recreation, and operational documentation for a self-hosted Linux server.
A local-to-server workflow for developing a static portfolio on a MacBook, pushing changes to Gitea, and deploying safely to a Docker-served web directory on a Linux server.
A homelab network design exercise using Tailscale, AdGuard Home DNS rewrites, home.arpa names, and reverse proxy routing to access internal services without public exposure.
A planned learning environment for exploring research-computing foundations such as job scheduling concepts, reproducible workflows, Linux service operation, and data-oriented infrastructure patterns.
Developing robust system administration and DevOps foundations to support, monitor, and scale repeatable scientific workflows.
Practical Linux administration foundations for reliable service operation.
Containerized service management for repeatable self-hosted infrastructure.
Private access, local naming, and service reachability for homelab systems.
Internal service routing and controlled access patterns.
Developing operational visibility for running services.
Small automation practices for repeatable infrastructure work.
Version-controlled notes, website work, and future infrastructure-as-code habits.
Learning how Linux infrastructure supports scientific and data-intensive work.
Planned learning path toward research computing and HPC-adjacent operations.
I am building a practical path toward scientific infrastructure and research computing roles by developing Linux systems, container operations, networking, observability, automation, documentation, and reliability habits in a hands-on homelab environment. This portfolio is intentionally aligned with the kinds of computing environments that support gravitational-wave observatories, open science platforms, HPC-adjacent operations, and data-intensive research workflows.
Observatories like LIGO Laboratory, Virgo, Cosmic Explorer, and the Einstein Telescope require absolute uptime for data acquisition nodes. My self-directed studies in internal DNS, private VPN tunnels, and system logging are inspired by these extreme uptime goals and are designed to build relevant system-monitoring habits.
The International Gravitational-Wave Network (IGWN) and GWOSC process large volumes of gravitational-wave data. My focus on local Gitea configuration tracking, reproducible Docker Compose directories, and structured scripts is geared toward preparing for data-intensive research grid environments.
Scientific computing environments often require rigorous, repeatable software states to avoid data drift. Studying Bash/Python scripting, systemd task timers, and automated status reviews serves as direct preparation for supporting scalable research pipelines.
I am developing a focused engineering path around Linux infrastructure, DevOps foundations, and research-computing readiness. My work starts with practical systems: a Docker-based homelab, private networking, internal DNS, reverse proxying, service monitoring, Git-based workflows, and repeatable documentation.
The long-term goal is to contribute to environments where reliable computing systems help researchers run experiments, process data, share results, and support scientific discovery.
"Scientific discovery depends on reliable, reproducible, and observable systems."
Open to opportunities where reliable, well-documented infrastructure supports meaningful science. I am especially interested in Linux infrastructure, research computing, DevOps foundations, homelab-to-platform engineering, and scientific systems work.
I am seeking roles within research computing groups, astrophysics collaborations, academic supercomputing centers, and physics facilities working on next-generation instruments (such as Cosmic Explorer, LIGO, and the Einstein Telescope).
