Career Portfolio
Career Plan Slideshow
Honors & Accomplishments
- Dean’s List, three consecutive years (semester-over-semester recognition)
- GPA: 3.8
- Multiple academic scholarships awarded
- Full-time offer secured prior to graduation
Interests
- Applied AI and its intersection with quantum computing
- Cloud platforms, distributed systems, and scalable infrastructure
Work Sample
All work I perform at SHI is confidential and cannot be shared publicly. The overview below is a high‑level description intended only to illustrate process thinking and governance patterns; it omits proprietary code, data, and implementation specifics.
Project DUST (Dormant User & Site Termination) is a multi‑stage governance framework that manages the lifecycle of inactive collaboration sites, designed to scale across domains and workloads. It begins with automated discovery: a PowerShell job queries Microsoft Graph site‑usage reports to broadly identify candidates for inactivity. A second, precision‑verification step connects to each candidate via PnP PowerShell and confirms true state by reading the authoritative LastItemUserModifiedDate. Verified results feed a central Dormant Site Management SharePoint list, which orchestrates a two‑stage approval workflow. First, the site owner is notified and their response moves the record to a Requested state. Next, a system administrator performs a mandatory review and makes the final determination—Approved – Archive or Approved – Keep Active.
When archival is approved, a Power Automate flow invokes an Azure Automation runbook that places the site in a read‑only lock, preserving content while preventing changes, and records a 93‑day retention timer in the tracking list. A separate, daily PowerShell process queries for items exceeding retention and executes termination, moving the site to the tenant recycle bin to provide a final recovery window. This closed‑loop design aligns discovery, verification, approvals, enforcement, and disposition under one auditable workflow, reducing risk and manual toil while providing clear accountability and retention controls. All timing, thresholds, and notifications are centrally configurable to align with policy.
OpenAI Emerging Talent Community
I’m an active participant in the OpenAI Emerging Talent community, a cohort of builders and learners who collaborate across disciplines to advance practical AI. We host frequent meetings, topic deep‑dives, and live discussions, and we regularly network with professionals and mentors from diverse backgrounds—including some of the world’s leading experts in their respective domains.
- Regular roundtables on applied AI, safety, evaluation, and deployment patterns
- Peer demos and feedback sessions to iterate on prototypes and research ideas
- Networking with engineers, researchers, designers, product leaders, and security practitioners
- Access to best practices and playbooks for integrating model capabilities responsibly
- Collaboration opportunities that often lead to real‑world projects and mentorship
Outside Technical Resources
As an IT professional, I regularly consult the following external resources to stay current, prototype ideas, and deepen knowledge.
- X (formerly Twitter) — Real‑time updates from AI researchers, vendors, and communities. I follow breaking releases, benchmarks, and threads to spot trends quickly and validate signals against primary sources.
- OpenAI — Official product docs, capability notes, and API references I use frequently when integrating models, reviewing safety guidance, and planning upgrade paths.
- NotebookLM — A study and research companion I use to synthesize papers, notes, and docs into concise briefs, extract citations, and generate questions for deeper exploration.
References (APA 7th)
- X Corp. (n.d.). X (formerly Twitter). https://x.com
- OpenAI. (n.d.). OpenAI. https://openai.com
- Google. (n.d.). NotebookLM. https://notebooklm.google
Professional References
The following professionals have agreed to serve as references and can speak to my skills, work ethic, and achievements.