10/14 Interview and Resume buildiing

Weekly Learning Journal

CST —

Part 1: Helping My Teammates Develop Capstone Ideas

This week our team met to brainstorm capstone directions. We first kicked around ideas in quantum computing—especially how it could reshape security and processing at scale. It was exciting, but we decided it might be too broad for our scope and timeline.

We then explored Neuralink, focusing on how neural interfaces could work with auditory/ear-related data. That generated a lot of interest, but as we compared options (and saw what other groups were selecting), we pivoted to something more grounded and immediately impactful: DeepFake detection.

Our working direction is to analyze video authenticity identifying whether a clip is a DeepFake or not. The topic hits a timely intersection of AI, ethics, and security, and it gives us a clear technical problem to prototype and measure.

Part 2: Learning Journal Update

From this week’s career guide, I refined my resume around measurable outcomes and clearer technical impact. Treating my resume like a prototype helped iterating layout, bullet clarity, and keyword alignment just like a design sprint.

I applied these updates directly to my current job applications and interview prep. I also refreshed core concepts like Big O Notation so I can reason through efficiency tradeoffs out loud during interviews. The same problem-solving mindset we’re bringing to the capstone (defining scope, testing, evaluating) is improving how I communicate my skills professionally.

As a team, we’ve started outlining our final presentation, divvying up roles, and sketching a timeline for research, model exploration, dataset curation, and demo criteria. We’re aiming for a clear, testable pipeline to evaluate DeepFake detection performance and limitations.

Next up: lock scope, gather datasets, and draft evaluation metrics for the first prototype.

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