The Path

Writing a blog, as I suspected, will be an uphill battle. Quality content comes through practice and reflection more than anything. When I published my first blog post this past week, I got some quality feedback from a good friend; when writing in a technical space, it’s important to qualify yourself. Accordingly, that’s the theme of the post this week; it’s time to go over my resume.

Academic Background

I’m 24 years old. I graduated from High School with an IB Diploma at 18. I graduated from Virginia Tech at 22 years old with a B.S. in Computer Engineering. During my undergraduate degree I began an internship with my current employer, and I joined full time upon graduating. The company I work for is a U.S. Government R&D organization. I have worked as a Computer Engineer and then as an AI Researcher since 2020.

A Brief Look into my Work

In my time working as an AI Researcher I have looked at a variety of different problems in the space of computational modeling. Models have a tendency to see their performance degrade over time, this is a phenomenon known as model drift and I have researched this fairly extensively. Machine Learning architectures cause models to innately inherit the biases of the data we provide, this causes concerns around models being biased or unfair and that is a problem area I have researched at length. Recently, I have done research in the area of Adversarial attacks against models and how to mitigate or defend them. Intelligent adversaries have many complex techniques for worsening the performance of models, and preventing or detecting adversarial activity is both very difficult technically and of high importance for security.

Some Interesting Resources

Not sure exactly how to end this one, so I figure I will leave you all with some interesting resources to take a look at. These are what I have read or looked at the most recently in my work.

Textbooks

Practical Statistics for Data Scientists

https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/

Generative Deep Learning

https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/

Papers (emphasis on Large Language Models like ChatGPT)

“Efficient Estimation of Word Representations in Vector Space”

https://arxiv.org/abs/1301.3781

“Attention Is All You Need”

https://arxiv.org/abs/1706.03762