I created the Semantic Universe Explorer (SUE) to solve a problem I kept encountering: how to meaningfully compare semi-structured documents such as corporate sustainability reports. In doing so, it became a natural showcase for several of my core AI specialties, including vector embeddings, retrieval-augmented generation (RAG), and semantic exploration. 

After winning the annual Hackathon at KLA for creating a project centered around AI-assisted Global Impact Reporting, I became very interested outside of work in how I could scrape these reports to gauge different corporations' performances in metrics relating to subjects such as: 

• Water Usage

• Hazardous Waste Handling

• Workforce Management and Fair Labor

and many more.

This ever-evolving library of viewers and tools demo's some of the ways to impartially compare not just passages from Global Impact Reports but any document type where important quantitative information must be parsed from passages written in qualitative, natural language.

My work with multi-agent systems goes back to 2015, when I earned my Master’s degree in Computer Science at the University of Southern California, specializing in these formulations.

Supported by a fellowship from the Department of Homeland Security, I studied a special game-theory formulation known as a Stackelberg game, in which two competing agents, an attacker and a defender, act and react to one another.

My research applied this framework to TSA airport screening, where defenders must allocate limited screening resources despite uncertainty about when passengers will arrive. I designed algorithms intended to preserve strong defender performance despite imperfect information.

I left the project in 2016 after becoming somewhat jaded about the nature of the work.* The research continued and was published shortly after my departure.

View a job talk and learn more by clicking here.

*The program was widely advertised as “AI for Social Good,” though my work could easily have appeared in Cathy O’Neil’s Weapons of Math Destruction. I spent the next several years focusing on teaching, somewhat as an attempt at a karmic cure.

When it comes to multi-agent systems, I definitely have favorites: I enjoy systems that thoughtfully employ adverseriality, asymmetric information, and agentic collusion.

I incorporated many of these techniques while designing this prototype system, which can readily be used as a general purpose anomaly detector, or binary classifier. 

This tool is especially useful in 2 cases: (1) when semantic information from the data needs to be taken into account-- such as a fraud detector that has built-in awareness that a purchase in the middle of the night might be suspicious, and (2) when interpretability and audit trails are necessary for the classification.

I've always really enjoyed a great data visualization, whether that is for education, featured in the news media, or for technical, research, or workplace consumption.

Over the years, I've gotten to create some insanely fun ones myself. Spanning from the microscopic-- my award-winning senior thesis that visually tracked the movement of dendritic cells in atheroscleretic patients-- to the cosmic-- applying 3D algorithms to astronomical blueshift data for the National Radio Astronomy Observatory (NRAO).

I wanted to share a few here, as well as any other miscellaneous work.

I gave these talks at the University of Virginia back in Feb 2020 (right before everything shut down).

I created and filmed Generative AI Is for Everyone!, a four-part, corporate training series designed to make generative AI understandable and useful to employees across KLA.

The approximately 2.5-hour series began with Journey to GenAI, an accessible introduction to how generative AI works and why it matters. It continued with Prompting Like a Pro, followed by more advanced instruction on enterprise AI tools, agents, and vibe coding.

More than 1,000 employees watched the first installment, and Generative AI Is for Everyone! became KLA’s most popular training offering that year.

Reach out to me if you are interested in more details about this curriculum. ~ orrico.elizabeth@gmail.com

My work with multi-agent systems goes back to 2015! I earned my Master's degree specializing in these systems at the University of Southern California. Check out some demo projects here