I'm Adam Hyland, a PhD student working in Charlotte Lee's lab in the department of Human Centered Design and Engineering at the University of Washington. I am interested in how communities of practice (often but not always engineering communities) coordinate around standards, standards processes, and things which look a bit like standards but aren’t. I care a lot about making the invisible parts of systems all around us easier to understand. Not just so we become better informed (which is cool) but so we are equipped to play with, break, and transform those systems (which is way cooler).

--------------

Areas of work:

Computer Arithmetic: My interest in computer arithmetic is in demonstrating and teaching the dramatic impact and importance of this invisible, negotiated, coordinated part of our world. Arithmetic on computers has moved from a central part of programmer training to being supplied by a vast array of libraries and standardized interfaces. Showing how this embedding works and has changed us as users, programmers, and designers is my goal.

AI Image Generation: I work to help designers and artists understand the promise and limitations of machine image generation, principally through exploring and experimenting with abberant and adversarial image prompts. Doing so is effective in helping people care about and inspect their functioning, which is increasingly critical as more and more of our visual culture is generated by these systems.

Interpretability and Robustness of Large Language Models: My work with Ruoxi Shang investigates new challenges with Large Language Models (LLMs) like GPT-4 and Llama as trustworthy interfaces to computing. Making sense of how model output can be understood (interpretability) and how models can be protected from manipulated input (robustness) is crucial for ensuring aligned behavior in high-stakes, high-complexity tasks, more of which are being turned over to LLMs every day.

--------------

Peer Reviewed Publications:

--------------

Teaching:

I teach Information Visualization, a complex and tool-laden topic like so much in Human-Computer Interaction. My approach--which I feel helps students remain curious in a space like this--is to show that struggling with tools (many of which represent thousands upon thousands of person-hours of work) is not their fault. Really excellent visualization and in many ways, the kinds of personal growth we sometimes call 'learning', can only occur if we productively struggle together.

Courses taught:

--------------

Other:

--------------

Contact: achyland @ UW 'dot' edu, or find me on LinkedIn