Mathew Titus, Ph.D.

Pictured here with Alison (wife) and Charles (son)

I love language and abstraction. It was very natural for me to gravitate towards mathematics and computing, studying probability theory and diffusion processes in grad school before working in machine learning during my postdoc. This was just a few years before artificial intelligence started showing up in every app and computers learned to speak and draw.

While AI is enabling a flurry of new activity in computing, business, and the basic sciences, it has a long way to go before it conquers all our quantitative sciences. Areas with small and messy datasets such as the water sector demonstrate that physical models and Bayesian statistics remain important pillars of thought in the applied sciences. The carefully drawn thought continues to quietly carry its weight next to the raw power of billion-parameter transformer models and their terabytes of training data.

The arenas of probability theory, statistics, physical modeling, data science, and artificial intelligence all contribute different philosophies and toolsets for dealing with uncertainty in the natural world.


I grew up in rural eastern Oregon, the second of three boys, sixteen miles outside of a town of about 2000 people. My father has collected a hat for every season - ranchhand, hunting guide, firefighter, writer, and pastor - while my mother has worked covering all administrative bases for a local company for nearly 30 years.

Blessed with being an introverted intuitive type (MBTI may not predict your success, but it draws a pretty fine bead on me), the high desert left me with a lot of time that I spent learning and listening to punk rock.


Outside of work I spend my time doing all the things I wanted to be doing during grad school: running, gardening, roasting chickens, playing Outer Wilds, and reading.