About Research

Life evolves through molecular substrates that are inseparable from information-based ones. Natural environments contain a diverse and vast amount of potentially useful signals. Yet, organisms become selectively attuned to specific subsets of those signals, rendering them biologically meaningful through representations and responses. I use cognition broadly to describe how living things detect information (sensing), encode information (memory), compresses, structures and generalizes from information (causal inference).

My central question is whether evolution draws on conserved computational “building blocks” of information-processing that are recombined and recur to produce diverse forms of cognition or whether similar capacities in different organisms are emergent and fundamentally different. My goal is to develop comparative ways of characterizing what is shared and what differs across biological and artificial systems, including their representations, dynamics, constraints, and energy-information tradeoffs. I seek to identifying the conditions under which different systems arise and contribute to empirical and theoretical frames that allow us to characterize differences in computational properties across kinds of substrates and scales.

I also work on epistemic questions about biology and computational modeling: when different models produce the same behaviors resembling life, what allows for deeper explanatory insights into biology? What assumptions about “what life is” and “what computation is” are baked in? Current dissertation work examines how living systems evade models and how where models fail may become a source of insight.