Living systems, from single cells to humans, receive information from the external world, and process that information to move and shape the processes within them. Information theory offers an excellent framework for placing limits on what an organism can sense, and serves as a design principle that can be used to rationalize what we observe in nature. Most organisms, however, self-organize and move, which breaks the limits of passive sensing and leads to a tight coupling between information processing and decision-making. Active information acquisition is an inherently non-stationary process, while organismal decision-making emerges as a sum of numerous microscopic computations occurring out of equilibrium, rendering canonical thermodynamic descriptions inapplicable. Our hope is to find a common mathematical framework that offers testable predictions and normative explanations for empirical decision-making phenomena, much like the role information theory has played for describing passive sensory systems. 

Additional complexity arises from the fact that living systems are not static but constantly learn, that is, make long-term changes to their core processes that allow them to adapt and anticipate future scenarios. Most biological learning operates in "high-dimensions" -- the number of tunable parameters is often exceedingly large, and the sensory-action response functions involve high-dimensional sensory inputs (say, from diverse receptors) and high-dimensional action outputs (say, control of muscles or gene regulation). A (seemingly) fundamental limitation of learning in this regime is that the larger the number of knobs one can tune, the longer it takes to find the optimal solution. However, this "limitation", known as the curse of dimensionality, does not appear to apply in practice -- modern machine learning models and extant biological complexity are prime examples. Presumably, most high-dimensional systems, through learning, acquire implicit low-dimensional structure that reflects the task they are meant to solve. How is the curse of dimensionality circumvented in high-dimensional learning systems and under what conditions does low-dimensional structure emerge? 

Learning by itself is not enough -- living systems need to learn quickly -- to survive, populations should rapidly acquire strongly beneficial mutations before they go extinct and animals should rapidly learn the structure of the environment that they are born into. How should biological systems be organized so that they learn quickly? Much like meta-trained machine learning systems, biological systems encounter and survive a diverse array of environments ("tasks") on organismal and evolutionary timescales. Does learning across many tasks over long timescales create a scaffold for adaptation on shorter timescales? 

We are a group of physicists contributing towards addressing these foundational questions in biological physics through a combination of theory and data-driven modeling. We collaborate closely with both machine learning researchers and experimental biologists. Machine learning models are excellent 'experimental' systems to probe principles of high-dimensional learning and test out new conceptual ideas. We believe that understanding how these models work will get us further along the path towards understanding how life works.