Theory and AI in Biology
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Vision: Mathematics is an unreasonably powerful language to describe and dissect natural phenomena. Wigner's article poetically conveys the sense of awe that many of us feel as mathematical and physical scientists. Mathematics isn't just a convenient language to communicate models of how some little corner of Life works; at its most potent it provides a general framework that organizes and illuminates a vast diversity of phenomena through abstraction. Pairing such theoretical abstractions with the power of modern computation and AI provides us an opportunity to understand, predict, and design complex living systems in unprecedented and beautiful ways. The group's vision is that prediction and computation-aided design need not come at the cost of intuitive human understanding, or vice-versa -- the two must be developed in synergy.
What we do: We work closely with experiments undertaken in long-term collaborating labs (on occasions by ourselves) and data generated by modern biological measurements. We do so in a diversity of biological settings, from proteins and cells to embryos and ecosystems. We view the breadth as necessary to distill unifying theoretical abstractions and data-driven approaches. We don't see ourselves as users of preexisting computational methods but are instead interested in developing new algorithms and frameworks suitable to further our understanding and design of living systems.
How we do it: Our scientific vision is a corollary to a human vision of working with young scientists to enjoy the pursuit of mathematics and computation, developing their talents and tastes.
