Algorithmic Self-Assembly and Natural Computing

Natural systems exist which are capable of accomplishing amazingly complex feats, from self-assembling intricate structures from the bottom up, molecule by molecule (e.g. the formation of snowflake crystals), to performing difficult computations (e.g. immune system identification of never before encountered pathogens). Studying such systems provides researchers with insights into a variety of fascinating domains such as the origin of life, the functioning of biological systems, and methods for creating complex materials. They have also provided inspiration for the design of artificial self-assembling systems, especially those designed to be algorithmically directed, and those which can compute using bio-molecules such as DNA.

Our group both mathematically and computationally models algorithmic self-assembling systems, and works with several laboratories which are currently building them to provide valuable insights via computational theory. While much of our work is based on theoretical computer science, it also requires interdisciplinary collaboration with chemists and biochemists, mathematicians, etc. Additionally, our computational modeling requires expertise in high-performance and distributed computing. Our students include both undergraduate and graduate students with a variety of educational backgrounds.

Please see our web site at or contact us for more information.