Dana Randall

Bio:  Dr. Dana Randall is a Regents’ Professor in the school of computer science, an Adjunct Professor of mathematics, and the Associate Dean for Access and Advancement in the College of Computing at the Georgia Institute of Technology.  She previously served as the inaugural co-executive director of the Institute for Data Engineering and Science (IDEaS) that she co-founded, director of the Algorithms and Randomness Center (ARC), and the ADVANCE Professor of Computing at Georgia Tech.  She received an AB in mathematics from Harvard University and a Ph.D. in computer science from the University of California at Berkeley before holding postdoctoral positions at the Institute for Advanced Study and the Center for Discrete Mathematics and Theoretical Computer Science, in Princeton. 
 
Dr. Randall is a Fellow of the Association of Computing Machines (ACM) and the American Mathematical Society (AMS), a National Associate of the National Academies, and was formerly a Sloan Foundation fellow and NSF CAREER Award recipient.  Her research lies in randomized algorithms, forming bridges with discrete mathematics and statistical physics.  She is currently the P.I. on an ARO Multidisciplinary University Research Initiative (MURI) on the Formal Foundations of Emergent Computation and Algorithmic Matter.

Research Interests: My research lies in the field of randomized algorithms, primarily exploring how random sampling can be utilized rigorously as an algorithmic tool.  Algorithms using random sampling, such as Markov chain Monte Carlo methods, have found widespread use across many areas of science, computation, and engineering, including programmable matter, which seeks to program collections of computationally bounded, locally interacting agents to achieve desirable global coordination.  

Self-organization of collectives is a phenomenon whereby unanticipated global configurations and patterns of activity emerge from fully distributed and simplistic rules implemented by each individual agent, without any global coordination or external intervention. Such emergent behaviors arising from self-organization can be seen across many domains, including distributed systems and swarm robotics in computer science, nonequilibrium and equilibrium systems in physics, population dynamics in biology, autonomous systems in robotics, and smart materials, among many others. Our goal is to provide a rigorous unifying framework for predicting and programming ensemble-level capabilities from active agents interacting through local sensing and communication.