Ryan Adams
Ryan Adams is an Assistant Professor in Harvard University’s School of Engineering and Applied Sciences. He was a CIFAR Junior Fellow from 2009 to 2011, supervised by Neural Computation and Adaptive Perception (NCAP) Fellow and Director Geoffrey Hinton and Fellow Brendan Frey at the University of Toronto. Ryan received a B.S. from the Massachusetts Institute of Technology in Electrical Engineering and Computer Science, before being awarded a Gates Cambridge Scholarship and an Overseas Research Scholarship to attend the University of Cambridge for a PhD in Physics. He completed his PhD in spring of 2009, under the supervision of Prof. David J.C. MacKay, an Associate of the NCAP program.
Ryan’s graduate research has focused on general statistical models for complex data. One of the challenges that all scientists face is how to make as few assumptions as possible when analyzing data. Typically, a difficult choice must be made between practicality and generality. In his research, Ryan has developed a new set of statistical methods for modeling natural phenomena that make minimal assumptions, while still being practical. His methods use Bayes’ Theorem, a formula for understanding how the probability of a hypothesis is affected by new pieces of evidence. Potential applications of this work exist in many areas: from protein modeling in drug development, to the distribution of galaxies in the cosmos, to the evolution of the human skull. As a Junior Fellow, Ryan will apply these sorts of methods to the study of computation in brain-like systems. His goal is not only to make more intelligent machines, but also to better understand how brains perform computation, with a specific focus on the visual system.
Ryan’s graduate research has focused on general statistical models for complex data. One of the challenges that all scientists face is how to make as few assumptions as possible when analyzing data. Typically, a difficult choice must be made between practicality and generality. In his research, Ryan has developed a new set of statistical methods for modeling natural phenomena that make minimal assumptions, while still being practical. His methods use Bayes’ Theorem, a formula for understanding how the probability of a hypothesis is affected by new pieces of evidence. Potential applications of this work exist in many areas: from protein modeling in drug development, to the distribution of galaxies in the cosmos, to the evolution of the human skull. As a Junior Fellow, Ryan will apply these sorts of methods to the study of computation in brain-like systems. His goal is not only to make more intelligent machines, but also to better understand how brains perform computation, with a specific focus on the visual system.