Andrew Ferguson

  • Associate Professor of Molecular Engineering and Deputy Dean of Equity, Diversity, and Inclusion
  • Research and Scholarly Interests: Molecular Simulation, Statistical Thermodynamics, Machine Learning, Inverse Materials Design, Enhanced Sampling, Protein Folding, Self-Assembly
  • Websites: Ferguson Lab
  • Contact: andrewferguson@uchicago.edu
  • Assistant: Brandi Carr
  • Office Location:
    Pritzker School of Molecular Engineering
    University of Chicago
    5640 South Ellis Avenue
    Chicago, IL 60637
    ERC 389

Andrew Ferguson joined the Pritzker School of Molecular Engineering in July 2018 as an associate professor of immunoengineering. Prof. Ferguson received an M.Eng. in chemical engineering from Imperial College London in 2005, and PhD in chemical and biological engineering from Princeton University in 2010.

From 2010 to 2012 he was a postdoctoral fellow of the Ragon Institute of MGH, Massachusetts Institute of Technology, and Harvard in the Department of Chemical Engineering at MIT. He commenced his independent career in the department of Materials Science and Engineering at the University of Illinois at Urbana-Champaign in 2012, and was promoted to associate professor of materials science and engineering and chemical and biomolecular engineering in 2018.

Ferguson’s research uses computation and theory to understand and design self-assembling materials, macromolecular folding, and antiviral therapies. In his materials work, he applies nonlinear manifold learning to all-atom and coarse-grained simulations of polymers, peptides, and colloids to determine folding and assembly mechanisms and rational design principles. In his virology work, he developed a statistical inference procedure to translate viral sequence databases into empirical models of fitness, and coupled these landscapes with models of host-pathogen interaction to perform computational design of vaccine immunogens against HIV and hepatitis C virus.

In his enhanced sampling work, he combines tools from dynamical systems theory and nonlinear manifold learning to recover folding landscapes from experimentally- accessible molecular observables, and uses tools from deep learning for on-the-fly collective variable identification and accelerated recovery of molecular free energy landscapes in molecular simulation.

Ferguson is the recipient of a 2017 UIUC College of Engineering Dean’s Award for Excellence in Research, 2016 AIChE CoMSEF Young Investigator Award for Modeling and Simulation, 2015 ACS OpenEye Outstanding Junior Faculty Award, 2014 NSF Career Award, 2014 ACS PRF Doctoral New Investigator, and was named the Institution of Chemical Engineers North America 2013 Young Chemical Engineer of the Year.

Ferguson Lab uses tools from theory, computation, data science, and machine learning to understand macromolecular folding, engineer self-assembling colloids and peptides, and design antiviral therapies.