People

Carl D. Laird is a professor in the Department of Chemical Engineering at Carnegie Mellon University. Laird received his B.S. in chemical engineering from the University of Alberta in 2000 and his Ph.D. in chemical engineering from Carnegie Mellon University in 2006. He completed his postdoctoral studies in the Epidemiology Department at the University of Pittsburgh. Before joining CMU, Laird held faculty positions at Texas A&M and Purdue. He recently served as a principal member of technical staff in the Discrete Mathematics and Optimization, Center for Computing Research at Sandia National Laboratories in Albuquerque, New Mexico.

Laird brings an internationally recognized research program in the field of process systems engineering most known for contributions in high-performance computing techniques for large-scale nonlinear optimization and parallel scientific computing, open-source software development, and successful solution of problems in non-traditional, high-impact research areas, including public health, homeland security and critical infrastructure and energy systems. He is the recipient of several research and teaching awards, including INFORMS Computing Society Prize, CAST Division Outstanding Young Researcher Award, National Science Foundation Faculty Early Development (CAREER) Award and the Montague Center for Teaching Excellence Award. He is also a recipient of the prestigious Wilkinson Prize for Numerical Software for his work on IPOPT, a software library for solving nonlinear, nonconvex, large-scale continuous optimization problems.

Office
4210C Doherty Hall
Phone
claird@andrew.cmu.edu
Email
claird@andrew.cmu.edu
Google Scholar
Carl Laird
Websites
Carl Laird’s Website

Education

2006 Ph.D., Chemical Engineering, Carnegie Mellon University

2000 B.S., Chemical Engineering, University of Alberta

Media mentions


Imperial College

Collaboration leads to new open-source Python package

CMU ChemE Professor, Carl Laird, teamed up with researchers from Imperial College and Sandia National Labs to develop the new open-source Python package, OMLT, which provides various optimization formulations for machine learning models.