Biography

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Carl D. Laird

Principal Member of Technical Staff, Discrete Mathematics and Optimization, Center for Computing Research, Sandia National Laboratories

Associated Professor, Davidson School of Chemical Engineering, Purdue University

Email: cdlaird@sandia.gov

Biography:
Carl Laird works at Sandia National Laboratories and has an appointment in the Davidson School of Chemical Engineering at Purdue University. Dr. Laird's research interests include large-scale nonlinear optimization and parallel scientific computing. Focus areas include chemical process systems, power grid optimization, homeland security applications, and large-scale infectious disease spread. Dr. Laird is the recipient of several research and teaching awards, including the 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 and the IBM Bravo award for his work on IPOPT, a software library for solving nonlinear, nonconvex, large-scale continuous optimization problems. Dr. Laird earned his Ph.D. in Chemical Engineering from Carnegie Mellon in 2006 and his Bachelor of Science in Chemical Engineering from the University of Alberta.

Awards:
Outstanding Young Researcher, AIChE CAST Division [2015]
AIChE Professor of the Year, Student Chapter, Texas A&M University [2013]
Caterpillar Teaching Excellence Award [2011-2012]
Wilkinson Prize for Numerical Software [2011]
NSF Faculty Early Career Development (CAREER) Award [2010]
Top Cited Article in 'Discrete Optimization', 2005-2010 [2010], (see article)
Herb H. Richardson Fellowship [2010]
Appointed the holder of the William and Ruth Neely Faculty Fellowship in Chemical Engineering [2010]
TEES Select Young Faculty [2010]
Montague-Center for Teaching Excellence, Texas A&M University [2010]
Registered Student Organization New Advisor of the Year [2010]
Celanese Excellence in Teaching Award, Texas A&M University [2009]
AIChE Professor of the Year, Student Chapter, Texas A&M University [2009]
Poster Award, 10th International Symposium on Process Systems Engineering, PSE'09, Brazil [2009]
Federation of A&M Mother's Clubs, Yearbook Dedication for Outstanding Service to Students [2009]
The Physicians Center Guest Coach Honor, Texas A&M [2008]
IBM Research Bravo Award [2005]
ChEGSA Symposium Speaker Award [2003]
Mark Dennis Karl Outstanding Graduate Teaching Award [2002]
Canadian National Sciences and Engineering Research Council Fellowship [2000]
KITE Award, Red Deer College, Canada [1994]
Society of Petroleum Engineers Scholarship, University of Alberta, Canada [1994]
Alexander Rutherford Scholarship, Alberta, Canada [1993]

Undergraduate Courses
CHEN 320:  Numerical Analysis for Chemical Engineering
Applications of numerical analysis techniques to mathematical models of processes common to chemical and associated industries; computational methods and software for analysis of chemical engineering processes. Prerequisites: CHEN 205; MATH 308; or approval of instructor.

CHEN 461:  Process Dynamics and Control
An overview of process modeling & dynamics, process control, and control system analysis and design.  Prerequisite: CHEN 320.

Graduate Courses
CHEN 604:  Chemical Engineering Process Analysis
Development and analysis of chemical process models that involve systems of algebraic equations, ordinary differential equations and partial differential equations. Prerequisite: MATH 308 or approval of instructor.

CHEN 661:  Optimization of Chemical Engineering Processes
Methods of optimization applied for the design and control of chemical engineering processes. Prerequisite: Approval of instructor.

PETE 689/CHEN 689:  Special Topics in CO2 Capture and Uses: Sequestration, Enhanced Oil Recovery (EOR)
A basic description of the need and potential of CO2 Capture and Uses, including Sequestration and Enhanced Oil Recovery (CCS-EOR), and the scientific, technological, and economic aspects of identifying and implementing CCS-EOR projects. Students will learn the methodology and tools necessary to evaluate and quantify the potential, the uncertainties, and the risks involved in CCS-EOR.