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Francis (Frank) J. Alexander

Deputy Director, Computational Science Initiative

Expertise

Prior to joining Brookhaven National Laboratory in 2017, Francis “Frank” Alexander spent nearly 20 years at Los Alamos National Laboratory, finishing his tenure as the acting division leader of the lab’s Computer, Computational, and Statistical Sciences (CCS) Division. At Los Alamos, he grew in several leadership roles, including serving as deputy leader of CCS Division’s Information Sciences Group and leader of the Information Science and Technology Institute. Frank was introduced to the DOE national laboratory complex during his postdoctoral work with Los Alamos’ Center for Nonlinear Studies and the Institute for Scientific Computing Research at Lawrence Livermore National Laboratory. He also was a research assistant professor at Boston University’s Center for Computational Science. Frank has led many research projects and has published more than 50 papers in peer-reviewed journals.

Computational Sciences Research Activities

  • Optimal Design of Experiments
  • Computational Physics
  • Nonequilibrium Statistical Mechanics

Education

  • Ph.D., Physics, Rutgers, The State University of New Jersey, 1991
  • B.S., Physics and Mathematics, The Ohio State University, 1987

Professional Appointments

Brookhaven National Laboratory

  • 2017-Present, Deputy Director, Computational Science Initiative

Los Alamos National Laboratory

  • February 2016-January 2017, Acting Division Leader—Computer, Computational and Statistical Sciences (CCS) Division
  • 2010-2016, Deputy Division Leader—CCS Division (including time in acting position)
  • 2012-2015, Institute Leader, Information Science and Technology Institute
  • 2008-2012, Center Leader, Information Science and Technology Center (merged with Information Science and Technology Institute in 2012)
  • 2007-2008, Group Leader, Information Sciences Group (CCS-3)
  • 2002-2006, Deputy Group Leader, Modeling, Algorithms and Informatics Group (CCS-3)
  • 2000-2006, Team Leader, Information Physics and Modeling Team
  • 1998-2017, Technical Staff Member
  • 1991-1993, Postdoctoral Researcher, Center for Nonlinear Studies

Boston University

  • 2000-2002, Adjunct Associate Professor, Center for Computational Science
  • 1995-1998, Research Assistant Professor, Center for Computational Science

Lawrence Livermore National Laboratory

  • 1993-1995, Postdoctoral Researcher, Institute for Scientific Computing Research

Selected Publications & Research Highlights

Lookman T, FJ Alexander, and AR Bishop (2016). Perspective: Codesign for Materials Science: An Optimal Learning Approach. APL Materials 4(5):05350 DOI: 10.1063/4944627.

Lookman T, FJ Alexander, and K Rajan (Eds.) (2016). Information Science for Materials Discovery and Design, Springer Series in Material Science, vol. 225. Springer International Publishing, Switzerland. DOI: 10.1007/978-3-319-23871-5.

Lookman T, P V Balachandran, D Xue, G Pilania, T Shearman, J Theiler, JE Gubernatis, J Hogden, K Barros, E BenNaim, and FJ Alexander (2016). A Perspective on Materials Informatics: State-of-the-Art and Challenges, Chapter 1 in Information Science for Materials Discovery and Design, Springer Series in Material Science, vol. 225, pp. 3-12. Springer International Publishing, Switzerland. DOI: 10.1007/978-3-319-23871-5.

Dougherty ER, LA Dalton, and FJ Alexander (2015). Small Data is the Problem. 49th Asilomar Conference on Signals, Systems and Computers, pp. 418-422. Online at: http://toc.proceedings.com/29658webtoc.pdf.

Taverniers S, TS Haut, K Barros, FJ Alexander, and T Lookman (2015). A Physics-based Statistical Learning Approach to Mesoscopic Model Selection. Physical Review E 92(5):053301. DOI: 10.1103/PhysRevE.92.053301.

Alexander FJ and C Meneveau (2015). Open Simulation Laboratories[Guest Editor’s Introduction]. Computing in Science & Engineering 17(5):7-9. DOI: 10.1109/MCSE.2015.99.

Taverniers S, FJ Alexander, and DM Tartakovsky (2014). Noise propagation in hybrid models of nonlinear systems: The Ginzburg–Landau equation. Journal of Computational Physics 262:313-324. DOI: 10.1016/j.jcp.2014.01.015.

Alexander FJ (2013). Machine Learning [Guest Editor’s Introduction]. Computing in Science & Engineering 15(5)9-11. DOI: 10.1109/MCSE.2013.107.

Alexander FJ and T Lookman (2013). Novel Approaches to Statistical Learning in Material Science, Chapter 3 in Informatics for Materials Science and Engineering, K. Rajan (ed.), pp. 37-52. Butterworth-Heinemann (Elsevier), Oxford, UK. DOI: 10.1016/B978-0-12-394399-6.00003-5.

Monteleoni C, GA Schmidt, F Alexander, A Niculescu-Mizil, K Steinhaeuser, M Tippett, A Banerjee, MB Blumenthal, AR Ganguly, JE Smerdon, and M Tedesco (2013). Climate Informatics, Chapter 4 in Computational Intelligent Data Analysis for Sustainable Development, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, T Yu, NV Chawla, and S Simoff (eds.), pp. 81-126. CRC Press (Taylor & Francis Group), Boca Raton, Florida.