Juan C Meza

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Juan C. Meza
BornAugust 2, 1956
Houston, Texas, U.S.
CitizenshipUnited States
Alma materRice University
AwardsACM Gordon Bell Award, Blackwell-Tapia Prize, Fellow of American Mathematical Society, Fellow of Society for Industrial and Applied Mathematics, Fellow of American Association for the Advancement of Science
Scientific career
FieldsApplied Mathematics
InstitutionsSandia National Laboratories; Lawrence Berkeley National Laboratory; University of California, Merced; National Science Foundation
Websitehttps://www.juancmeza.com/

Juan C Meza (born Aug 2, 1956) is a Latino American applied mathematician specializing in numerical analysis and numerical optimization. He is a fellow of the Society for Industrial and Applied Mathematics (SIAM), American Mathematics Society (AMS), and the American Associate for the Advancement of Science (AAAS). He is currently Professor of Mathematics at University of California, Merced.

Early Life

Juan C. Meza was born in Houston, Texas in 1956, to Mexican immigrants Carmen and Camilo Meza.[1]. He and his family moved the family back to Mexico in 1956 before returning to Houston in 1965. Juan attended to public schools in the Houston area, graduating from Milby High School in 1974.[2] His first summer out of high school, he found an internship at NASA and was exposed to computing, which was a very formative experience.[1]

Education

He was awarded a National Merit Scholarship.[2] He received a B.S. and M.S. degrees in Electrical Engineering and Computer Science from Rice University in 1978 and 1979 respectively.[2] After receiving his degrees, he moved to California to work for Amdahl Corporation as a systems design engineer and then returned to Houston to work for Exxon Production Research as a research engineer. After several years in industry, he returned to school for graduate studies his Ph.D. in Computational & Applied Mathematics in 1986 from Rice University.[2]

Meza was one of six students to receive a PhD in Mathematics as a US-born Latino at this time.[NEEDS CITATION]

Career

From 1987 to 2002, he was a researcher and research manager at Sandia National Laboratories in Livermore, California, including a one-year assignment in 2000 as a Senior Technical Advisor to the Department of Energy NNSA Advanced Simulation and Computing Program.[3] During his time at Sandia, he developed parallel optimization software and worked on semiconductor device simulation.[2]

From 2002 to 2011, he worked at Lawrence Berkeley National Laboratory as Department Head for the High-Performance Computing Research Department, leading of 250+ employees and a budget of $50M. He was also a Senior Scientist.[3]

From 2011 to 2017, he was Dean of the School of Natural Sciences at University of California Merced[3].

From 2018-2022, he worked at National Science Foundation as Director for the Division of Mathematical Sciences (DMS), leading a team of 28 program managers overseeing $245M in research grants for the mathematical sciences.[3] As DMS Director, he advocated for research in fundamental mathematics and on the power of mathematics for understanding the world[4]. Also during this time, he served as the Co-Chair for the Steering Committee of the Harnessing the Data Revolution, one of NSF’s 10 Big Ideas, that implemented the strategic vision and implementation plan for the allocation of $300M in funds to universities throughout the U.S.[3]

Since 2022, Dr. Meza is Professor of Applied Mathematics at the University of California, Merced.[3]

Diversity and Outreach

Meza is a life member of the Society for the Advancement of Chicanos/Hispanics and Native Americans in Science (SACNAS) and served on its Board from 2011-2014.

In an interview on Texas Math Mundo, Meza discussed some of the challenges and opportunities for underrepresented groups in STEM[5] and the case for increasing diversity to maintain a strong research culture in the US.

He received the 2008 Blackwell–Tapia prize|Blackwell-Tapia Prize and the 2008 SACNAS Distinguished Scientist Award [6]. He was also named to Hispanic Business Magazine’s Top 100 Influential Hispanics in 2009 and was one of only two scientists to make the list.[7] He was named one of the Top 200 Most Influential Hispanics in Technology in 2011 by Hispanic Engineer and Information Technology Magazine.[8]

Honors

  • Fellow of the American Mathematical Society (AMS), 2021, for "contributions to the mathematical profession through leadership at the national level and to scientific computing and applications"[9] and the first AMS Fellow from University of California, Merced[10]
  • Fellow of the Society for Industrial and Applied Mathematics, 2019, for "contributions to optimization methods and software applied to challenging real-world problems, technical leadership, and service to the SIAM community" [11]
  • Fellow of the American Association for the Advancement of Science, 2010, for "exemplary service to the federal energy laboratories and professional societies in enhancing research and research participation." [12]
  • Blackwell-Tapia Prize, 2008 for "effective leadership and mentorship in the field of applied mathematics" [6]
  • SACNAS Distinguished Scientist Award, 2008 [6]
  • ACM Gordon Bell Prize, 2008 [13][14]

Research

Professor Meza has research interests in numerical analysis and numerical optimization, particularly derivative-free optimization, and parallel computing. He has worked on a wide-range of applications including: problems in nanotechnology, density functional theory, reliability of the electric power grid, and semiconductor device modeling. More recently, he has been working on developing mathematical models for predicting antibiotic resistance.

Antibiotic Resistance

Antibiotic resistance due to the abuse of drugs has led to issues in developing new treatments. There is a need to understand the relationship between antibiotic use and the evolution of resistance. Professor Meza's research is focusing on creating data-driving mathematical models to develop new treatments that can hinder the evolution of drug resistance.

In a paper published in 2015, Professor Meza discusses the findings of his research:

"[The] results show that great potential exists for remediation of antibiotic resistance through antibiotic treatment plans when pleiotropic fitness costs are known for an appropriate set of antibiotics. While developed using a model of Gram-negative antibacterial resistance, this approach could also be used for Gram-positive bacteria and HIV treatment plans".[15]

Machine Learning

Machine learning and large datasets have allowed for the discovery of new patterns and policies in science. Professor Meza is involved in machine learning research, using it to create new mathematical algorithms for pattern detection in datasets. Specifically, Professor Meza is interested in ensemble methods. Ensemble methods are used to predict class labels with groups of simple base learners. The issue with these methods are that they are not good at detecting patterns or the structure of a large dataset. In a paper published in 2011, Professor Meza and his coworkers compared the rule ensemble method on a set of multi-class problems with boosting and bagging. These are ensemble techniques that use decision trees as base learners. This numerical study found that tree ensemble methods work better on multi-class problems because of their use of multi-class decision trees.

Density Functional Theory

Density Functional Theory is a technique used in computational chemistry and material simulations. It is used to calculate material properties, electronic structure, charge density, and total energy of electronic systems. Professor Meza's research on DFT focuses on developing mathematical algorithms to study large systems of atoms. The applications of the research extend to the study of solar cells, renewable energy, medical imaging, and the design of materials. Such applications in the field of electronics often requires the use of nonlinear eigenvalues, which can be quite difficult to solve for. In 2009, Professor Meza published a paper that looked into the possibility of applying the standard Newton method. In the research he found that:

"When viewed as a system of nonlinear equations, this type of problem has a very large and dense Jacobian matrix. However, the Jacobian matrix has certain low rank structure that allows us to solve the Newton correction equation efficiently using an iterative solver".[16]

References

  1. ↑ 1.0 1.1 Small, Ashley (2022-05-23). "How Diversity in Computing Impacts Innovation: A Conversation With Tapia Award Recipient Dr. Juan Meza". CMD-IT. Retrieved 2022-12-04.
  2. ↑ 2.0 2.1 2.2 2.3 2.4 "EAC Focus - Juan C. Meza". www.crpc.rice.edu. Retrieved 2023-06-02.
  3. ↑ 3.0 3.1 3.2 3.3 3.4 3.5 Meza, Juan C. "Dr. Juan C. Meza" (PDF) (Curriculum Vitae). Retrieved November 8, 2022.
  4. ↑ https://www.openaccessgovernment.org/mathematics-a-powerful-tool-for-understanding-the-world/47052/
  5. ↑ https://www.youtube.com/watch?v=c_5rpAX1j-0
  6. ↑ 6.0 6.1 6.2 "Juan Meza Wins 2008 Blackwell-Tapia Prize and SACNAS Distinguished Scientist Award". Berkeley Lab Computing Sciences. Retrieved 2023-06-02.
  7. ↑ "Juan Meza Named One of Hispanic Business Magazine's "100 Influentials"". Berkeley Lab Computing Sciences. Retrieved 2023-06-02.
  8. ↑ "Juan Meza is Named One of Top 200 Most Influential Hispanics in Technology". today.lbl.gov. Retrieved 2023-06-02.
  9. ↑ "Fellows of the American Mathematical Society". American Mathematical Society. Retrieved 2023-06-02.
  10. ↑ https://www.ams.org/cgi-bin/fellows/fellows.cgi#m
  11. ↑ https://www.siam.org/prizes-recognition/fellows-program/all-siam-fellows/class-of-2019
  12. ↑ "Berkeley Lab's Juan Meza Elected Fellow of AAAS". Berkeley Lab Computing Sciences. Retrieved 2023-06-02.
  13. ↑ https://www.eurekalert.org/news-releases/635118
  14. ↑ "ACM Gordon Bell Prize". awards.acm.org. Retrieved 2023-06-02.
  15. ↑ Mira, Crona, Greene, Meza, Sturmfels, Barlow (2015). "Rational Design of Antibiotic Treatment Plans: A Treatment Strategy for Managing Evolution and Reversing Resistance". PLOS ONE. 10 (5): e0122283. doi:10.1371/journal.pone.0122283. PMC 4422678. PMID 25946134.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  16. ↑ Gao, Meza, Yang (2009). Solving a Class of Nonlinear Eigenvalue Problems by Newton's Method. Lawrence Berkeley National Laboratory. OCLC 727360553.{{cite book}}: CS1 maint: multiple names: authors list (link)

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