General Lab Information

Meifeng Lin

Distinguished Scientist/Group Leader, High Performance Computing, Computational Science Initiative

Meifeng Lin

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725, Room 2-167
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-4379
mlin@bnl.gov

Pronouns: she/her

Dr. Meifeng Lin is currently the High Performance Computing group lead at BNL's Computational Science Initiative. She holds a PhD in theoretical particle physics from Columbia University, and has worked in the field of computational physics and high performance computing for more than 10 years. Prior to joining BNL in 2013, she had held several research positions in Massachusetts Institute of Technology, Yale University, Boston University and Argonne National Laboratory. Her research interests include lattice QCD, nuclear and high energy physics, high performance computing and applications of machine learning and quantum computing. 

Expertise | Research | Education | Appointments | Publications | Highlights | Awards

Expertise

  • Lattice QCD
  • High Performance Computing
  • Numerical Simulations and Modeling
  • Computational Physics

Research Activities

  • Performance portability
  • Exascale computing
  • Physics-based quantum compiler optimization 
  • Machine Learning-enhanced simulations and data analysis

Education

  • Ph.D. Theoretical Particle Physics, Columbia University
  • M.Phil. / M.A., Physics, Columbia University
  • B.S. Physics, Peking University, Beijing, China

Professional Appointments

  • 07/2019 - present, Group Leader, High Performance Computing, Computational Science Initiative, Brookhaven National Laboratory
  • 10/2018 - present, Computational Scientist, Computational Science Initiative, Brookhaven National Laboratory
  • 11/2018 - 05/2019, Acting Group Leader, Quantum Computing, Computational Science Initiative, Brookhaven National Laboratory
  • 10/2016 - 09/2018, Associate Computational Scientist, Computational Science Initiative, Brookhaven National Laboratory
  • 11/2013 - 09/2016, Assistant Computational Scientist, Computational Science Initiative, Brookhaven National Laboratory
  • 03/2013 - 09/2013, Assistant Computational Scientist, Argonne Leadership Computing Facility, Argonne National Laboratory
  • 11/2012 - 03/2013, Postdoctoral Research Associate, Center for Computational Science, Boston University
  •  10/2009 - 09/2012, Postdoctoral Research Associate, High Energy Theory Group, Department of Physics, Yale University
  • 09/2007 - 09/2009, Postdoctoral Research Associate, Center for Theoretical Physics, Massachusetts Institute of Technology
  • 05/2017 - 05/2020, Adjunct Associate Professor, Stony Brook University

  • 02/2010 - 10/2015, Visiting Scientist, RIKEN Brookhaven Research Center

Selected Publications

  • Matekole ES, Fang Y-LL, Lin M (2022) Methods and Results for Quantum Optimal Pulse Control on Superconducting Qubit Systems. 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). https://doi.org/10.1109/ipdpsw55747.2022.00102
  • Mishra A, Chheda S, Soto C, et al (2022) COMPOFF: A Compiler Cost model using Machine Learning to predict the Cost of OpenMP Offloading. 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). https://doi.org/10.1109/ipdpsw55747.2022.00074
  • Bak S, Bertoni C, Boehm S, et al (2022) OpenMP application experiences: Porting to accelerated nodes. Parallel Computing 109:102856. https://doi.org/10.1016/j.parco.2021.102856
  • Chapman B, Pham B, Yang C, et al (2021) Outcomes of OpenMP Hackathon: OpenMP Application Experiences with the Offloading Model (Part II). Lecture Notes in Computer Science 81–95. https://doi.org/10.1007/978-3-030-85262-7_6
  • Yu HW, Bishai M, Gu WQ, et al (2021) Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network. Journal of Instrumentation 16:P01036–P01036. https://doi.org/10.1088/1748-0221/16/01/p01036
  • Dong Z, Gray H, Leggett C, et al (2021) Porting HEP Parameterized Calorimeter Simulation Code to GPUs. Frontiers in Big Data 4:. https://doi.org/10.3389/fdata.2021.665783
  • Yu H, Dong Z, Knoepfel K, et al (2021) Evaluation of Portable Acceleration Solutions for LArTPC Simulation Using Wire-Cell Toolkit. EPJ Web of Conferences 251:03032. https://doi.org/10.1051/epjconf/202125103032
  • Abramczyk M, Blum T, Izubuchi T, Jung C, Lin M, Lytle A, Ohta S, Shintani E (2020) Nucleon mass and isovector couplings in 2+1-flavor dynamical domain-wall lattice QCD near physical mass. Physical Review D. doi: 10.1103/physrevd.101.034510
  • Dong Z, Fang Y-LL, Huang X, Yan H, Ha S, Xu W, Chu YS, Campbell SI, Lin M (2018) High-Performance Multi-Mode Ptychography Reconstruction on Distributed GPUs. 2018 New York Scientific Data Summit (NYSDS). doi: 10.1109/nysds.2018.8538964
  • Boyle PA, Clark MA, DeTar C, Lin M, Rana V, Vaquero Avilés-Casco A (2018) Performance Portability Strategies for Grid C++ Expression Templates. EPJ Web of Conferences 175:09006. doi: 10.1051/epjconf/201817509006
  • Abeykoon SK, Lin M, Van Dam KK (2017) Parallelizing x-ray photon correlation spectroscopy software tools using python multiprocessing. 2017 New York Scientific Data Summit (NYSDS). doi: 10.1109/nysds.2017.8085042
  • Rana VS, Lin M, Chapman B (2016) A Scalable Task Parallelism Approach for LU Decomposition with Multicore CPUs. 2016 Second International Workshop on Extreme Scale Programming Models and Middlewar (ESPM2). doi: 10.1109/espm2.2016.008
  • Papenhausen E, Wang B, Langston MH, Baskaran M, Henretty T, Izubuchi T, Johnson A, Jung C, Lin M, Meister B, Mueller K, Lethin R (2015) Polyhedral user mapping and assistant visualizer tool for the r-stream auto-parallelizing compiler. 2015 IEEE 3rd Working Conference on Software Visualization (VISSOFT). doi: 10.1109/vissoft.2015.7332433
  • Appelquist T, Brower RC, Fleming GT, Kiskis J, Lin MF, Neil ET, Osborn JC, Rebbi C, Rinaldi E, Schaich D, Schroeder C, Syritsyn S, Voronov G, Vranas P, Weinberg E, Witzel O (2014) Lattice simulations with eight flavors of domain wall fermions in SU(3) gauge theory. Physical Review D. doi: 10.1103/physrevd.90.114502
  • Appelquist T, Brower RC, Buchoff MI, Cheng M, Cohen SD, Fleming GT, Kiskis J, Lin MF, Neil ET, Osborn JC, Rebbi C, Schaich D, Schroeder C, Syritsyn S, Voronov G, Vranas P, Wasem J (2013) Lattice calculation of composite dark matter form factors. Physical Review D. doi: 10.1103/physrevd.88.014502
  • Aoki Y, Arthur R, Blum T, Boyle PA, Brömmel D, Christ NH, Dawson C, Flynn JM, Izubuchi T, Jin X-Y, Jung C, Kelly C, Li M, Lichtl A, Lightman M, Lin MF, Mawhinney RD, Maynard CM, Ohta S, Pendleton BJ, Sachrajda CT, Scholz EE, Soni A, Wennekers J, Zanotti JM, Zhou R (2011) Continuum limit physics from2+1flavor domain wall QCD. Physical Review D. doi: 10.1103/physrevd.83.074508
  • Syritsyn SN, Bratt JD, Lin MF, Meyer HB, Negele JW, Pochinsky AV, Procura M, Engelhardt M, Hägler P, Hemmert TR, Schroers W (2010) Nucleon electromagnetic form factors from lattice QCD using2+1flavor domain wall fermions on fine lattices and chiral perturbation theory. Physical Review D. doi: 10.1103/physrevd.81.034507

Research Highlights

Turbocharging Data (2021) 

Accelerating Scientific Discovery Through Code Optimization on Many-Core Processors (2018)

Accelerated Computing Hackathon Returns for Second Year (2018) 

Brookhaven Lab Hosts "Brookathon," a Five-Day GPU Hackathon (2017) 

Awards & Recognition

Spotlight Awards, Brookhaven National Laboratory, 2016, 2017, 2018 

Faculty Fellowship, Columbia University, 2001-2003 

Meifeng Lin

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725, Room 2-167
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-4379
mlin@bnl.gov

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