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Meifeng Lin

Department Chair, Computational Science, Computational Science Initiative

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725, Room 2-102
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-4379
mlin@bnl.gov

Pronouns: she/her

Dr. Meifeng Lin is currently the chair of the Computational Science Department in the Computing and Data Sciences Directorate at BNL. She previously led the High Performance Computing group at BNL's Computational Science Initiative from 2019 to 2024. 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 15 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

Lattice QCD
High Performance Computing
Numerical Simulations and Modeling
Computational Physics
Digital Twins

Research Activities

High Performance Computing (HPC)

High Energy Physics Center for Computational Excellence (DOE HEP), Task Lead at BNL, 2019-present
Overcoming Computational Bottlenecks with HPC and ML (BNL LDRD), PI, 2021-2024
Exascale Lattice Gauge Theory (DOE ASCR Exascale), Senior Personnel, 2016-2023
Performance Portability Strategies for HPC Applications (SBU-BNL Seed Grant), BNL PI, 2016-2017
PUMA-V for LQCD Software Optimization (DOE SBIR/STTR), BNL PI, 2013-2015

Quantum Computing

Quantum-Centric Supercomputing (BNL LDRD), Co-PI, 2023-2026
Quantum Chromodynamics with Quantum Computers (DOE NP), Co-PI, 2023-2026
Quantum Machine Learning for NISQ Devices (BNL LDRD), Co-PI, 2019-2022
Physics-Guided Optimization of Quantum Gates (BNL LDRD), PI, 2018-2021

Machine Learning (ML)

Transformative Cloud Modeling through ML (BNL LDRD), Co-PI, 2024-2026
AI-assisted Detector Design for EIC (DOE NP), Key Personnel, 2023-2025
Cycle-Consistent Generative Models for Simulations and Experiments (BNL LDRD), Co-PI, 2021-2024
High Performance X-ray Diffraction Simulation with GPU and CPU (BNL LDRD), Co-PI, 2017-2019

Education

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

Professional Appointments

01/2025 - present, Department Chair, Computational Science, Computing and Data Sciences Directorate, Brookhaven National Laboratory
02/2024 - present, Distinguished Computational Scientist, Computational Science Initiative, Brookhaven National Laboratory
07/2019 - 12/2024, Group Leader, High Performance Computing, Computational Science Initiative, Brookhaven National Laboratory
10/2020 - 01/2024, Senior Computational Scientist, Computational Science Initiative, Brookhaven National Laboratory
10/2018 - 09/2020, 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

Alexeev Y, Amsler M, Barroca MA, et al (2024) Quantum-centric supercomputing for materials science: A perspective on challenges and future directions. Future Generation Computer Systems 160:666–710. https://doi.org/10.1016/j.future.2024.04.060
Zhang T, Li L, López-Marrero V, et al (2024) Emulator of PR-DNS: Accelerating Dynamical Fields With Neural Operators in Particle-Resolved Direct Numerical Simulation. Journal of Advances in Modeling Earth Systems 16:. https://doi.org/10.1029/2023ms003898
Diefenthaler M, Fanelli C, Gerlach LO, et al (2024) AI-assisted detector design for the EIC (AID(2)E). Journal of Instrumentation 19:C07001. https://doi.org/10.1088/1748-0221/19/07/c07001
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
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
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