Link to Resume: (PDF)
Over more than 15 years in scientific computing, I have gained extensive managerial experience across large organizational structures, including line management at CSAID, international collaborations such as CMS, and major U.S. operations programs.
Since August 2024, I have served as the Deputy Associate Lab Director (interim) for CSAID at Fermilab. In addition, since November 2024, I have held the roles of Acting Deputy Division Director for the Scientific Computing Systems and Services Division and Department Head for the Facility Evolution Department.
From March 2019 to December 2024, I was appointed U.S. CMS Software and Computing Operations Program Manager. This program, jointly funded by the National Science Foundation (NSF) and the Department of Energy (DOE), provides the software and computing infrastructure that enables U.S. CMS researchers to maintain leadership in CMS physics. Under my leadership, the program’s annual budget grew from 15M U.S. Dollars in 2019 to nearly 20M U.S. Dollars in 2024, supporting more than 60 FTEs, including scientists, technical staff, and computing hardware across Fermilab and seven U.S. university sites.
I am actively engaged in recruiting and sustaining a vibrant, high-performing workforce. I support this goal by building career pipelines through internship programs and by mentoring junior technical staff, early-career scientists, and postdoctoral researchers.
Leveraging my extensive expertise in scientific software and computing infrastructure, I contribute to global community efforts to shape the future of software and computing for science, with a particular focus on the High-Luminosity LHC (HL-LHC). My leadership centers on providing the strategic vision needed to enable groundbreaking scientific discoveries.
For HL-LHC, I co-authored a strategic plan for the U.S. CMS Software & Computing Operations Program (arXiv:2312.00772) that outlines four grand challenges that must be addressed:
To implement this plan, I established a Research Initiative within the U.S. CMS Software & Computing Operations Program. This initiative provides partial funding for postdoctoral researchers to explore novel and forward-looking solutions to these four grand challenges. The strategic plan also builds on prior contributions to broader community planning exercises, such as the Roadmap for HEP Software and Computing R&D for the 2020s.
In 2024, I was appointed co-lead of the CMS Collaboration Board Sub-Group for Offline & Computing for HL-LHC. I proposed the creation of this sub-group to introduce structured coordination and effort planning within the Offline & Computing area—an innovation for CMS, where such formal coordination had previously only existed in detector projects through institutional boards.
At Fermilab, I created the Computing Resources Evolution STrategy (CREST) process, which defines a ten-year strategic plan for evolving the lab’s computing resources. This plan considers both current experimental needs and anticipated demands from DUNE and HL-LHC.
As part of the Department of Energy’s Center for Computational Excellence (HEP-CCE), I co-authored a proposal in 2020 to lead four sub-projects addressing key challenges in adapting HEP computing for heterogeneous architectures:
I was appointed technical lead of the PPS sub-project, which has since completed its funding cycle.
I am nationally and internationally recognized for my leadership. I was selected to co-lead the Computational Frontier for the Snowmass 2021 process, the U.S. particle physics community planning exercise. I also serve on the editorial boards of the journal “Computing and Software for Big Science” and the European Physical Journal (EPJ C).
I possess deep expertise in planning, developing, maintaining, and operating distributed computing infrastructures that provide access to several hundred thousand computing cores and hundreds of petabytes of disk storage. I am proficient in efficiently storing and retrieving data from permanent tape storage. I am intimately familiar with both High-Throughput Computing (HTC) and High-Performance Computing (HPC), including scientific grid sites, academic and commercial clouds, as well as the largest supercomputers at HPC centers in the U.S. and worldwide. This infrastructure supports scientific software composed of millions of lines of C++ and Python code, essential for extracting physics results. I am an expert in object-oriented software development, statistical data analysis methods, Monte Carlo simulation techniques, and various optimization and machine learning approaches.
The technical components of my work are tightly linked to scientific research, enabling the analysis of particle physics detector data and simulations as the foundation for producing physics results. My active engagement in High Energy Physics (HEP) research allows me to guide the scientific community in leveraging the latest computing innovations, effectively bridging the domains of science and scientific computing.
My current R&D projects include:
I am a particle physicist at heart, originally motivated by conducting leading-edge research into New Physics Beyond the Standard Model, as well as precision measurements within the Standard Model.
I have many years of experience analyzing high-energy collisions from various particle colliders using a wide range of techniques. I have published numerous papers in leading journals and am currently a member of the CMS collaboration operating one of the four detectors at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland. The CMS collaboration is a global endeavor, comprising more than 3000 physicists from over 50 countries, including over 1000 students.
In my past LHC research, I have led searches for evidence of physics beyond the Standard Model using top quarks and contributed to investigations of Supersymmetry and Dark Matter. Among my most notable publications is the Observation of the Higgs Boson in 2012, where my work in scientific computing played a significant role.
A.M. Sirunyan et al., Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in protonproton collisions at $\sqrt{s}=13{Te}{V}$, Eur. Phys. J. C. 79 (2019) 280, doi:10.1140/epjc/s10052-019-6730-7, arXiv:1811.06562 [hep-ex]
V. Khachatryan et al., Measurements of $t \bar t$ charge asymmetry using dilepton final states in pp collisions at $\sqrt s=8$ TeV, Phys. Lett. B. 760 (2016) 365–386, doi:10.1016/j.physletb.2016.07.006, arXiv:1603.06221 [hep-ex]
A. Apresyan et al., Detector R&D needs for the next generation $e^+e^-$ collider, (2023). http://arxiv.org/abs/2306.13567, arXiv:2306.13567 [hep-ex]
M. Atif et al., Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics, (2023). http://arxiv.org/abs/2306.15869, arXiv:2306.15869 [hep-ex]
B. Bockelman et al., IRIS-HEP Strategic Plan for the Next Phase of Software Upgrades for HL-LHC Physics, (2023). http://arxiv.org/abs/2302.01317, arXiv:2302.01317 [hep-ex]
V.D. Elvira et al., The Future of High Energy Physics Software and Computing, in: Snowmass 2021, 2022, doi:10.2172/1898754, arXiv:2210.05822 [hep-ex]
M. Bhattacharya et al., Portability: A Necessary Approach for Future Scientific Software, in: Snowmass 2021, 2022. http://arxiv.org/abs/2203.09945, arXiv:2203.09945 [physics.comp-ph]
J. Balcas et al., Automated Network Services for Exascale Data Movement, EPJ Web Conf. 295 (2024) 01009, doi:10.1051/epjconf/202429501009
O. Gutsche et al., The U.S. CMS HL-LHC R&D Strategic Plan, EPJ Web Conf. 295 (2024) 04050, doi:10.1051/epjconf/202429504050, arXiv:2312.00772 [hep-ex]
K.H.M. Kwok et al., Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels, EPJ Web Conf. 295 (2024) 11003, doi:10.1051/epjconf/202429511003, arXiv:2401.14221 [physics.acc-ph]
N. Smith et al., A Ceph S3 Object Data Store for HEP, EPJ Web Conf. 295 (2024) 01003, doi:10.1051/epjconf/202429501003, arXiv:2311.16321 [physics.data-an]
N. Smith et al., Coffea: Columnar Object Framework For Effective Analysis, EPJ Web Conf. 245 (2020) 06012, doi:10.1051/epjconf/202024506012, arXiv:2008.12712 [cs.DC]
J. Albrecht et al., A Roadmap for HEP Software and Computing R&D for the 2020s, Comput. Softw. Big Sci. 3 (2019) 7, doi:10.1007/s41781-018-0018-8, arXiv:1712.06982 [physics.comp-ph]
published on: 01. September 2025