HPC Researcher Spotlight
The department of Research Computing would like to spotlight the following researchers who utilized the High Performance Computing system at 黑料网.
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Marco Guzzi, Ph.D., Associate Professor of Theoretical Particle Physics
Determination of proton distribution functions (PDFs) of the proton within the CTEQ collaboration.
Calculations of theory predictions for Top-quark pair production at the LHC.
Analysis of the recent Charm and bottom quark production data at HERA.
Search of extra neutral currents at hadron colliders.
QCD Factorization in presence of heavy flavors in Deep inelastic scattering reactions and in proton-proton collisions.The most recent project is in theoretical particle physics, and it is relevant to the Large Hadron Collider (LHC), which is currently the highest-energy particle collider in the world, and to future colliders. By colliding protons at higher energies than have ever been previously explored, physicists expect to learn more about the fundamental particles of matter and their interactions. The research involves theoretical calculations that are sufficiently precise and accurate to make meaningful comparisons with data from the LHC. The KSU HPCs are important tools to achieve these goals.
The projects listed above use large codes in C++/C/Fortran and codes for Symbolic manipulations (e.g., Mathematica, FeynCalc, Form, FormCalc, Madgraph, ROOT, etc.) that require extensive use of the KSU HPC cluster.
Funded work includes:
- National Science Foundation, N. Kidonakis (PI) & M. Guzzi (co-PI), 鈥淭heoretical Calculations for Particle Physics at Hadron Colliders鈥, Aug 2024-Jul 2027, Grant: PHY-2412071. Amount: $360,000
- NSF - National Science Foundation, Guzzi, M. (PI) 鈥淧recision theory at the LHC: strong interaction dynamics and new physics searches鈥, Sep. 2018 - Aug. 2021, $108,830.
Award number: 1820818 - National Science Foundation Grant no. 2112025, N. Kidonakis & M. Guzzi "Particle Theory for High-Energy Collider Physics",
September 2021 - August 2024. Amount: $300,000.
Particle theory group at 黑料网:
Marco Guzzi -
Nikolaos Kidonakis -
Andreas Papaefstathiou -
Alberto Tonero - Email
There are opportunities for undergraduate students who are interested in High Energy Physics and Particle Theory to be hired as undregraduate research assistants and being paid from my NSF grant.
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Todd Pierson, Ph.D., Assistant Professor of Biology, Department of Ecology, Evolution, and Organismal Biology
Hybridization of Appalachian woodland salamanders.
Species delimitation of Brazilian foam frogs.
Urban landscape genomics of the eastern kingsnake.
Population genomics of the patch-nosed salamander.uses computational resources through HPC to assemble genomic data and conduct phylogenomic and population genomic analyses to study the ecology, evolution, and conservation of amphibians and reptiles.
Projects include:
- Species delimitation and systematics of Appalachian salamanders
- Genomic basis of reproductive polymorphisms in two-lined salamanders
- Hybridization and species delimitation of Brazilian foam frogs
- Parentage analysis and breeding systems in mole salamanders
- Population genomics of rare and imperiled amphibians
The Pierson Lab includes student at all levels and external collaborations with scientists at Clemson University, the University of Georgia, the University of North Carolina Asheville, the Universidade Estadual de Campinas, and more. KSU undergraduate students who are excited about gaining experience with field or laboratory research and prospective graduate students interested in the MSIB program should reach out to Dr. Pierson via email at tpierso3@kennesaw.edu
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Junhkyu (Justin) Park, Ph.D., Associate Professor of Mechanical Engineering
Tritium Control using Novel Nanomaterials.
High Strain Impact of Graphene.
Carbon Nanocomposites with High. Thermal Performance.
Phonon Scattering in 3D Carbon Nanostructures.All of the projects listed above involve the atomistic simulations of transport properties of novel nanomaterials. We have been able to simulate the materials' properties successfully by the help of HPC at 黑料网.
Is this work sponsored or work toward a proposal?
The study for Carbon Nanocomposites with High Thermal Performance is sponsored by KSU's OVPR fund.More details about my research projects can be found in my . I am also a faculty member in KSU's nuclear research group called Students in my research group use a molecular dynamics simulator called LAMMPS together with different programing languages such as C++ and MATLAB to investigate thermal transport and molecular transport in novel nanomaterials.
Currently, I am working with ten students on the research projects listed above. Any student who is interested in exploring the exciting properties of novel nanomaterials and nanoscale transport phenomena should contact Dr. Jungkyu (Justin) Park at jpark186@kennesaw.edu.
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Mahmut Karakaya, Ph.D., Assistant Professor of Computer Science
Improving the performance of standoff iris recognition using deep learning techniques within both traditional and nontraditional iris recognition frameworks.
This work is sponsored by the National Science Foundation (NSF) under award: 2100483, Division of Computer and Network SystemsSaTC: CORE: Small: RUI: Improving Performance of Standoff Iris Recognition Systems Using Deep Learning Frameworks, 8/2020-9/2022, $233,606.00.
The iris of the eye enables one of the most accurate, distinctive, universal, and re liable biometrics for authenticating the identity of a person. However, the accuracy of iris recognition depends on the quality of data acquisition, which is negatively affected by the angle of view, occlusion, dilation, and other factors. Since standoff iris recognition systems are much less constrained than traditional systems, the captured iris images are likely to be off-angle, dilated, and otherwise less than ideal. This project addresses these challenging problems and investigates solutions to eliminate their effects on standoff systems. The project provides potential benefits from several perspectives: At the national level, it aims to enhance the national security and competitiveness of the United States by improving the performance of iris recognition to lead the next generation of standoff biometrics systems. At the state level, it improves the quality of research and education in Arkansas, an EPSCoR (Established Program to Stimulate Competitive Research) state, and contributes to the development of a diverse and skilled workforce. At the university level, it provides research opportunities for students from underrepresented groups and equips them with valuable skills to build their careers including creativity, self-confidence, critical thinking and problem solving.
This project aims to improve the performance of standoff iris recognition using deep learning techniques within both traditional and nontraditional iris recognition frameworks. First, a deep learning-based frontal image reconstruction framework is developed to eliminate the effect of the eye structures on standoff images before comparing these images with their frontal images in a database. It will unwrap non-ideal iris images within the traditional iris recognition framework using non-linear distortion maps and occlusion masks. Second, nontraditional iris recognition frameworks are developed based on deep learning algorithms to improve the performance of standoff systems using additional biometric information in ocular and periocular structures. This approach also investigates the effect of the gaze angle in iris/ocular/periocular biometrics and combines the biometric information in different standoff images.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the
Foundation's intellectual merit and broader impacts review criteria.
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Martina Kaledin, Ph.D., Professor of Chemistry
Infrared and Raman Spectroscopy from Ab Initio Molecular Dynamics and Driven Molecular Dynamics simulations: The analysis of hydrogen-bonded systems.
This work is sponsored by the National Science Foundation (NSF) under award: CHE 1855583, Division of Chemistry.RUI: Computational Study of Vibrational Motion in Hydrogen-Bonded Systems, 09/2019-08/2022, $232,892.00.
The primary goals of this project are:
- Design fast and highly scalable computational methods to study the structure, functions,
and intermolecular interactions of hydrogen-bonded systems at the atomic level and
applying these methods to understand and predict the relations between the structure
and function of these molecules.
- Simulate and assign linear and two-dimensional (2D) spectra of hydrogen-bonded systems
using normal mode analysis, molecular dynamics, and driven molecular dynamics methods.
- Development of computational chemistry curriculum that enhances students鈥 problem-solving skills and technology skills that involve using software and visualization tools to collect and analyze data.
- Recruitment and training of students for successful STEM careers.
Funded work includes:- M. Kaledin, 鈥淭heoretical study of the vibrational anharmonicity using molecular dynamics methods and elements of machine learning鈥 CSM funds to Sustain Research Excellence (FSRE), Budget: 2-months summer salary, $19,850 (05/2024-06/2024).
- M. Kaledin, (Principal), Grant, "RUI: Computational study of vibrational motion in hydrogen-bonded systems", Sponsored by NSF, Federal Award, CHE 1855583, $232,892, 09/2019-12/2023.
Kaledin鈥檚 website:
Collaborators:
Joel M. Bowman Emory UniversityAlexey L. Kaledin Emory University
Dalton Boutwell Vanderbilt University
Students interested in joining M. Kaledin鈥檚 research group and working on the computational chemistry project as undergraduate research assistants (paid by the NSF grant) should contact her directly by email: martina.kaledin@kennesaw.edu. - Design fast and highly scalable computational methods to study the structure, functions,
and intermolecular interactions of hydrogen-bonded systems at the atomic level and
applying these methods to understand and predict the relations between the structure
and function of these molecules.