HPC Researcher Spotlight

The department of Research Computing would like to spotlight the following researchers who utilized the High Performance Computing system at KSU.

  • 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 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: NSF - National Science Foundation, Guzzi, M. (PI) “Precision theory at the LHC: strong interaction dynamics and new physics searches”, 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 Kennesaw State University:

    Marco Guzzi -  FacultyWeb

    Nikolaos Kidonakis - FacultyWeb

    Andreas Papaefstathiou - FacultyWeb

    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.

  • 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.

    The Pierson Lab 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.

    Undergraduate student researcher: Jadin Cross

    External collaborators from Clemson University, the University of Georgia, Piedmont University, and the Universidade Estadual de Campinas.

    KSU undergraduate students who are excited about gaining experience with field or laboratory research, should reach out to Dr. Pierson via email at tpierso3@kennesaw.edu

  • 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 KSU.
    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 faculty website. I am also a faculty member in KSU's nuclear research group called NESEL. 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.

  • 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 Systems

    SaTC: 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.

    Learn more about Mahmut Karakaya research.

  • 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’ 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.

    Kaledin’s website: http://facultyweb.kennesaw.edu/mkaledin/index.php
     
    Collaborators:
     
    Joel M. Bowman Emory University

    Alexey L. Kaledin Emory University

    Dalton Boutwell Vanderbilt University
     
    Students interested in joining M. Kaledin’s 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.

 

Publications

  • M. Veksler*, R. Aygun, K. Akkaya and S. Iyengar, "Video Origin Camera Identification using Ensemble CNNs of Positional Patches," 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR), CA, USA, 2022, pp. 41-46, doi: 10.1109/MIPR54900.2022.00015.

    S. A. Kudratov*, M. Kaledin, A. L. Kaledin, Revisiting the N(N+1)/2-site s-type Gaussian charge model for permutationally invariant polynomial fitting of molecular tensor properties. 
    Int. J. Quant. Chem. (2023) (In press). 

    O. Omodemi*, R. Revennaugh*, J. Riley*, A. L. Kaledin, M. Kaledin
    A Fermi resonance and a parallel-proton-transfer overtone in the Raman spectrum of linear centrosymmetric N4H+: A polarizability-driven first principles molecular dynamics study. 
    J. Chem. Phys. 157 (2022) 154303 (10 pages).

    O. Omodemi*, M. Kaledin, A. L. Kaledin
    Permutationally Invariant Polynomial Representation of Polarizability Tensor Surfaces for Linear Regression Analysis. J. Comput. Chem. 43 (2022) 1495-1503.

    D. Boutwell*, D. Pierre-Jacques*, O. Cochran*, J. Dyke*, D. Salazar*, C. Tyler*, M. Kaledin
    Intramolecular Proton Transfer in the Hydrogen Oxalate Anion and the Cooperativity Effects of the Low-Frequency Vibrations: A Driven Molecular Dynamics Study. J. Phys. Chem. A 126 (2022), 583-592.

    O. Omodemi*, S. Sprouse*, D. Herbert*, M. Kaledin, A. L. Kaledin 
    On the Cartesian representation of the molecular polarizability tensor surface by polynomial fitting to ab initio data. J. Chem. Theory Comput. 18 (2022) 37-45.

  • Hou, T.-J., Gao, J., Hobbs, T.J., Xie, K., Dulat, S., Guzzi, M., Huston, J., Nadolsky, P., Pumplin, J., Schmidt, C., Sitiwaldi, I., Stump, D., Yuan, C.-P., 2021. New CTEQ global analysis of quantum chromodynamics with high-precision data from the LHC. Phys. Rev. D 103, 014013.

    Huang, X., Lin, Z., 2021. LOCAL COMPOSITE QUANTILE REGRESSION SMOOTHING: A FLEXIBLE DATA STRUCTURE AND CROSS-VALIDATION. Econometric Theory 37, 613–631.

    Karakaya, M., 2021. Iris-ocular-periocular: toward more accurate biometrics for off-angle images. J. Electron. Imaging 30, 033035.

    Kidonakis, N., Guzzi, M, Yamanaka, N, 2021. tW and tZ’ production at hadron colliders. Presented at the Deep-Inelastic Scattering and Related Subjects. 

    D. Pierre-Jacques*, C. Tyler*, J. Dyke*, A. L. Kaledin, M. Kaledin.
    A Polarizability driven ab initio molecular dynamics approach to stimulating Raman activity: Application to C20. Mol. Phys. 119 (2021) e1939453 (9 pages).

  • Anderle, D., Dasgupta, M., El-Menoufi, B.K., Guzzi, M., Helliwell, J., 2020. Groomed jet mass as a direct probe of collinear parton dynamics. Eur. Phys. J. C 80, 827.

    Boutwell, D., Okere, O., Omodemi, O., Toledo, A., Barrios, A., Olocha, M., Kaledin, M., 2020. Analysis of the Proton Transfer Bands in the Infrared Spectra of Linear N2H+···OC and N2D+···OC Complexes Using Electric Field-Driven Classical Trajectories. J. Phys. Chem. A 124, 7549–7558.

    Guzzi, M., Kidonakis, N., 2020. $$tZ’$$ production at hadron colliders. Eur. Phys. J. C 80, 467.

Grants

  • Agency: NSF 
    Title: PARTICLE THEORY FOR HIGH-ENERGY COLLIDER PHYSICS  
    Prof. Nikolaos Kidonakis (PI) and  Dr. Marco Guzzi(co-PI) 
    Start date: 1 Sep 2021 
    End date: 31 Aug 2024 
    Amount funded: $300,000

  • Title: Design and Implementation of an IoT Testbed for Secure Software Deployment and Testing using Containerization 
    PI: Dr. Maria Valero 
    Start Date: July 1st, 2021 
    End Date: June 30th, 2022 
    Amount: $14,907.89
  • Agency: NSF 
    Title: SaTC: CORE: Small: RUI: Improving Performance of Standoff Iris Recognition Systems Using Deep Learning Frameworks  
    Mahmut Karakaya (PI) 
    Start date: 1 Aug 2020 
    End date: 30 Sep 2022 
    Amount funded: $233,606 

  • Agency: Army Research Office (ARO) 
    Title: Multimodal Inference of Human State to Track Cognitive Processes in Risky Environments 
    Sylvia Bhattacharya (PI) 
    Start date: Aug 2020 (?) 
    End date: Aug 2021 (?) 
    Amount funded: $154,872 
  • Agency: NSF 
    Title: RUI: Computational Study of Vibrational Motion in Hydrogen-Bonded Systems  
    Martina Kaledin (PI) 
    Start date: Sep 2019 
    End date: Aug 2022 
    Amount funded: $232,892

  • Agency: NSF 
    Title: Quantum Kinetics of Laser-Induced Electron Hole Plasmas in Nanowire Arrays  
    Jeremy Gulley (PI) 
    Start date: June 2019 
    End date: May 2022 
    Amount funded: $116,948 

HPC Acknowledgement

Kennesaw State University recommends that users of the university-level HPC include the following acknowledgement statement: “This work was supported in part by research computing resources and technical expertise via a partnership between Kennesaw State University’s Office of the Vice President for Research and the Office of the CIO and Vice President for Information Technology [1].”

Cite as (using the appropriate citation format):

[1] High Performance Computing System, Kennesaw State University, [Digital Commons Training Materials].