成人直播

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 成人直播 HPC cluster. 

Funded work includes: 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 成人直播:

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.

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.

成人直播 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

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 成人直播.
Is this work sponsored or work toward a proposal?
The study for Carbon Nanocomposites with High Thermal Performance is sponsored by 成人直播's OVPR fund.

More details about my research projects can be found in my . I am also a faculty member in 成人直播'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.

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.

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.

Kaledin鈥檚 website: 
 
Collaborators:
 
Joel M. Bowman Emory University

Alexey 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.