Arthur S. Williams

Graduate Student
Ph.D. Program - Computational and Data Science
Email: asw3x[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~asw3x/

Hierarchical and indirection-based working memory models for task generalization in partially observable reinforcement learning domains.

Matthew T. Radice

Graduate Student
Ph.D. Program - Computational and Data Science
Email: mtr3t[at]mtmail.mtsu.edu
WWW: None

Limitations of Q-Learning for partially observable reinforcement learning domains and compensatory mechanisms via working memory.

David W. Ludwig

Graduate Student
Ph.D. Program - Computational and Data Science
Email: dwl2x[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~dwl2x/

Deep learning models for dynamic task switching and permutation invariant representations.

Jackson L. Goble

Graduate Student
M.S. Program - Computer Science
Email: jlg2av[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~jlg2av/

Episodic memory models for frequency-based task generalization in reinforcement learning domains.

Karuna D. Gujar

Graduate Student
M.S. Program - Computer Science
Email: kdg5v[at]mtmail.mtsu.edu
WWW: None

Recurrent and transformer neural networks for predicting biophysical traits from fMRI.

Scott P. Morton

Graduate Student
Ph.D. Program - Computational Science
Email: spm3c[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~spm3c/

Currently researching the ElectroStatic Surface Charge Pipeline for HIV antibody binding characteristics. Additionally converting all Bash scripts into a unified python language format with a json based configuration file. Further research goals include a rewrite of frodaN for ease of use and parallel processing potentials involving CUDA and/or MPI, Massively Parallel Processing (MPP) of model protein sequences for predicting binding characteristics of HIV and HIV antibodies, potential electronic circuitry characteristics of protein sequences as a means of binding energy.

Ivan Syzonenko

Graduate Student
Ph.D. Program - Computational Science
Email: is2k[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~is2k/

Ivan's research has focused on understanding and overcoming the limitations of machine learning algorithms, particularly clustering, in biomolecular simulation analysis. More recently, he is developing and testing methods for accelerated unbiased targeted MD simulations using classical pathfinding approaches with a focus on protein folding.

Heena Khan

Graduate Student
M.S. Program - Computer Science
Email: hk4h[at]mtmail.mtsu.edu
WWW: None

Comparison of various machine learning and deep learning models for the identification of Islamophobic content on social media.

Terryn J. Seaton

Undergraduate Student
B.S. Program - Computer Science
Email: tjs6z[at]mtmail.mtsu.edu
WWW: None

OpenLDAP deployment for a Singularity/SLURM HPC cluster in the cloud via Kubernetes.

Chaning B. Mullinax

Undergraduate Student
B.S. Program - Computer Science
Email: cbm5d[at]mtmail.mtsu.edu
WWW: None

Nested LSTM/Indirection models of working memory for human-like extrapolatory generalization.

Will H. Haase

Undergraduate Student
B.S. Program - Computer Science
Email: whh2p[at]mtmail.mtsu.edu
WWW: None

Minimizing catatrophic interference via unitization and generative recurrent neural network models.

Nibraas A. Khan

Undergraduate Student
B.S. Program - Computer Science
Email: nak2z[at]mtmail.mtsu.edu
WWW: None

Combined working memory and N-task models for partially-, non-observable reinforcement learning problems.

Lucas Remedios

Undergraduate Student
B.S. Program - Computer Science
Email: lwr2k[at]mtmail.mtsu.edu
WWW: https://www.cs.mtsu.edu/~lwr2k/

Keras/TensorFlow tools for N-task learning.

Huizhi Wang

Graduate Student
M.S. Program - Computer Science
Email: hw3m[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~hw3m/

Dimensional attention mechanisms for holographic reduced representational encodings and category learning.

Ngozi C. Omatu

Undergraduate Student
B.S. Program - Biology
Email: nco2f[at]mtmail.mtsu.edu
WWW: None

Dimensional attention for working memory: accelerated early learning with asymptotically optimal performance.

Mike Jovanovich

Graduate Student
M.S. Program - Computer Science
Graduation: Fall 2017
Email: mpj2n[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~mpj2n/

Neurobiologically plausible models of working memory, task switching, and task generalization.

Joshua M. Arnold

Undergraduate Student
B.S. Program - Computer Science
Email: jma5x[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~jma5x/

Coupled action-working memory learning for partially observable reinforcement learning problems.

Cody Crawford

Graduate Student
M.S. Program - Computer Science
Graduation: Summer 2017
Email: crn2k[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~crn2k/

Journalism today is dealing with so much data that better methods are needed to process it. Latent Dirichlet Allocation (LDA) is often used to sort text into topics. The Afghan War Diary (AWD) was processed with LDA and model trees to ascertain fatality numbers. The AWD was used in a separate study that analyzed the documents with point process modeling (PPM) to predict where conflicts would occur in space and time. We have combined the two approaches in this study, hopeful that the results will allow us to predict where and when conflicts occur and if the fatality numbers can also be obtained in reference to that. We anticipate that our results will show that PPM combined with LDA and model trees will give more useful results than using either of the methods separately.

Jonathan Howton

Graduate Student
M.S. Program - Computer Science
Graduation: Spring 2017
Email: jh6w[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~jh6w/

Jonathan's research focuses on using high-throughput structural analysis to understand how environmental factors affect protein binding particularly with regard to viral transmission.

Grayson Dubois

Undergraduate Student
B.S. Program - Computer Science - B.S. Spring 2017
Email: Grayson.Dubois[at]mtsu.edu
WWW: http://www.cs.mtsu.edu/~gmd2n/

Grayson's research looks into a new method of representing concepts in artificial neural networks (ANNs) that mimic human working memory systems. This new method uses something called Holographic Reduced Representations (HRRs), which are powerful tools capable of representing compositional structure in distributed representations. Grayson has developed an engine for encoding and decoding HRRs and is working on integrating it into the Working Memory toolkit, a software library written in ANSI C++ designed to allow researchers to write simulations of learning tasks using ANNs modeled after working memory. The current toolkit requires the programmer to explicitly provide methods to convert concepts used by working memory from symbolic encodings (SE) to distributed encodings (DE). Grayson's HRR Engine will automate the process of SE/DE conversion, thus taking the burden off of the programmer and opening the door to future possibilities not possible with previous DE methods. These include but are not limited to the chunking of similar concepts in memory, transferability of learned behaviors between tasks, and long term memory.

Gary Hammock

Graduate Student
M.S. Program - Computer Science
Graduation: Fall 2016
Email: glh2y[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~glh2y/

Gary's research focuses on the development and testing of novel simulation-inspired methods for cryptography.

Robert Myers

Graduate Student
M.S. Program - Computer Science
Graduation: Spring 2016
Email: rvm2d[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~rvm2d/

Robert's research focused on the development and testing of distributed pathfinding algorithms.

Michael Murphy

Graduate Student
M.S. Program - Computer Science
Graduation: Spring 2016
Email: mcm7f[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~mcm7f/

Fractal dimension is a number that describes the self-similarity, or "complexity", of a geometry. In image processing, fractal dimension is often used as a novel method for contrasting and comparing image content. The Box-Counting Algorithm is one of the most popular methods of computing an estimate for the fractal dimension of an image, but the algorithm is influenced by many factors such as filtering and noise. Our research found a relationship between dimensional estimations and the variability in those estimations when using the Box-Counting Algorithm in the presence of increasing levels of uniform noise. This relationship provides a way to strengthen relative dimensional rankings between noisy images.

Stephen Kinser

Undergraduate Student
B.S. Program - Computer Science
Email: sdk2v[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~sdv2k/

Stephen's research focused on converting a semi-autotmated protein electrostatics pipeline to a fully-automated version which requires significantly less user intervention.