Spring 2023 Office Hours

Please note that my office hours change due to appointments, meetings, etc. I strongly encourage everyone to contact me directly to schedule an appointment. The tutoring schedule contains a list of times in which students can get assistance for computer science courses.

Monday	Tuesday	Wednesday	Thursday	Friday	Saturday
		Research (Appointment only) 12:00 pm - 5:00 pm		Research (Appointment only) 10:00 am - 2:00 pm
4:00 pm - 5:00 pm	Senior Projects 4:00 pm - 5:00 pm		Meetings 4:00 pm - 5:00 pm

Courses

The follow list contains courses that I have taught within the past few years.

Course	Link
CS 2003 - Data Structures	Su 17    Fa 18    Sp 19    Su 19     Fa 19    Sp 20     Su 20     Fa 20     Sp 21     Su 21     Fa 21    Sp 22     Fall 2022
CS 2033 - Web Systems	Fall 2015    Summer 2016   Summer 2017    Spring 2022
CS 3043 - Database Systems	Sp 17    Fa 17    Sp 18    Su 18    Sp 19    Sp 20    Su 21    Fa 21    Fall 2022
CS 3103 - Algorithms	Fa 16    Fa 17    Fa 18    Su 2019    Fa 2019    Summer 2020    Fall 2021   Fall 2022
CS 3113 - Artificial Intelligence	Spring 2020    Spring 2021    Spring 2022    Spring 2023
CS 4043 - Formal Languages	Fall 2021    Spring 2022    Spring 2023
CS 4153 - Advanced Algorithms	Spring 2018   Spring 2020
CS 4323 - Data Analytics	Summer 2016    Fall 2016    Summer 2018    Fall 2019
CS 4333 - Machine Learning	Spring 2017    Spring 2019
CS 4343 - Natural Language Processing	Fall 2018    Fall 2020    Fall 2022
CS 4143 - Deep Learning	Summer 2020     Spring 2022

Resources

The following resources are available for students to use.

Linux

• Introduction to the Linux Command Line
• Linux Tools, Utilities, and Other Services

Programming

• String Encryption using SHA-256
• Using JSON with PHP, Javascript, and MySQL
• Git for Software Development

Data Science, Artificial Intelligence, and Machine Learning

• Python Programming for Computer Science
• Python for Data Science and Machine Learning
• R Programming and Statistics for Data Science

Other

• LaTeX Document Formatting and Examples

Advising

All students who are advised by me should schedule an appointment with me each semester if nothing more to let me know how your semester is progressing. The following advising materials are available to faciliate any discussions:

• B.S. in Computer Science Degree with Data Science/Artificial Intelligence Concentration
• UAFS Course Schedule
• Data Analytics Certificate and Data Science Concentration
• Current Degree Plan
• Course Syllabi

 

Summer 2022 Special Topics/Advanced Courses

• CS 4143 - Deep Learning: Students will learn the latest advancements in deep neural networks and how to apply them to solve various types of problems, including computer vision and natural language processing. Major topics include deep feed forward neural networks, regularization techniques for deep learning, convolutional neural networks (image and pattern recognition), recurrent neural networks (text, sequences, time series, etc.), autoencoders, attention, generative adversarial networks (GANs), transfer learning, reinforcement learning, and applications of deep learning to computer vision and natural language processing. Students enrolled will pursue a major project at the end of the course that require deep neural networks. Prerequisites: Artificial Intelligence or Machine Learning, or have instructor approval.

Research

My work focuses on the design and implementation of algorithms that optimize, explain (transparency), and improve existing Artificial Intelligence (AI) systems. The following represents my areas of interest:

I am always engaged and willing to sponsor student research in the aforementioned areas. If you are interested in participating in research and/or lab activities, feel free to schedule a time to visit with me and we can determine the best path forward.

Previous Research

• A. Mackey, S.G., et al. , "Fake News Identification Using Neural Language Models", Fourteenth International Conference on Information, Process, and Knowledge Management, Barcelona, Spain, 2022.
• A. Mackey, S.G., K.L. , "Detecting Fake News Through Emotion Analysis", Thirteenth International Conference on Information, Process, and Knowledge Management, Nice, France, 2021.
• A. Cuevas, K. Kelly, I. Cuevas, and A. Mackey, "UAFS ParkBOT: An Autonomous Vehicle Identification Robot", UAFS Research Symposium, Fort Smith, AR, 2021.
• S. Atchley, B.Bright, I. Cuevas, R. Fisher, L. Hinton, A. McFerran, and A. Mackey, "Sentiment Analysis Using Deep Learning for Arkansas-based Organizations", Arkansas State Capitol, Little Rock, AR, 2020.
• A. Cuevas, I. Cuevas., F. Estrada, K. Kelly, and A. Mackey, "Improving the Privacy of Text Through Natural Language Processing and Machine Learning", Arkansas State Capitol, Little Rock, AR, 2020.
• B. Bright, C. Cuevas, I. Cuevas., and A. Mackey, "Scalable Processing of Massive Text Data Stores for NLP", Consortium for Computing Sciences in Colleges, Little Rock, AR, 2019.
• B. Bright, C. Cuevas, I. Cuevas., and A. Mackey, "Using NLP to Automate the Categorization of Text Across Disparate Systems Temporally", Arkansas State Capitol, Little Rock, AR, 2019.
• C. Cuevas and A. Mackey, "UAFS Inbox Manager - A Natural Language Text Categorization Tool for Email," UAFS Research Symposium, Fort Smith, AR, 2018.
• I. Cuevas and A. Mackey, "Automatic Text Summarization Within Big Data Frameworks", Consortium for Computing Sciences in Colleges, Memphis, TN, 2018.
• B. Bright, C. Cuevas and A. Mackey, "Text Categorization Using Distributed NoSQL Databases", Consortium for Computing Sciences in Colleges, Memphis, TN, 2018.
• I. Cuevas and A. Mackey, "In-Memory Cluster Computing for Machine Learing," Consortium for Computing Sciences in Colleges, Batesville, AR, 2017.
• C. Cuevas, I. Cuevas and A. Mackey, "Machine Learning in the Age of Automation," UAFS Research Symposium, Fort Smith, AR, 2017.
• C. Cuevas and E. Maravilla, "Inverted Indexes and Graph Algorithms using Hadoop," Consortium for Computing Sciences in Colleges, Batesville, AR, 2017.
• A. Mackey, "Incorporating Big Data Technology into Computing Curriculum," Consortium for Computing Sciences in Colleges, Memphis, TN, 2016.
• I. Cuevas and A. Mackey, "Optimizing Data Analytics Using In-Memory Big Data Clusters," Consortium for Computing Sciences in Colleges, Memphis, TN, 2016.
• K. Reed and A. Mackey, "Big Data, Analytics and Their Potential," Consortium for Computing Sciences in Colleges, Memphis, TN, 2016.
• A. Mackey, "Enterprise Systems," Consortium for Computing Sciences in Colleges, Conway, AR, 2015.
• D. Cabrera and A. Mackey, "Big Data - Big Opportunities: NoSQL and Hadoop," Consortium for Computing Sciences in Colleges, Conway, AR, 2015.
• K. Seo and A. Mackey, "NoSQL Document Databases for Enterprise Application Deployment," Consortium for Computing Sciences in Colleges, Conway, AR, 2015.
• S. Jeong and A. Mackey, "Big Data and Relational Database Management Systems," Consortium for Computing Sciences in Colleges, Conway, AR, 2015.

Programming Team

If you are interested in joining the UAFS programming team, please send me an email or stop by my office and visit with me. I would be more than happy to discuss this with you.

Upcoming Competitions

• November 2021 - ACM ICPC Programming Competition
• April 2022 - CCSC Programming Competition