Engage in Research!

Our faculty and students engage in a variety of research activities ranging from computerized diagnosis of melanoma to designing secure embedded systems. For a list of faculty research areas, please visit here.

We are one of the most research-active departments at UCA. Our faculty brings in a sizeable percentage of UCA’s annual federal research funding and publishes a large number of papers in reputable venues. Current or recent federal funding resources include the National Institutes of Health (NIH), National Science Foundation (NSF), and National Aeronautics and Space Administration (NASA).

Undergraduate students may receive credit for research by enrolling in CSCI 4V95: Independent Study (1–3 credit hours; a maximum of six credit hours may be applied towards the BS degrees in Computer Science and Computer Engineering, whereas a maximum of three credit hours may be applied towards the BS degree in Cybersecurity with Cyberphysical Security concentration and towards the BS degree in Data Science with Computer Science concentration). CSCI 4V95 counts as a subject elective in BS programs in Computer Science, Computer Engineering, Cybersecurity (only in the Cyberphysical Security concentration), and Data Science (only in the Computer Science concentration)

Graduate students may receive credit for research by enrolling in CSCI 6395: Independent Study (a maximum of six credit hours may be applied towards the degree) or, if they are pursuing the thesis option, in CSCI 6V99: Master’s Thesis (1–6 credit hours; a total of six credit hours is required).

Note that CSCI 4V95, 6395, and 6V99 are not traditional courses (e.g., there are no lectures). These research classes are all offered on demand.

Before embarking on a research project:

  1. Narrow down the list of research areas in which you are interested (for a list of faculty research areas, please visit here).
  2. If multiple faculty members are working in your areas of interest, make an appointment with each.
  3. Meet with your prospective supervisors to talk about possible project topics. Be sure to  compile a list of questions that you would like to ask in advance. Example questions: does the faculty member have time to supervise your project?; what kind of background does the project require?; how long will it take?; what are the expectations of the faculty member? (more on this below); etc.
  4. Choose a supervisor with whom you can work and communicate easily.
  5. Choose a topic that is of interest to you, keeping in mind that some topics may be unattainable because of the complexity of the topic, shortness of the period of research, lack of experience of your supervisor in that domain, etc. For example, you may be fascinated with computer viruses; however, we currently do not have any faculty members who specialize in that topic. In most cases, your prospective supervisors will recommend suitable topics that are very likely to be related to one of their current or recent research projects.
  6. If you plan to choose the thesis option (in the MS program in Computer Science) or pursue a doctoral degree later on, consider the topic’s publishability. Keep in mind that doing research is not the same as publishing it and that it is easier to produce publishable research in some areas than others. If you publish a paper as a result of your research, this might turn into a thesis eventually or, you might be able to get into a doctoral program by leveraging this research experience.
  7. Understand the expectations of your supervisor. For example, are you supposed to submit a paper as a result of this research or, is your work supposed to be documented as a course report? What exactly is the measure of performance and the target? In other words, how do you know when you are done with your research project?
  8. If you would like to receive CSCI 4V95/6395/6V99 credit for your research, discuss this with your supervisor.

Possible Research Projects for Independent Study (CSCI 4V95 or 6395) or Thesis (CSCI 6V99)

  • Color Quantization: This project involves the design of efficient partitional clustering algorithms for reducing the number of distinct colors in an input image with minimum possible distortion. (Supervisor: Dr. Celebi)
  • Wineinformatics: This project involves the use of data science techniques to analyze wine reviews through natural language processing to discover useful and interesting information for winemakers, distributors, and consumers. (Supervisor: Dr. Chen)
  • Development and Validation of a Virtual Colorectal Surgical Trainer (VCoST): The goal of this project is to develop and validate a Virtual Reality (VR) platform for colorectal tasks. The project aims to show the effectiveness of the simulator in surgery training and assessment based on the quantitative performance metrics. (Supervisor: Dr. Halic).
  • Development and Validation of a Virtual Bariatric Endoscopic (ViBE) Simulator: This project aims to develop and validate virtual simulation for the Endoscopic Gastric Sleeve procedure. (Supervisor: Dr. Halic).
  • Development and Validation of Virtual Arthroscopy Simulations: The research aims to develop surgery simulation using Virtual Reality (VR) by providing various modalities, including haptic feedback for Arthroscopy procedures. These projects aim to validate the effectiveness of the simulators with human subject studies. (Supervisor: Dr. Halic, Collaborator: Dr. Kockara)
  • Interval Computation and Applications: Instead of commonly used point-valued computation, this project uses interval-valued data and methods to encapsulate qualitative information and uncertainty in computation. It involves interval methods in application modeling, algorithm design, software implementation, and testing. (Supervisor: Dr. Hu)
  • Change Detection in Skin Lesions Using Deep Learning: Total Body Photography (TBP) is an important tool for the early detection of skin cancer. This project aims to develop deep learning based computer vision methods to automatically detect, track, map, and highlight changing lesions in time series TBP images. (Supervisor: Dr. Kockara)
  • Interpretable and Secure Deep Learning: Current deep learning systems sometimes make wrong, unexplainable, and/or unpredictable misclassifications. This project aims at improving deep learning systems by learning to extract robust representations by developing cortex-inspired deep neural network architectures. (Supervisor: Dr. Kursun)
  • Security of Embedded and Cyber-Physical Systems: Instead of relying on computationally intensive encryption algorithms, security of embedded systems should be guaranteed under their design constraints (low available memory and computational power). This project combines efficient hardware/software design methods to deal with recent security challenges in embedded and cyber-physical systems. (Supervisor: Dr. Patooghy)
  • Video Compression and Transmission: This project investigates efficient methods to control video compression bitrates in order to meet variable network transmission rates while obtaining optimal encoding quality. (Supervisor: Dr. Sun)