Hello! I graduated from Istanbul Kultur University (TURKEY) with a B.E. in Computer Engineering, and I earned my M.S. and PhD in Computer Science from University of Arkansas at Little Rock in May 2015 and May 2018, respectively.
My main research interests are in Signal Processing, Brain Functions and Disorders, Machine Learning and Brain Research Development Tools.
Please feel free to check out my CV.
Students study problems arising from the physical, biological, and/or social sciences and the algorithms and theory used to solve them computationally. Included among the problems are numerical methods for maximizing a function and solving a differential equation.
An introduction to recent tools and algorithms for building interactive games. Students will learn fundamental design mechanics and implement a substantial development project. Topics may include steering and flocking behaviors, path finding algorithms, finite state machines, behavior trees, alpha-beta pruning, Monte Carlo Tree Search, shaders, 3D modeling, animation, procedural content generation, and the intersection of games and society. Content varies according to the interests of the participants and instructor.
Introduction to solving computational problems, including the fundamentals of computer programming. Topics include imperative programming constructs (variables, loops, conditionals, functions, recursion), basic object-oriented constructs (classes, objects), and some fundamental algorithms and data structures (dictionaries, arrays, linked lists). Student learn these concepts through studying the Python programming language.
This course is a study of the layers of abstraction composing the design of modern computing systems. Topics include numeric representation, digital logic, CPU design, machine and assembly language, the program stack, virtual machines, compilers, assemblers, memory management and device drivers.
Building on the skills acquired in CSCI 151, this course introduces data structures such as lists, stacks, queues, trees, and graphs, in the context of object-oriented software design. We will program using the Java language.
A combination of readings, writing assignments, oral presentations, and independent project work integrates the lessons from each student's undergraduate studies. Students assess the content of formal writing about computing subjects, investigate ethical and social issues in computing, and complete a substantial independent capstone project. Students also prepare themselves for professional work by resume writing and the creation of a professional portfolio.
This is a hands-on course in which you will learn to use a computer to practice the four most popular programs within the Microsoft Office Suite (Word, Excel, Access, and PowerPoint). You will learn to be an intermediate level user of the Microsoft Office Suite. Within the Microsoft Office Suite, you will use Word, Excel, Access, and PowerPoint.
Functional and structural networking of neurons governed with a set of organizational principles forms a modular/colonial structure that controls the information processing behaviour of human. The structures in this environment are hierarchical in nature and connections are established through the mutually exclusive nodes. Therefore, it is safe to hypothesize that the decision mechanism behind a cognitive process takes place at the highest hierarchical and spatial level. In order to conduct the necessary experimentation to support the hypothesis, a non-invasive method called Electroencephalography (EEG) is used to monitor and record the electrical activity of the brain for detecting and analyzing the cognitive functions and mental disorders. One innovative finding of this study is the use of EEG to find Antipodal Hub Nodes (AHNs) which are dynamic and provide collaborative information.
The NAMS is an interactive software environment to analyze and monitor the coordinated complex network structures of the neural communities. The purpose of a toolbox-oriented system is to support scalability and minimally invasive enhancement for behavioural pattern identification and analysis.
The cortical behavior at neural level could be originated from specified disorder such as Obsessive-Compulsive Disorder (OCD). OCD is a commonly encountered long-lasting mental health disorder that affects the daily activities of the people in all ages with a cycle of obsessions and compulsions. It is known to be the fourth most common mental illness. Despite of the research being conducted on the condition, the exact cause, other than having neurobiological base and different neurological patterns in people with the disorder, is yet to be identified. Although OCD abnormalities are associated with the imbalance in neurotransmitters or brain chemicals, it is necessary to classify the behavioural patterns of the brain for understanding cause and effect related details. Therefore, the objective of this study is to develop an algorithm to analyze EEG data to obtain significantly improved classification accuracy, which could sufficiently contribute to the identification of dysfunctional lobes of OCD.