Teaching

CSIS 3700L: Data Structures and Objects Lab (Spring 2026)

Undergraduate Laboratory Course, Youngstown State University, 2026

Undergraduate Laboratory Course, Instructor
CSIS 3700L provides hands-on experience implementing and analyzing data structures in C++. Topics include classes, dynamic memory, linked lists, stacks, queues, trees, graphs, and the C++ Standard Template Library.

CSIS 3700: Data Structures and Objects (Spring 2026)

Undergraduate Course, Youngstown State University, 2026

Undergraduate Course, Instructor
CSIS 3700 introduces students to object-oriented programming and fundamental data structures. Topics include classes, abstract data types, linked structures, stacks, queues, trees, graphs, algorithm analysis, and software design.

CSCI 6952: Deep Learning (Spring 2026)

Graduate Course, Youngstown State University, 2026

Graduate Course, Instructor
CSCI 6952 introduces students to the foundations and practice of deep learning. Topics include neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative models, model optimization, and real-world applications.

CSCI 6951: Data Science and Machine Learning (Spring 2026)

Graduate Course, Youngstown State University, 2026

Graduate Course, Instructor
CSCI 6951 introduces students to the data science pipeline and machine learning. Topics include data acquisition, preprocessing, visualization, feature engineering, classification, clustering, deep learning, recommendation systems, and real-world data analysis.

CSCI 4852: Deep Learning (Spring 2026)

Graduate Course, Youngstown State University, 2026

Undergraduate Course, Instructor
CSCI 4852 introduces students to the foundations and practice of deep learning. Topics include neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative models, model optimization, and real-world applications.

CSCI 4851: Data Science and Machine Learning (Spring 2026)

Undergraduate Course, Youngstown State University, 2026

Undergraduate Course, Instructor
CSCI 4851 introduces students to the data science pipeline and machine learning. Topics include data acquisition, preprocessing, visualization, feature engineering, classification, clustering, deep learning, recommendation systems, and real-world data analysis.

CSIS 3731: Human-Computer Interaction (Fall 2025)

Undergraduate Course, Youngstown State University, 2025

Undergraduate Course, Instructor
CSIS 3731 introduces students to the principles and practices of human-computer interaction, including user-centered design, usability evaluation, prototyping, accessibility, and interaction design.

CSIS 2610L: Programming and Problem-Solving Lab (Fall 2025)

Undergraduate Laboratory Course, Youngstown State University, 2025

Undergraduate Laboratory Course, Instructor
CSIS 2610L provides hands-on programming experience in C++ through guided lab activities, coding exercises, debugging practice, and collaborative problem solving.

CSIS 2610: Programming and Problem-Solving (Fall 2025)

Undergraduate Course, Youngstown State University, 2025

Undergraduate Course, Instructor
CSIS 2610 introduces students to problem-solving methods and programming using C++. Topics include algorithms, control structures, functions, arrays, pointers, file processing, and object-oriented programming.

CSCI 6951: Data Science and Machine Learning (Fall 2025)

Graduate Course, Youngstown State University, 2025

Graduate Course, Instructor
CSCI 6951 introduces students to the data science pipeline and machine learning. Topics include data acquisition, preprocessing, visualization, feature engineering, classification, clustering, deep learning, recommendation systems, and real-world data analysis.

CSCI 4851: Data Science and Machine Learning (Fall 2025)

Undergraduate Course, Youngstown State University, 2025

Undergraduate Course, Instructor
CSCI 4851 introduces students to the data science pipeline and machine learning. Topics include data acquisition, preprocessing, visualization, feature engineering, classification, clustering, deep learning, recommendation systems, and real-world data analysis.