Teaching

300726: Medical Image Processing (Graduate)

Semester: Fall 2025

009954: Algorithms and Practice (Undergraduate: sections 001/002)

Semester: Fall 2025

084611: Special Topics on Medical Image Processing (Graduate)

Semester: Spring 2025

This course provides an in-depth exploration of advanced topics in image processing with a specific focus on medical image processing applications. Students will gain theoretical knowledge and practical skills in key areas such as image compression, template matching, image registration, and feature extraction. The course will also highlight cutting-edge research papers, emphasizing the integration of AI technologies in medical imaging advancements.

200079: Master's Thesis research (Graduate)

Semester: Spring 2025

This course is designed to guide students in identifying and refining research topics and tracking the progress of their Master’s thesis.

009955: Discrete Mathematics and Programming (Undergraduate)

Semester: Spring 2025

This course introduces fundamental concepts of discrete mathematics with applications in computer science and engineering. Students will learn mathematical reasoning, proof techniques, combinatorial analysis, and discrete structures. The course emphasizes algorithmic thinking and problem-solving, blending theory with practical applications through Python programming.

007330: Probability and Statistics (Undergraduate: sections 002/008/009)

Semester: Spring 2025

Almost all scientific fields now routinely analyze data using probability models and statistical techniques. This course offers a comprehensive introduction to the probability models and statistical methods that students are most likely to come across and utilize in their careers. This course will emphasize a balance between theory and applications, including an understanding of probability, random variables, probability distributions, hypothesis testing, regression, and correlation.

300726: Medical Image Processing (Graduate)

Semester: Fall 2024

This course offers a comprehensive introduction to the fundamental concepts and methodologies of digital image processing, with a particular emphasis on medical image analysis and applications. By the end of this course, students will be able to:

Understand digital image fundamentals.
Implement and apply image enhancement techniques in both spatial and frequency domains.
Understand the theory and application of image restoration methods.
Apply segmentation techniques to identify and analyze regions of interest in medical images.
Use morphological techniques to enhance and extract meaningful information from images.
Describe different medical imaging modalities and apply reconstruction methods.

009954: Algorithms and Practice (Undergraduate: sections 001/002)

Semester: Fall 2024

009959: Probability and Statistics Programming (Undergraduate)

Semester: Spring 2024

009954: Algorithms and Practice (Undergraduate: sections 001/002)

Semester: Fall 2023

This course offers a comprehensive introduction to modern computer algorithms. It focuses on the following main objectives:

Understand algorithmic fundamentals, emphasizing algorithm design and analysis.
Analyze algorithm efficiency in terms of time and space complexity.
Design efficient algorithms for computational problems, including divide-and-conquer and dynamic programming approaches.
Explore sorting and searching algorithms (e.g., merge sort, quicksort, binary search) to process and retrieve data efficiently.
Gain insights into graph algorithms, covering traversal, shortest paths, and minimum spanning trees for network analysis and optimization.
Strengthen programming proficiency through practical coding exercises and lab assignments (Python Programming), implementing efficient algorithmic solutions.

009959: Probability and Statistics Programming (Undergraduate)

Semester: Spring 2023

Almost all scientific fields now routinely analyze data using probability models and statistical techniques. This course offers a comprehensive introduction to the probability models and statistical methods that students are most likely to come across and utilize in their careers. This course will emphasize a balance between theory and applications, including an understanding of probability, random variables, probability distributions, hypothesis testing, regression, and correlation. Students will also practice Python programming for problems involving probability and statistics theory.