Offered Tutorials/Workshops

Offered Tutorials/Workshops

The following tutorials/workshops are offered for ACMSE 2025:


AI-Driven Collaboration for Diagnosis of Tuberculosis and COVID-19 Based on Images

Presenter: Octavio Juarez (Southeast Missouri State University – USA)
Type of Event: Tutorial
Date: Thursday, April 24, 2025
Time: 09:30–11:30 (Eastern Time)
Duration: 2 hours
Room: Dempster Hall 026

Abstract:
The rapid and accurate diagnosis of respiratory illnesses like tuberculosis and COVID-19 is crucial for timely intervention and public health management. This tutorial explores the transformative potential of Artificial Intelligence (AI) in collaborative diagnostic workflows leveraging medical imaging. Participants will gain insights into the fundamental principles of AI, including machine learning and deep learning, and their application in analyzing chest X-rays and CT scans for the detection of tuberculosis and COVID-19. The session will cover data preprocessing techniques, model development strategies, and evaluation metrics relevant to medical image analysis. Furthermore, it will delve into the collaborative aspects of AI-driven diagnostics, highlighting tools and methodologies that enable seamless interaction between AI systems and healthcare professionals. Through practical examples and case studies, attendees will understand how AI can augment diagnostic accuracy, improve efficiency, and facilitate remote expert consultation in the fight against these infectious diseases.

Keywords:
Artificial Intelligence, Medical Imaging, Tuberculosis, COVID-19, Deep Learning, Machine Learning, Chest X-ray, CT Scan, Diagnostic Collaboration, Healthcare, Image Analysis.

Covered Topics:
The covered topics include:

  • Introduction and welcome
  • Understanding tuberculosis and COVID-19 imaging
  • Fundamentals of AI in medical imaging
  • AI for tuberculosis and COVID-19 diagnosis: techniques and models
  • Collaborative AI in diagnostics
  • Practical considerations and future directions
  • Q&A and closing remarks

Prerequisites for Participants:
None.


Broadening CI Workforce Development for Quantum-Based Machine Learning Research in Science and Engineering

Presenter: Dan Lo (Kennesaw State University – USA)
Type of Event: Workshop
Date: Saturday, April 26, 2025
Time: 09:00–11:00 (Eastern Time)
Duration: 2 hours
Room: Dempster Hall 026

Abstract:
Quantum-based machine learning (QML) applies quantum computing (QC) to machine learning (ML), which revolutionizes computational tasks by leveraging the unique properties of quantum mechanics, leading to more efficient solutions in science and engineering (S&E). However, there is a shortage of QML research workforce for S&E. In addition, QML is absent in most colleges’ curricula, and there is a need for hands-on QML training materials. To meet these challenges, Kennesaw State University and Florida Agricultural and Mechanical University (HBCU) will collaboratively build QML research, education capacity, and workforce in S&E. We will develop open-source, hands-on QML training materials on a dedicated open repository, including nine transferable modules with hands-on labs that cover key concepts of QC and QML in computer science (CS) and their applications in industrial engineering (IE), integrate the modules into existing CS and IE courses, and host faculty workshops and student training camps to train faculty and students with the developed hands-on QML training modules. Through these efforts, the project aims to (1) train and empower S&E faculty and students with QML skills and enhance their real-world research capability with advanced quantum cyberinfrastructure and (2) build diverse and multidisciplinary communities of collaborative QML research in S&E.

Keywords:
Quantum Computing, Machine Learning, Quantum Machine Learning, Quantum Neural Network, Quantum Support Vector Machine.

Covered Topics:
The covered topics include:

  • Introduction to Quantum Machine Learning
  • Quantum algorithms for square, cosine, and polynomials
  • Quantum Support Vector Machine
  • Quantum Neural Network

Prerequisites for Participants:
Linear Algebra, Machine Learning, Python Programming.