Student Camp on Quantum-Based Machine Learning (QML)
The camp will be held on January 28-30, 2026 (online)
Quantum-based machine learning (QML) is at the forefront of technological innovation, with the potential to revolutionize fields ranging from artificial intelligence to cybersecurity. As industries and academia increasingly invest in quantum technologies, students who gain expertise in QML will be uniquely positioned for future careers in this transformative domain. To meet the growing demand for QML education, we invite students to participate in our immersive Student Camp on QML.
Why Join?
This camp provides an unparalleled opportunity to:
Deepen your understanding of QML through hands-on learning based on our specialized QML modules.
Play a pivotal role in shaping the future of QML education by providing feedback to enhance, improve, and update the learning modules.
Explore research opportunities by connecting with faculty looking for student researchers for supervision and collaboration in future QML projects.
Contribute to the promotion and dissemination of QML learning content, ensuring its wide and sustained adoption.
What You’ll Gain
A strong foundation in QML concepts and applications.
The chance to influence and improve educational resources in quantum computing.
Potential research opportunities with leading faculty in QML.
Join us in this exciting opportunity to learn, contribute, and shape the future of quantum-based machine learning!
We look forward to seeing you at the forefront of quantum innovation!
This workshop is sponsored by the National Science Foundation (NSF), NSF Award #2413540。
How to Apply: Please fill out the application form here and reach out to Dr. Luisa Valentina Nino de Valladares lvallad1@kennesaw.edu and Dr. Yong Shi yshi5@kennesaw.edu with questions.
Deadline of Application: January 20, 2026
Student Camp Dates: January 28-30, 2026 (online).
Screening and selecting participants will be based on the following criteria:
1. Academic Background and Level
2. Interest and Motivation
3. Commitment & Availability
Stipend: Selected participants will receive $300 to compensate for their time and effort.
Agenda:
This material is based upon
work supported by the
National Science Foundation
Under Grant No. NSF Award #2413540
Contact :
Yong Shi (Lead Principal Investigator) yshi5@kennesaw.edu
Hongmei Chi (Lead Principal Investigator) hongmei.chi@famu.edu
Luisa Valentina Nino de Valladares (Principal Investigator) lvallad1@kennesaw.edu