Accurately estimating subsurface tumor depth during fluorescence-guided surgery remains an open challenge, as current intraoperative methods provide only surface-level fluorescence contrast without quantitative depth information. Spatial Frequency Domain Imaging offers a pathway toward quantitative fluorescence imaging by capturing both reflectance and optical property maps; however, its application to tumor depth estimation is limited by the scarcity of patient-derived datasets and significant domain gaps between simulated and experimental measurements. To address these challenges, we propose U-KAN, a hybrid deep learning framework that combines a siamese attention U-Net for spatial feature extraction with a Kolmogorov-Arnold Network (KAN) regression head for functional depth mapping. The U-Net captures morphological and structural cues from fluorescence and optical property inputs, while the KAN performs nonlinear pixelwise regression to improve generalization under limited data. Experiments on diffusion-theory, Monte Carlo, and patient-derived phantom datasets demonstrate that the hybrid model achieves more accurate and robust tumor depth estimation than either model alone, establishing a promising foundation for quantitative, depthaware fluorescence imaging in surgical oncology. U-KAN reduced tumor-region MAE by more than 30 % and cut minimum-depth errors nearly in half on phantom data, demonstrating markedly improved cross-domain robustness.

In this paper, we focus on the certificate redesign of our machine learning program within the context of a three-program bundle; we used an outcome-based approach with the student's targeted career outcome in mind. The purpose of this curriculum redesign is threefold: (1) To align the course content with the industry's rapidly changing needs and demands of the job market; (2) To identify the proper sequence of laddering structure for students taking multiple programs; and (3) To create a natural and streamline flow of the learning experience (i.e., removing overlapping content). We show that the resulting redesign is a holistic set of certificate programs with industry-relevant content tailored for skills-based experiential learning.

Three previous Working Groups (WG) have met at ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula by mapping industry job skills to knowledge areas (KAs) and KAs to student learning objectives (LOs) and using these as the framework for a repository of learning materials and course exemplars \citefoster2018, foster2019, adams2020. The ongoing value of the work of these WGs will be enhanced by validating the KAs and LOs and their mapping to current job skills and continuing to build a community of educators who will contribute to and benefit from the repository.

As machine learning (ML) becomes prevalent in industries and businesses, the need to use these algorithms to solve real-world problems grows rapidly. However, there is a serious deficit of qualified talent in this field and thus a corresponding shortage of educational programs. To address the shortage of ML specialists in the field, universities are offering continuing education programs that fast-track the development of technical and transverse skills needed for success in the field. The award-winning machine learning program described in this paper is carefully designed with industry and community partners while focusing on practical skills and participation in the local industry network. This program can be summarized in three learning principles: 1) learners are encouraged to build their knowledge and skills in a self-directed manner; 2) group projects in both simulated and workplace settings are incorporated to support problem-based learning; and 3) supportive learning environment is established to encourage open and safe learning. This paper reports on the instructors' experiences in teaching the four courses based on these principles, which has resulted in high satisfaction from students, successfully placing students in industry, and winning a national award. We offer this experiential report in the hope that it may serve as a point of reference for other instructors and programs for mature technical learners in machine learning.

The accelerating evolution and adoption of cloud computing services is generating increased demand for job skills in this domain. To address this growth, higher education has identified the importance of cloud computing courses that are practical and compatible with this rapidly changing field. This is especially relevant as cloud services are becoming common computing resources for many new computational approaches and advanced subjects such as machine learning and data science. The ability to incorporate specific components of cloud computing teaching content into a variety of courses has become important. However, the lack of availability of high-quality teaching material that is easy to integrate, when teaching rapidly evolving cloud-related concepts continues to be a challenge for instructors. This working group will try to address this challenge.