Artificial Intelligence Series

January 20, 2026 (4-5 pm EST) | Self-Supervised Learning  Workshop Slides Recording

Self-Supervised Learning (SSL) is a powerful machine learning paradigm that enables models to learn directly from unlabeled data by predicting parts of the data from other parts of the same input. This workshop introduces the fundamentals of SSL, explains why it has become essential for modern AI, and demonstrates how SSL supports strong representation learning for downstream tasks such as classification, segmentation, anomaly detection, and forecasting. This session includes real-world case studies (with notebooks), including SSL with LoRA fine-tuning and an application in subglacial mapping.

January 27, 2026 (2-4 pm EST) | Reproducible AI: GitHub & Version Control  Workshop Slides Recording

AI Reproducibility can be defined as the ability to utilize similar code and data to regenerate research findings. Alternatively, it can be perceived as achieving the same outcomes with distinct data and methods. With significant research in AI depicted by growth in number of publications, attempts to reproduce research findings are often met with challenges. These include accessibility to research artifacts such as code, communication hindrances with authors, time constraints, external randomness, under-reporting of hyper-parameters, inconsistency in computing environments, to mention a few. This workshop introduces the importance of AI reproducibility and takes a hands-on approach on leveraging version control systems like GitHub, which thus enable tracking changes in research artifacts like code, data, models, hyperparameters, dependencies, among others.