This course aims to improve data visualization literacy—the expertise and skills needed to read and make data visualizations. It teaches theoretical foundations and advanced tools that help turn data into insights.

The visual representation of information requires a deep understanding of human perceptual and cognitive capabilities, data mining and visualization algorithms, interface and interaction design, as well as creativity. Data is typically non-spatial and needs to be mapped into a physical space that represents relationships contained in the information faithfully and efficiently. If done successfully, data visualizations combine human and machine intelligence to solve tasks that neither could accomplish alone. This course provides an overview of the state-of-the-art in information visualization. It teaches the process of producing effective temporal, geospatial, topical, and network visualizations. Specifically, the course covers:

• visualization frameworks that guide development,

• data analysis algorithms that enable extraction of structures and trends in data,

• major visualization and interaction techniques,

• discussions of systems that drive research and development, and

• trends, opportunities, and challenges in the field.

The course objective is to provide students with a working knowledge of how to effectively visualize abstract information and hands-on experience in the application of this knowledge to specific domains, different tasks, and diverse, possibly non-technical users. 

This short course, “Deep Learning for Unstructured Medical Data,” explores advanced machine learning techniques for analyzing complex healthcare data such as medical images, clinical notes, and genomic sequences. Participants will learn key deep learning architectures, including convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) and transformers for natural language processing (NLP) in medical contexts. The course emphasizes real-world applications, such as disease diagnosis, patient outcome prediction, and personalized treatment planning. Hands-on sessions using popular frameworks like TensorFlow and Keras will equip learners with practical skills to handle unstructured data effectively.

This lecture series introduces the core concepts of machine learning with a focus on biomedical applications. Theoretical sessions cover fundamental models, including linear and logistic regression, and an introduction to neural networks. Practical sessions provide hands-on experience implementing these models using Python and relevant libraries (scikit-learn, PyTorch, PyTorch Geometric). Participants will work with real biomedical datasets, gaining insights into model training, evaluation, and interpretation. An additional session explores graph neural networks and their applications in biomedical research.

The course aims to provide participants with the skills to analyze, model, and predict the behavior of complex networks. They will explore fundamental concepts of graphs and networks, mathematical representations, and key metrics such as closeness, betweenness, and PageRank. The curriculum covers universal properties of networks, including the small-world effect, scale-freeness, and clustering, as well as foundational network models such as the Erdős–Rényi and Barabási–Albert models. Participants will study network robustness, percolation transitions, and the dynamics of information and epidemic spreading. The course also introduces network motifs and community detection methods to uncover hidden structures, with a particular emphasis on biomedical networks and applications. Theoretical knowledge is reinforced through practical analyses and modeling exercises.