Topic: Interdisciplinarity of Data Science and AI: Driving Innovation and Advancing Societal Impact. The ubiquity of AI and data science is spurred by significant factors.. In this talk, I’ll present elements of our research program and vision that is centered on the principles of data & society, including partnerships, fostering innovation in data science and AI while generating educational opportunities, societal impact, and entrepreneurship.

Deep Learning to identify MRI signatures along the age continuum using Brain Age. Teresa Wu is a Professor from School of Computer and Augmented Intelligence (SCAI), Arizona State University and an adjunct Professor of Radiology in College of Medicine, Mayo Clinic. Her current research interests include imaging informatics and clinical decision support. Professor Wu has published ~140 journal articles in journals such as NeuroImage, NeuroImaging: Clinical, Brain Communication, IEEE Transactions on Pattern Analysis and Machine Intelligence, Information Science. The objective of this study is to assess a deep learning approach to characterizing brain age signatures using MRI from cognitively normal subjects and explore its potential as biomarkers in neurodegenerative disease (e.g., Alzheimer’s disease) diagnosis.

Topic: Active Machine Learning: Combining Human and Artificial Intelligence for Improved Learning Efficiency and Accuracy Active machine learning combines the power of artificial intelligence with human intelligence to improve the efficiency and accuracy of machine learning algorithms. The fundamental idea behind active learning is to actively engage the human user in the learning process by selecting the most informative data points for labeling, thus reducing the amount of labeled data required to achieve a given level of accuracy. In this talk, we will discuss the theory and applications of active machine learning, including various sampling strategies, query selection methods, and active learning algorithms.

Topic: Two Decades of Low-Rank Optimization Big data matrices are often low rank, and so modern optimization algorithms often require the use of low-rank decision variables. Such variables also arise naturally in certain optimization problems, irrespective of the input data. As a result, over the past two decades, low-rank optimization has grown as a critical area of research in optimization and machine learning. However, enforcing low-rank structure on a variable gives rise to severe...