As part of the Clarence Tow Lecture Series, the Economics Department in the Tippie College of Business is hosting “Will Cities Survive?” a public presentation by Edward Glaeser, who is the Fred and Eleanor Glimp Professor of Economics and Chairman of the Department of Economics at Harvard University.

Professor Glaeser has served as the Director of the Taubman Center for State and Local Government and as Director of the Rappaport Institute for Greater Boston. He is a world-renowned leader in...

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, including 1) major methodological and computational advances; 2) the increasing availability of problem statements in sciences, engineering, business and the social sciences; and 3) an increasing demand from academia, research institutions, corporations, and governments for talent. This is presenting unparalleled...

Topic: Deep Learning to identify MRI signatures along the age continuum using Brain Age.

Imaging biomarkers are being increasingly applied for early diagnosis and staging of disease in humans. Developing imaging biomarkers requires advances in both image acquisition and analysis. In recent years, deep learning has rapidly dominated the computer vision field with advances also diffusing into the medical field. The objective of this study is to assess a deep learning approach to characterizing...

Kristin F. Butcher, Vice President and Director of Microeconomic Research at the Federal Reserve Bank of Chicago, will present "The Impact of State Paid Leave Laws on Firms and Establishments: Evidence from the First Three States."

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...

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...