Nathan Kallus is a Professor of Operations Research and Information Engineering at Cornell University and Cornell Tech, starting Fall 2016. His research revolves around data-driven decision making in operations, the interplay of optimization and statistics in decision making and inference, and the analytical capacities and challenges of unstructured, large-scale, and web-based data. His works span basic theory, effective methodology, and novel applications and has been recognized by awards.
Nathan hails from the town of Haifa, Israel. He holds a PhD in Operations Research from MIT as well as a BA in Pure Mathematics and a BS in Computer Science both from UC Berkeley. Previously, Nathan has been a Visiting Scholar at USC's Department of Data Sciences and Operations and a Postdoctoral Associate at MIT's Operations Research and Statistics group.
Applied Machine Learning
This course will help students learn and apply key concepts of modeling, analysis and validation from Machine Learning, Data Mining and Signal Processing to analyze and extract meaning from data. Students will implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. They will also gain a working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, association rule mining and dimensionality reduction.