Department of Computer Science
Cornell University

David Bindel is an associate professor of computer science at Cornell and a visiting scholar in statistics at University of Chicago. He works broadly in applied numerical methods, with a particular interest in numerical methods for problems in data science. Over the past ten years, he has worked on several such problems, including rapid computation of parameterized rankings, community detection in network data, spectral methods for topic modeling, scalable Gaussian process regression and kernel learning, and analysis of networks through spectral densities. In Spring 2018, he launched a new graduate course on 'Numerical Methods for Data Science’ at Cornell University; he has also taught undergraduate courses on some of the same material at Shanghai Jiao Tong University in June 2018 and May 2019, and has given a lecture series on the subject at University of Maryland in April 2019. He is currently working on a book of the same title.

Department of Mathematics
University of Vienna

Educated at University of Vienna (BSc, MSc) with a specialization in applied mathematics and scientific computing, Julius Berner became very interested in machine learning and neural networks, in particular.

Currently working towards a doctoral degree at University of Vienna under the supervision of Prof. Dr. Philipp Grohs, his research focuses on the mathematical analysis of deep learning based methods at the intersection of approximation theory, statistical learning theory, and optimization.

Department of Mathematics
University of Vienna

Studied at TU Munich and is currently working on his doctoral degree at the University of Vienna under the supervision of Prof. Philipp Grohs. Has a background in Harmonic Analysis and Approximation Theory, and is currently interested in the Mathematics of Deep Learning.