Machine learning systems are constantly making decisions based on data. They decide what ads you see when you visit a webpage, whether or not your credit card transactions are flagged as fraudulent, and whether a self-driving car accelerates or brakes—all without humans in the loop. Hackers can (and do!) manipulate data to trick these systems. Adversarial Learning researchers try to make life harder for these hackers. Assistant Professor of Computer Science Scott Alfeld will discuss prior research projects on adversarial learning led by his students. These include attacks against learning systems, methods of hardening learners against attackers, and sneakily stealing data from sequential learners. This discussion will be moderated by Beitzel Professor in Technology and Society (Statistics and Data Science) Nicholas Horton.
Scott Alfeld, assistant professor of computer science, teaches topics in AI, machine learning and security, ranging from the highly practical to the purely theoretical. Alfeld aims to span the same spectrum in teaching introductory courses as well. Alfeld graduated from the University of Utah and earned both a M.S. and Ph.D. from the Department of Computer Sciences at the University of Wisconsin-Madison.
Alfeld’s primary research is at the intersection of machine learning and security. They study settings where an intelligent adversary has limited access to perturb data fed into a learned or learning system. The goal of this research is two-fold: to detect attacks and to build/augment learning systems to be more robust to undetected attacks. In addition, they develop methods for inferring properties of the underlying sensors (whether trustworthy or not) and incorporating that knowledge into the data analysis pipeline.
Before coming to Amherst, Alfeld taught computer science and public speaking/debate professionally in Salt Lake City, Utah, and Madison, Wis. As a volunteer, they gave guest lectures for courses from the Wisconsin Center for Academically Talented Youth (a nonprofit organization offering courses for advanced students in grades 2 through 12) and taught locksport (recreational lockpicking and related physical security topics) through Sector67 in Madison.
Nicholas Horton, the Beitzel Professor in Technology and Society (Statistics and Data Science), teaches a variety of courses in statistics, data science and related fields, including probability, mathematical statistics, regression and design of experiments. Horton is passionate about improving quantitative and data literacy for students with a variety of backgrounds as well as engagement and mastery of higher-level concepts and capacities to undertake research. Horton graduated from Harvard College and earned a Sc.D. from the Harvard School of Public Health.
Horton has won a number of teaching awards, including the Undergraduate Teaching Award from the Boston Chapter of the American Statistical Association in 2018, the Robert V. Hogg Award For Excellence in Teaching Introductory Statistics from the Mathematical Association of America in 2015, and the Journal of Statistics Education award for best paper in 2011.
As an applied biostatistician and data scientist, Horton’s work is based squarely within the mathematical, statistical and computational sciences, but spans other fields in order to ensure that research is conducted on a sound footing. The real-world data problems that these investigators face often require the use of novel solutions and approaches, since existing methodology is sometimes inadequate. Bridging the gap between theory and practice in interdisciplinary statistics and data science settings is often a challenge, and has been a particular focus of Horton’s work.