Jordan Awan - My primary research interest is in data privacy, where the goal is to publish meaningful statistical results on sensitive datasets, without compromising the privacy of the participants in the dataset. In particular, I mostly work in the framework of differential privacy, which has been adopted by a number of tech companies as well as the US Census. Some data privacy problems that I am particularly interested in are 1) performing valid statistical inference subject to privacy constraints (e.g., confidence intervals, hypothesis tests, posterior inference), 2) designing privacy-aware algorithms for a variety of tasks (e.g., functional data analysis, topological data analysis), and 3) foundations of data privacy (e.g., definitions of privacy, optimizing basic privacy mechanisms) I also work with a variety of scientists as an applied Statistician on problems related to 1) diagnosing and treating voice disorders, 2) developing novel methods of low-cost spirometry, and 3) studying the basic laws of physics. Before transitioning to Statistics, I worked on dicrete mathematics problems in graph theory, matroid theory, and discrete geometries. I studied at Clarion University from 2011-2014, earning a B.S. in Mathematics. After this, I completed a M.A. in Mathematics at Brandeis University in 2016 under the advisement of Dr. Olivier Bernardi. In May of 2020, I completed my Ph.D. in Statistics at Penn State University, advised by Dr. Aleksandra Slavkovic and Dr. Matthew Reimherr. Currently I am an Assistant Professor of Statistics at Purdue University. I also work as a differential privacy consultant for the federal non-profit, MITRE.