In May 2016, I received a Ph.D. in Statistics from UC Berkeley, where I was advised by Bin Yu. I was also fortunate to be mentored by Jasjeet Sekhon, Julien Mairal, and Ameet Talwalkar. Prior to my Ph.D., I spent a year working in the neuroscience lab of Scott Emmons at the Albert Einstein College of Medicine. In a previous life, I was a pianist, and spent two years as a fellow at the Bard College Conservatory of Music after getting a master's degree at the Yale School of Music. I did my undergraduate studies in mathematics and music at Yale College. Here is my CV.
335 Evans Hall
University of California, Berkeley
Berkeley, CA 94720
email: adam at stat dot berkeley dot edu
I am an applied statistician, and am particularly interested in computational neuroscience and causal inference in the social sciences and medicine. My research involves analyzing large datasets, and I am generally interested in scalable methods for parameter estimation and statistical inference. My research has led me to do a lot of programming, particularly in R, C++, and Scala. In the summer of 2015 I was a software engineering intern at Google in Mountain View, and in the summer of 2014 I was a research assistant at the UC Berkeley AMPlab, working on bioinformatics.
Jalil Kazemitabar, Arash Amini, Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 426-434. (link), and Ameet Talwalkar. "Variable importance using decision trees." In
Proceedings of the National Academy of Sciences 113, no. 27 (2016): 7383-7390. (link)*, Hanzhong Liu*, Cun-Hui Zhang, Jasjeet Sekhon, and Bin Yu. "Lasso adjustments of treatment effect estimates in randomized experiments."
Jarrell, Travis A.*, Yi Wang*, Science 337, no. 6093 (2012): 437-444. (link), Christopher A. Brittin, Meng Xu, J. Nichol Thomson, Donna G. Albertson, David H. Hall, and Scott W. Emmons. "The connectome of a decision-making neural network."
* Indicates joint first authors
Courses I've TA'd at Berkeley:
I was also a teaching assistant at the 2013 IMA short course on applied statistics and machine learning at the University of Minnesota.
Courses I took at Berkeley: probability theory, theoretical statistics, applied statistics, machine learning, applications of parallel computing, causal inference.
My wonderful wife, Abby, is an opera singer and a professor of voice at the CU Boulder College of Music. In March 2015, we welcomed our son, Ewan, to the world.