Adam Bloniarz

Software Engineer, Google

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.

Publications and Preprints

Jalil Kazemitabar, Arash Amini, Adam Bloniarz, and Ameet Talwalkar. "Variable importance using decision trees." In Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 426-434. (link)

Adam Bloniarz, Christopher Wu, Bin Yu, and Ameet Talwalkar. "Supervised neighborhoods for distributed nonparametric regression." In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, pp. 1450-1459. 2016. (link) (code)

Adam Bloniarz*, Hanzhong Liu*, Cun-Hui Zhang, Jasjeet Sekhon, and Bin Yu. "Lasso adjustments of treatment effect estimates in randomized experiments." Proceedings of the National Academy of Sciences 113, no. 27 (2016): 7383-7390. (link)

Adam Bloniarz*, Ameet Talwalkar*, Jonathan Terhorst*, Michael I. Jordan, David Patterson, Bin Yu, and Yun S. Song. "Changepoint Analysis for Efficient Variant Calling." In Research in Computational Molecular Biology, pp. 20-34. Springer International Publishing, 2014. (link) (code)

Jarrell, Travis A.*, Yi Wang*, Adam Bloniarz, 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." Science 337, no. 6093 (2012): 437-444. (link)

* Indicates joint first authors

Courses I've TA'd at Berkeley:

  • Stat 215, Graduate applied statistics. Topics covered included exploratory data analysis, PCA, clustering, classification, regression, high-dimensional statistics, parallel computing.
  • Stat 133, Concepts in computing with data. Topics covered included fundamentals of R programming, data visualization, XML, regular expressions.

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.

Some old recordings of me playing piano, and a video of a recent concert I gave at Berkeley with Fanny Yang.

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.