Norm Matloff's Statistics Bio

(Updated July 30, 2018)

I sometimes am asked about my "unusual background," combining Statistics and Computer Science (CS). Below is my history in that regard. See also my full bio.

• I was one of the 7 original founding members of the UC Davis Department of Statistics, which was a spinoff from the Math Dept. The Stat Dept. was established after I had been in Math for 4 years, as an Assistant Professor.
• In the Stat Dept., I taught undergrad courses in math stat, regression analysis, multivariate analysis (classical version), reliability methods and statistical computing, and taught the grad course in linear models. In the Math Dept., I also taught a course in stochastic processes.
• My early career publications included stat journals such as the Annals of Probability, Biometrika, Statistics and Probability Letters and JRSS C.
• The year the Stat Dept. was formed, I spent a year in Silicon Valley, working on database systems. When I returned to the university, I was given a joint appointment in Stat and Electrical and Computer Engineering, majority in Stat, with the understanding that I would eventually be majority in ECE. Four years later a CS Dept. formed, and I joined that. Unfortunately, the administration did not want to continue a joint appointment for me, but I have continued to work in statistics ever since.
• My statistical work has appeared in CS outlets such as the ACM Trans. on Database Systems, the ACM Trans. on Modeling and Computer Simulation, the IEEE Trans. on Data Engineering, the IEEE Trans. on Reliability, Microelectronics and Reliability, the IEEE Transactions on Knowledge and Data Engineering, Performance Evaluation and so on.
• In the last few years, my work in statistical computing has been recognized through presenting invited papers at Interface 2012: the Future of Statistical Computing; userR! 2012; JSM 2013; and GTC 2016 (GPU computing). I will give a keynote address at useR! 2017 in Brussels in July 2017, and will present another invited paper in the Small Area Estimation conference in Paris, also in July.
• My book, Parallel Computation for Data Science, was published by Chapman and Hall in 2015.
• My book, Statistical Regression and Classification: from Linear Models to Machine Learning, published by Chapman and Hall in 2017, was selected for the Ziegal Award of 2017.
• I am on the editorial boards of the Journal of Statistical Software and the R Journal, and was on the organizing committee of useR! 2016, sponsored by the Stanford University Statistics Dept.
• I teach probability and statistics in the CS Dept., in ECS 132 and 256.
• My current projects in mainstream statistics include:
  • A New Framework for Random Effects Models
  • A New Method for Avoiding Data Disclosure While Automatically Preserving Multivariate Relations
  • A Different Approach to the Problem of Missing Data
  • Improved Estimation of Class Prior Probabilities through Unlabeled Data