Camelia Simoiu, ASA
Management Science & Engineering Department
email: csimoiu [at] stanford [dot] edu
I am a PhD candidate at Stanford University in the Management Science and Engineering Department, working in the emerging area of computational social science.
My research interests are in developing statistical methods and data-driven tools to evaluate and design effective public policy in the areas of cyber security and criminal justice.
My current focus is on the behavioural and economic aspects of cyber security.
I draw on techniques from machine learning, Bayesian analysis, computational statistics, and social network analysis.
Previously, I was as a Fellow of the University of Chicago’s Data Science for Social Good program, and a visiting researcher at the MIT Media Lab in the Human Dynamics group. Prior to graduate school, I was a consulting actuarial analyst at Mercer, focusing on quantitative risk modeling. During my time there, I earned the ASA (Associate of the Society of Actuaries) designation, and initialized and taught a data analysis workshop for classes of 50 undegraduate students at the University of Toronto. I received a B.S. in applied statistics (actuarial science) from the University of Toronto and a M.S. in artificial intelligence from the University of Amsterdam.
At risk of ransomware: A behavioral analysis of web browsing habits [Work in progress]
with Joseph Bonneau and Sharad Goel
Collective intelligence: an at-scale analysis [Work in progress]
with Chiraag Sumanth, Alok Mysore, and Sharad Goel
Stanford Crowd Research: Enabling Global Access to Research at Scale (MOOR) [Under review]
with Michael Bernstein, Sharad Goel, Rajan Vaish, Geza Kovacs, Ranjay Krishna, Imanol Arrieta Ibarra, Michael Wilber, Andreas Veit, Serge Belongie, James Davis, Snehalkumar (Neil) Gaikwad
A large-scale analysis of racial disparities in police stops across the United States [Under review]
With Emma Piersona, Jan Overgoor, Sam Corbett-Davies, Vignesh Ramachandran, and Cheryl Phillips.
The Problem of Infra-marginality in Outcome Tests for Discrimination
With Sharad Goel and Sam Corbett-Davies. Annals of Applied Statistics, forthcoming.
Investing the Wisdom of the Crowds at Scale ACM UIST 2015, Charlotte, NC.
With Mysore A. et. al.
Understanding and Improving Energy Consumption in Commercial Buildings, Knowledge Discovery and Data Mining conference, 2013
With Scott Alfeld, and Andrea Fernandez-Conde, poster with oral presentation
In the News
08/16 Vox. Study: police officers have lower standards for searching black people than white people
06/16 Stanford researchers develop new statistical test that shows racial profiling in police traffic stops
02/16 Stanford Traffic Stops Database Will Let Public Analyze Racial Profiling
02/16 Engineers battle bias in the criminal justice system
02/16 Stanford engineers' 'Law, Order & Algorithms' data project aims to identify bias in the criminal justice system
07/13 New Data Project Seeks to Raise Investor Confidence in Energy Efficiency