Camelia Simoiu, ASA
PhD Candidate

Computational Social Science
Stanford University

email: csimoiu [at] stanford [dot] edu

Curriculum Vitae


My research focuses on designing and evaluating algorithmic tools to aid human decision-making
in the areas of cyber security and criminal justice. I draw on methods from machine learning, statistics,
and online experiments. I am part of the Stanford Computational Policy Lab, advised by Professor Sharad Goel.
Our work has been featured in the The Economist, The Daily Show, NBC News, and Vox.

Previously, I was 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. I received a B.S. in applied statistics from the
University of Toronto and a M.S. in artificial intelligence from the University of Amsterdam. Outside of work,
I enjoy hiking, road biking on California's back roads, and diving.


"I was told to buy a software or lose my computer. I ignored it":A study of ransomware
Camelia Simoiu, Christopher Gates, Joseph Bonneau, Sharad Goel.
Fifteenth Symposium on Usable Privacy and Security (SOUPS), 2019

Studying the "Wisdom of Crowds" at Scale [Best Paper Award]
Camelia Simoiu, Chiraag Sumanth, Alok Mysore, Sharad Goel
Seventh AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2019
data | slides

A large-scale analysis of racial disparities in police stops across the United States [Working Paper]
Emma Pierson, Camelia Simoiu, Jan Overgoor, Sam Corbett-Davies, Vignesh Ramachandran, Cheryl Phillips, Sharad Goel
Stanford Open Policing project | data | code | press

The Problem of Infra-marginality in Outcome Tests for Discrimination
Camelia Simoiu, Sam Corbett-Davies, Sharad Goel
Annals of Applied Statistics, 2017
data | code | slides

Crowd Research: Open and Scalable University Laboratories.
with Rajan Vaish, Michael Bernstein, Geza Kovacs, et. al.
UIST 2017

Investigating the Wisdom of the Crowds at Scale
Alok Shankar Mysore, Camelia Simoiu, Sharad Goel, et al.
UIST 2015


Data Study Group Final Report: Imperial College London, Los Alamos National Laboratory, Heilbronn Institute, Alan Turing Institute, 2018

Quantifying Systemic Cyber Risk, Report on the "Connectedness in Cyber Risk" Workshop, Global CRQ Network, 2018

Selected talks

03/2020 Facebook Core Data Science, Menlo Park, CA.

10/2019 Cybersecurity & Privacy Festival (Defending the Human), Stanford, CA.

10/2019 Seventh AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Stevenson, WA.

08/2019 Fifteenth Symposium on Usable Privacy and Security (SOUPS), Santa Clara, CA.

05/2019 WWW CyberSafety Workshop, San Francisco, CA.

05/2019 Google (Apps UX Research), Sunnyvale, CA

12/2018 Eleventh International Conference on Computational and Methodological Statistics (CMStatistics), Pisa, Italy.

12/2018 Stanford Computer Science Security Lunch, Stanford, CA.

05/2018 Electronic Frontier Foundation, San Francisco, CA.

04/2018 Santa Clara University, AI for social impact speaker series, Santa Clara, CA.

12/2017 StanCon2018, Pacific Grove, CA.

10/2017 Conference On Digital Experimentation (CODE), Boston, MA.

05/2017 World Economic Forum, Cyber Risk Quantification Workshop, San Diego, CA.

11/2016 TEDxBeaconStreet, Cambridge, MA.

08/2016 Joint Statistical Meetings (JSM), Chicago, IL.

07/2016 2nd Annual International Conference on Computational Social Science (CSS), Evanston, IL.


I have served on the program committee for the following conferences:

Co-organized the Behavioral security seminar, Stanford, CA, 2018

Press coverage

10/17 Stanford News. A Stanford-led platform for crowdsourced research gives experience to global participants
08/17 The Daily Show. A Stanford University study uncovers racial disparity in routine traffic stops
06/17 The Economist. Measuring racial bias in police forces
06/17 NBC News. Police searches drop dramatically in states that legalized marijuana
06/17 The Daily Mail. Police officers are more likely to cite black or Latino drivers than whites during traffic stops

08/16 Vox. Study: police officers have lower standards for searching black people than white people
08/16 Vox. Commentary in VOX
06/16 Stanford News. Stanford researchers develop new statistical test that shows racial profiling in police traffic stops
02/16 The Observer. Stanford Traffic Stops Database Will Let Public Analyze Racial Profiling
02/16 Engineers battle bias in the criminal justice system
02/16 Stanford News. Stanford engineers' 'Law, Order & Algorithms' data project aims to identify bias in the criminal justice system

05/2019 Excited to be joining Google this summer for a research internship.
12/2018 Excited to be participaing in the Data Science Study Group, Alan Turing Institute, London, UK
07/2017 Excited to be joining Symantec this summer for a research internship.
07/2017 Our paper on crowd research won a Best paper honorable mention award at UIST.
07/2017 Leading a data science workshop at IRE 2017 Investigative Reporters and Editors Conference
05/2017 Invited to attend the GREPSEC III Workshop on computer security research.
11/2016 New blog post on Bayesian statistics: A bag of tips and tricks for dealing with scale issues with Jim Savage
09/2016 Our paper "A large-scale analysis of racial disparities in police stops across the United States" accepted for presentation at the Computation + Journalism 2016 Conference at Stanford
09/2016 Awarded ACCEL Fellowship, courtesy of the MS&E Department, Stanford
08/2016 Our paper "Testing for Racial Discrimination in Police Searches of Motor Vehicles" voted most interesting papers on arXiv (week 08/06)
03/2016 Awarded ACCEL Fellowship, courtesy of the MS&E Department, Stanford
02/2016 Selected as one of RSA Scholars. Thank you RSA!
06/2015 Winner of the Knight News Challenge on our project Law, Order & Algorithms. With Sharad Goel, Sam Corbett-Davies, and Vignesh Ramachandran
11/2015 Invited poster with oral presentation at ACM UIST 2015 on collective intelligence
02/2015 Excited to lead the data science team of the Aspiring Researcher Challenge along with Sharad Goel, Rajan Vaish, and Imanol Arrieta Ibarra