Camelia Simoiu, ASA
Computational Social Science
email: csimoiu [at] stanford [dot] edu
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
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
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 |
The Problem of Infra-marginality in Outcome Tests for Discrimination
Camelia Simoiu, Sam Corbett-Davies, Sharad Goel
Annals of Applied Statistics, 2017
Crowd Research: Open and Scalable University Laboratories.
with Rajan Vaish, Michael Bernstein, Geza Kovacs, et. al.
Investigating the Wisdom of the Crowds at Scale
Alok Shankar Mysore, Camelia Simoiu, Sharad Goel, et al.
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
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
- Reviewer, Debugging Machine Learning Models, ICLR 2019 workshop.
- Reviewer AI for Social Good, ICML 2019 Workshop.
- Reviewer, RSAC Security Scholar Program, 2017-2018, San Francisco, CA.
- Local chair, ACM Conference on Online Social Networks (COSN), Stanford, CA.
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 Phys.org 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