PeopleofSuperintelligence

Researcher

Ravi Shroff

Ravi Shroff is a statistician and computational social scientist who studies how algorithms and simple statistical rules can improve decisions in criminal justice and child welfare. He earned a mathematics PhD at UC San Diego and a master's degree in applied urban science and informatics at NYU's Center for Urban Science and Progress, and he was a 2016–2017 fellow at the Data & Society Research Institute. With Sharad Goel and other coauthors, he wrote "Simple rules for complex decisions" in 2017, which builds weighted-checklist rules for pretrial release decisions, and "Causal Conceptions of Fairness and their Consequences," which won an Outstanding Paper award at ICML in 2022. The Journal of Machine Learning Research published his coauthored paper "The Measure and Mismeasure of Fairness" in 2023. He is an associate professor of applied statistics at NYU Steinhardt and program director of Statistics and Computational Social Science.

Timeline

  1. By 2016

    From UC San Diego to CUSP

    His degrees are a BS in mathematics and economics from the University of Washington, a PhD and MS in mathematics from UC San Diego, and an MS in applied urban science and informatics from NYU's Center for Urban Science and Progress (CUSP). By 2016, he is a research scientist at CUSP who specializes in computational social science.

  2. 2016

    Risk assessments for stop-and-frisk

    With Sharad Goel and Justin M. Rao, he publishes "Personalized Risk Assessments in the Criminal Justice System" in the American Economic Review in 2016. The paper examines New York City's stop-and-frisk program and shows that risk assessment tools can help police officers make considerably better real-time stop decisions and help audit past actions.

  3. 2016–2017

    Data & Society fellowship

    He joins the Data & Society Research Institute as a 2016–2017 fellow, where he examines how simple computational models can be designed and implemented in city government. In a Databite talk published in June 2017, he covers his research on decision making in police departments, courts and child welfare agencies, and emphasizes that predictive tools should be used to aid the decision-making of the individual.

  4. 2017

    Simple rules for complex decisions

    In 2017, he coauthors "Simple rules for complex decisions" with Jongbin Jung, Connor Concannon, Sharad Goel and Daniel G. Goldstein. The paper presents select-regress-and-round, a method for building weighted-checklist rules that can be applied mentally; in a case study of pretrial release and detention decisions, the simple rules significantly outperform judges and match random forests trained on all available features.

  5. 2022

    An outstanding paper at ICML

    In 2022, he coauthors "Causal Conceptions of Fairness and their Consequences" with Hamed Nilforoshan, Johann Gaebler and Sharad Goel. The paper shows that popular causal definitions of algorithmic fairness almost always result in strongly Pareto dominated decision policies, and it wins an Outstanding Paper award at ICML. Less than 1% of accepted papers receive an award.

  6. 2023

    The measure and mismeasure of fairness

    In 2023, the Journal of Machine Learning Research publishes "The Measure and Mismeasure of Fairness," which he coauthors with Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan and Sharad Goel. The paper argues that the equitable design of algorithms requires grappling with their context-specific consequences, akin to the equitable design of policy.

  7. As of October 2026

    Associate professor at NYU Steinhardt

    He is an Associate Professor of Applied Statistics and Program Director of Statistics and Computational Social Science at NYU Steinhardt, with an affiliated appointment at NYU's Center for Urban Science and Progress. His current research examines interpretable statistical models for pretrial detention decisions, methods to quantify discrimination where decision makers have clear policy objectives, and how algorithms can assist human judgment.

Career

  1. NowNew York UniversityResearch Scientist
  2. 2016–2017Data & Society Research InstituteFellow

Education

  1. University of WashingtonBS
  2. University of California, San DiegoMS
  3. New York UniversityMS

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes New York University. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes New York University. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes New York University. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes New York University. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Ravi Shroff | NYU Steinhardt steinhardt.nyu.edu. Checked 22 October 2026.
  2. Ravi Shroff - Data & Society datasociety.net. Checked 22 October 2026.
  3. Personalized Risk Assessments in the Criminal Justice System ideas.repec.org. Checked 22 October 2026.
  4. Databite No. 101 | Stats and the City: A Data-Driven Approach to Criminal Justice and Child Welfare datasociety.net. Checked 22 October 2026.
  5. [1702.04690] Simple rules for complex decisions arxiv.org. Checked 22 October 2026.
  6. Causal Conceptions of Fairness and their Consequences icml.cc. Checked 22 October 2026.
  7. PRIISM - Dr. Ravi Shroff Wins Outstanding Paper Award | NYU Steinhardt steinhardt.nyu.edu. Checked 22 October 2026.
  8. The Measure and Mismeasure of Fairness jmlr.org. Checked 22 October 2026.