Ya’acov Ritov

By |

My main interest is theoretical statistics as implied to complex model from semiparametric to ultra high dimensional regression analysis. In particular the negative aspects of Bayesian and causal analysis as implemented in modern statistics.

An analysis of the position of SCOTUS judges.

Barbara Koremenos

By |

Every year, states negotiate, conclude, sign, and give effect to hundreds of new international agreements. Koremenos argues that the detailed design provisions of such agreements matter for phenomena that scholars, policymakers, and the public care about: When and how international cooperation occurs and is maintained.
Theoretically, Koremenos develops hypotheses regarding how cooperation problems like incentives to cheat can be confronted and moderated through law’s detailed design provisions. Empirically, she exploits her data set composed of a random sample of international agreements in economics, environment, human rights and security.
Her theory and testing lead to a consequential discovery: Considering the vagaries of international politics, international cooperation looks more law-like than anarchical, with the detailed provisions of international law chosen in ways that increase the prospects and robustness of cooperation.

Christopher Fariss

By |

My core research focuses on the politics and measurement of human rights, discrimination, violence, and repression. I use computational methods to understand why governments around the world torture, maim, and kill individuals within their jurisdiction and the processes monitors use to observe and document these abuses. Other projects cover a broad array of themes but share a focus on computationally intensive methods and research design. These methodological tools, essential for analyzing data at massive scale, open up new insights into the micro-foundations of state repression and the politics of measurement.

People rely more on strong ties for job help in countries with greater inequality. Coefficients from 55 regressions of job transmission on tie strength are compared to measures of inequality (Gini coefficient), mean income per capita, and population, all measured in 2013. Gray lines indicate 95% confidence regions from 1000 simulated regressions that incorporate uncertainty in the country-level regressions (see below for more details). In each simulated regression we draw each country point from the distribution of regression coefficients implied by the estimate and standard error for that country and measure of tie strength. P values indicate the simulated probability that there is no relationship between tie strength and the other variable. Laura K. Gee, Jason J. Jones, Christopher J. Fariss, Moira Burke, and James H. Fowler. “The Paradox of Weak Ties in 55 Countries” Journal of Economic Behavior & Organization 133:362-372 (January 2017) DOI:10.1016/j.jebo.2016.12.004

Sol Bermann

By |

I am interested in the intersection of big data, data science, privacy, security, public policy, and law. At U-M, this includes co-convening the Dissonance Event Series, a multi-disciplinary collaboration of faculty and graduate students that explore the confluence of technology, policy, privacy, security, and law. I frequently guest lecture on these subject across campus, including at the School of Information, Ford School of Public Policy, and the Law School.

James R. Hines Jr.

By |

Professor Hines’ research focuses on the analysis of the donative behavior of Americans, and how it affects the intergenerational and interpersonal transmission of economic well-being. To what extent do parents leave property to their children and others, and how is this behavior affected by legal institutions, taxes, social norms, and other considerations? While there are no comprehensive sources of data on wills, trusts, lifetime gifts, and other forms of property transmission, there is ample available information from legal documents that with the help of natural language processing can hopefully be coded and analyzed in a systematic way.

Nicholson Price

By |

I study how law shapes innovation in the life sciences, with a substantial focus on big data and artificial intelligence in medicine. I write about the intellectual property incentives and protections for data and AI algorithms, the privacy issues with wide-scale health- and health-related data collection, the medical malpractice implications of AI in medicine, and how FDA should regulate the use of medical AI.

Kevin Quinn

By |

Kevin Quinn, PhD, is Professor of Political Science in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prior to joining the Michigan faculty, Professor Quinn was a Professor of Law at UC Berkeley. His research focuses on questions of empirical legal studies and statistical methodology. His research has been supported by the National Science Foundation and has appeared in leading journals in political science, statistics, and law. Professor Quinn is a former President of the Society for Political Methodology and his research has received multiple professional awards.