University Lecturer

Dan Fagin is a professor of journalism and the director of the Science, Health, and Environmental Reporting Program at New York University’s Arthur L. Carter Journalism Institute. For fifteen years, he was the environmental writer at Newsday, where he was twice a principal member of reporting teams that were finalists for the Pulitzer Prize.

Date: April 24, 2025
Lecture 1: 11:05 a.m., G10 Biotech, “The future of the monarch butterfly and biodiversity in the Anthropocene”
Lecture 2: 4:30 p.m., Atkinson Hall 121, “Science communication in the age of denialism”

University Lecturer

Dr. Michael Gordin, Dean of the College and Rosengarten Professor of Modern and Contemporary History in the History Department at Princeton University, will provide a lecture as part of a celebration of the life and work of Dr. Henry Guerlac.

Lecture Title: “Was 1869 Mendeleev’s ‘Crucial Year’?: Or, Henry Guerlac in St. Petersburg”
Date: April 30, 2025
Time: 5-6:30 p.m.
Place: Guerlac Room, A.D. White House

Messenger Lecturer

Cynthia Dwork is the Gordon McKay Professor of Computer Science at the Harvard University John A. Paulson School of Engineering and Applied Sciences and Affiliated Faculty at Harvard Law School.

She uses theoretical computer science to place societal problems on a firm mathematical foundation.

Her awards and honors include the National Medal of Science, the IEEE Hamming Medal, the RSA Award for Excellence in Mathematics, the Dijkstra, Gödel, and Knuth Prizes, and the ACM Paris Kanellakis Theory and Practice Award. She is a member of the US National Academy of Sciences and the US National Academy of Engineering, and is a Fellow of the American Academy of Arts and Sciences and the American Philosophical Society.

Place: All lectures will take place in G01 Bill and Melinda Gates Hall

Lecture title: “Differential Privacy and the US Census”
Date: May 5, 2025
Time: 3:45-4:45pm
Abstract: Anonymized data aren’t:  either they are not really anonymized or the anonymization process destroys their utility.  Aggregate statistics, too, can fail to protect privacy, sometimes spectacularly.  Predictive models trained on large datasets memorize substantial portions of the training data and have been tricked into revealing this information.  The US Census Bureau demonstrated a privacy attack against the statistics the Bureau itself published in the 2010 census.  Although there is provably(!) no magic bullet, Differential Privacy – a definition of privacy tailored to statistical data analysis and a collection of supporting algorithmic techniques — has proven fruitful in a wide range of settings, from generating QuickType suggestions on phones and computers to publication of US Census redistricting data.

Why is privacy so slippery?  Why was this a new problem?  What is Differential Privacy and how can be achieved?  What happened when Alabama sued to prevent its deployment in the 2020 redistricting data?

Lecture title: “From Algorithmic Fairness to Outcome Indistinguishability”
Date: May 6, 2025
Time: 11:45am-12:45pm
Abstract: The past fifteen years have witnessed the emergence of algorithmic fairness as a new field in theoretical computer science, statistics, and AI.  In the course of developing the definitional and structural foundations for the area, a philosophical question emerged.  Predictive models, also known as scoring functions, assign to each individual, or individual instance, a number between 0 and 1 that is often interpreted as a probability:  “How likely is this tumor to metastasize?” “What is the probability this individual will commit a violent crime in the next two years?”  But what is the probability of a non-repeatable event?  Without an understanding of what an algorithm is supposed to be producing, how can we evaluate the algorithms we build?  Outcome Indistinguishability, a notion with roots in fairness and complexity theory, provides an avenue of attack.

This talk describes key milestones in the theory of algorithmic fairness and introduces Outcome Indistinguishability.

Lecture title: “Springbards”
Date: May 7, 2025
Time: 3:45pm
Abstract: Differential Privacy and Outcome Indistinguishability have yielded results beyond the areas in which they were conceived.  In this talk, we use Differential Privacy to obtain a Reusable Holdout Set, thereby enabling sound exploratory data analysis.  We then use Outcome Indistinguishability to obtain a strong complexity-theoretic regularity theorem analogous to Szemerdi’s Graph Regularity Lemma, and explore some corollaries and open questions.

University Lecturer

Lawrence Wein is The Jeffrey S. Skoll Professor of Management Science and Senior Associate Dean of Academic Affairs. He also is a Senior Fellow at Stanford’s Center for International Security and Cooperation, Stanford, Graduate School of Business. His main research interests are in operations management and public health with his primary current interest on solving violent crimes, with a focus on ballistic imaging, sexual assault kits and forensic investigative genetic genealogy.

Lecture Title: “TBD”
Date: Fall 2025
Time: TBD
Place: TBD

Messenger Lecturer

Professor Ludovic Orlando, HDR, PhD, Agrégé, Normalien, Director, Centre for Anthropobiology & Genomics of Toulouse, CAGT, Université Paul Sabatier Toulouse III, France, is one of the world’s leading scientists who study ancient DNA. His published work over the past decade has provided paradigm-changing insights into the evolution and domestication of the horse and the contributions of the horse to human civilization.

Lecture Titles: “The Ancient DNA revolution in human evolution,” “The horse before and after our shared history, ” and “Environmental DNA: time capsules of past and present life”
Date: Spring 2026, week of April 20, 2026
Time: TBD
Place: TBD