Talks

Vahideh Manshadi is an Associate Professor of Operations at Yale School of Management. She is also affiliated with the Yale Institute for Network Science, the Department of Statistics and Data Science, and the Cowles Foundation for Research in Economics. Her current research focuses on the operation of online and matching platforms in both the private and public sectors. Professor Manshadi serves on the editorial boards of Management Science, Operations Research, and Manufacturing & Service Operations Management. She received her Ph.D. in electrical engineering at Stanford University, where she also received MS degrees in statistics and electrical engineering. Before joining Yale, she was a postdoctoral scholar at the MIT Operations Research Center. Alex Teytelboym is an Associate Professor at the Department of Economics, University of Oxford, a Tutorial Fellow at St. Catherine’s College, and a Senior Research Fellow at the Institute for New Economic Thinking at the Oxford Martin School. His research interests lie in market design and the economics of networks, as well as their applications to environmental economics and energy markets. His policy work has been on designing matching systems for refugee resettlement and environmental auctions. He is co-founder of Refugees.AI, an organization that is developing new technology for refugee resettlement. Kimon Drakopoulos is the Robert R. Dockson Assistant Professor in Business Administration at the Data Sciences and Operations department at USC Marshall School of Business. His research focuses on the operations of complex networked systems, social networks, stochastic modeling, game theory and information economics. In 2020 he served as the Chief Data Scientist of the Greek National COVID-19 Scientific taskforce and a Data Science and Operations Advisor to the Greek Prime Minister. He has been awarded the Wagner Prize for Excellence in Applied Analytics and the Pierskalla Award for contributions to Healthcare Analytics. Moon Duchin is a Professor of Mathematics at Tufts University, and runs the MGGG Redistricting Lab, an interdisciplinary research group at Tisch College of Civic Life of Tufts University. The lab's research program centers on Data For Democracy, bridging math, CS, geography, law, and policy to build models of elections and redistricting.  She has worked to support commissions, legislatures, and other line-drawing bodies and has served as an expert witness in redistricting cases around the country. Philip Leclerc is an operations research analyst working in the Center for Enterprise Dissemination-Disclosure Avoidance (CEDDA) at the U.S. Census Bureau. He graduated with a B.A. in mathematical economics and psychology from Christopher Newport University, and later completed his Ph.D. in Systems Modeling and Analysis at Virginia Commonwealth University. He joined the U.S. Census Bureau 6 years ago, where he first learned about differential privacy, and for the last 5 years has served as the internal scientific lead on the project for modernizing the disclosure avoidance system used in the first two major releases from the Decennial Census. Samir Shah is Vice President, Partnerships & Customer Success at VolunteerMatch, where, for over a decade, he has contributed to a vision of developing the global digital volunteering backbone. Samir uses technology, networks, and data to empower volunteers, nonprofits, governments, companies, and brands to create value from VolunteerMatch’s products and services. He has negotiated complex partnerships with Fidelity, California Volunteers, Office of the Governor, and STEM Next, and manages trusted relationships with VolunteerMatch’s Open API Network of third party platform partners. Samir has a BA in Economics from the UT, Austin, a MA in Asian Studies from the UC, Berkeley, and an MBA from the Haas School of Business. 

Deep learning algorithms that achieve state-of-the-art results on image and text recognition tasks tend to fit the entire training dataset (nearly) perfectly including mislabeled examples and outliers. This propensity to memorize seemingly useless data and the resulting large generalization gap have puzzled many practitioners and is not explained by existing theories of machine learning. We provide a simple conceptual explanation and a theoretical model demonstrating that memorization of outliers and mislabeled examples is necessary for achieving close-to-optimal generalization error when learning from long-tailed data distributions. Image and text data are known to follow such distributions and therefore our results establish a formal link between these empirical phenomena. We then demonstrate the utility of memorization and support our explanation empirically. These results rely on a new technique for efficiently estimating memorization and influence of training data points.  Our results allow us to quantify the cost of limiting memorization in learning and explain the disparate effects that privacy and model compression have on different subgroups.

We study the concurrent composition properties of interactive differentially private mechanisms, whereby an adversary can arbitrarily interleave its queries to the different mechanisms. We prove that all composition theorems for non-interactive differentially private mechanisms extend to the concurrent composition of interactive differentially private mechanisms for all standard variants of differential privacy including $(\eps,\delta)$-DP with $\delta>0$, R\`enyi DP, and $f$-DP, thus answering the open question by \cite{vadhan2021concurrent}. For $f$-DP, which captures $(\eps,\delta)$-DP as a special case, we prove the concurrent composition theorems by showing that every interactive $f$-DP mechanism can be simulated by interactive post-processing of a non-interactive $f$-DP mechanism. For R\`enyi DP, we use a different approach by showing the optimal adversary against the concurrent composition can be decomposed as a product of the optimal adversaries against each interactive mechanism.

In this talk I will review some of the psychological and economic factors influencing consumers’ desire and ability to manage their privacy effectively. Contrary to depictions of online sharing behaviors as careless, consumers fundamentally care about online privacy, but technological developments and economic forces have made it prohibitively difficult to attain desired, or even desirable, levels of privacy through individual action alone. The result does not have to be what some have called "digital resignation" though: a combination of individual and institutional efforts can change what seems to be the inevitability of the death of privacy into effective privacy protection.

The Privacy Sandbox aims "to create technologies that both protect people's privacy online and give companies and developers tools to build thriving digital businesses." This talk will describe some of the design, implementation and practical challenges in evolving measurement solutions away from persistent cross-site identifiers.

I'll explain work in progress, joint with Miroslav Rapcak, on geometric constructions of vertex algebras associated to divisors in toric Calabi-Yau threefolds, in terms of moduli stacks of objects in certain exotic abelian subcategories of complexes of coherent sheaves on the underlying threefold. These vertex algebras were originally proposed by Gaiotto-Rapcak, and constructed mathematically in the example of affine space by Rapcak-Soibelman-Yang-Zhao, building on Schiffmann-Vasserot's proof of the AGT conjecture. We give a geometric explanation and generalization of the quivers with potential that feature in the latter results, and outline the analogous construction of vertex algebras in this setting.

Zoom link:  https://pitp.zoom.us/j/93749710253?pwd=Y3NUTHBXZ3FPQUdPU0E0d0ttVzFFdz09

I discuss novel symmetries of perturbation theory around rotating and non-rotating black holes in general relativity, and discuss their origins and implications for gravitational-wave astronomy. This is motivated by two special aspects of black hole perturbations in four dimensions: isospectrality of quasinormal modes and the vanishing of tidal Love numbers. There turn out to be off-shell symmetries underlying each of these phenomena. One is a duality, which on shell reproduces the famous Chandrasekhar duality and therefore underlies isospectrality, and can be thought of as an extension of electric-magnetic duality to black hole backgrounds. The other is a set of "ladder symmetries" relating modes of different angular momentum or spin, which imply the vanishing of Love numbers. This has a geometric origin in the conformal symmetry of low-frequency modes.

Zoom link:  https://pitp.zoom.us/j/93633894223?pwd=cEFRYno5WSt5NUJlOWJLdVZHWjE1QT09