Jessica Hullman (Northwestern University)
https://simons.berkeley.edu/events/law-society-fellow-talk
Law & Society Fellow Talk

Abstract: Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive visual data analysis as well as data communication. However, our understanding of how to design robust visualizations for data-driven inference remains limited by researchers' heavy reliance on small user studies and hunches about the role of visual representations in inference. Using examples from recent visualization research, this talk will motivate the need for better-defined objectives and theoretical approaches for measuring the value of a visualization for supporting exploratory data analysis or communication. This talk will discuss recent work in progress at the intersection of visualization and theory.

Bio: Jessica Hullman is the Ginni Rometty Associate Professor of Computer Science at Northwestern University. Her research addresses challenges that arise when people draw inductive inferences from data summaries. Hullman's work has contributed visualization techniques, applications, and evaluative frameworks for improving data-driven inference in applications like visual data analysis, data communication, privacy budget setting, and responsive design. Her current interests include how theorizing reasoning under uncertainty as mediated by representations of data could transform research and practice by providing insight into the value of a better interface. Hullman's work has received best paper awards at top visualization and HCI venues, and she has received a Microsoft Research Faculty Fellowship and NSF CAREER, Medium, and Small awards as PI, among others.

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Established in 2020, the Simons Institute's Law and Society Fellowships enhance Institute programs that address technologies with profound impacts on human society and with implications for ethics, law, and policy, by supporting a researcher within each who is focused on addressing the broader societal implications of the techniques and technologies addressed within these programs.

Law and Society Fellows participate in the Institute's programs and engage with visiting scientists. Additional contributions include an initial talk on the fellow’s work for visiting researchers at the Simons Institute; and a white paper on recommendations and findings.

Santosh Vempala (Georgia Tech)
Simons Institute 10th Anniversary Symposium

Rahul Singh (MIT)
https://simons.berkeley.edu/talks/kernel-methods-causal-inference
Algorithmic Aspects of Causal Inference

We propose a family of estimators based on kernel ridge regression for nonparametric dose response curves and semiparametric treatment effects. Treatment and covariates may be discrete or continuous, and low, high, or infinite dimensional. We reduce causal estimation and inference to combinations of kernel ridge regressions, which have closed form solutions and are easily computed by matrix operations, unlike other machine learning paradigms. This computational simplicity allows us to extend the framework to several settings: (i) heterogeneous effects and distribution shift; (ii) instruments and proxies; (iii) mediation and dynamic effects; (iv) sample selection and data fusion. For dose responses, we prove uniform consistency with finite sample rates. For treatment effects, we prove root-n consistency, Gaussian approximation, and semiparametric efficiency with a new double spectral robustness property.

Jennifer Listgarten (UC Berkeley)
https://simons.berkeley.edu/talks/machine-learning-based-design-proteins
Learning from Interventions

Rahul Jain (USC)
https://simons.berkeley.edu/talks/tbd-241
Reinforcement Learning from Batch Data and Simulation

Craig Gidney (Google)
https://simons.berkeley.edu/talks/tbd-138
Quantum Algorithms

Elette Boyle (IDC Herzliya), Henry Corrigan-Gibbs (Stanford University)
https://simons.berkeley.edu/talks/fully-linear-pcps
Probabilistically Checkable and Interactive Proof Systems

Emma Brunskill (Stanford University)
https://simons.berkeley.edu/talks/tba-92
Emerging Challenges in Deep Learning

S. Murray Sherman, University of Chicago
https://simons.berkeley.edu/talks/s-murray-sherman-4-18-18
Computational Theories of the Brain

Furong Huang, UC Irvine
Representation Learning
https://simons.berkeley.edu/talks/furong-huang-2017-03-29