Kshitij Kulkarni

Chih-Yuan Chiu, Kshitij Kulkarni, Shankar Sastry

Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide non-asymptotic sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS). Code containing the datasets and experiments is publicly available.

Dezhi Fang, Matthew Keezer, Jacob Williams et al.

We are working on a scalable, interactive visualization system, called Carina, for people to explore million-node graphs. By using latest web browser technologies, Carina offers fast graph rendering via WebGL, and works across desktop (via Electron) and mobile platforms. Different from most existing graph visualization tools, Carina does not store the full graph in RAM, enabling it to work with graphs with up to 69M edges. We are working to improve and open-source Carina, to offer researchers and practitioners a new, scalable way to explore and visualize large graph datasets.

Kshitij Kulkarni, Sven Neth

We study group decision making with changing preferences as a Markov Decision Process. We are motivated by the increasing prevalence of automated decision-making systems when making choices for groups of people over time. Our main contribution is to show how classic representation theorems from social choice theory can be adapted to characterize optimal policies in this dynamic setting. We provide an axiomatic characterization of MDP reward functions that agree with the Utilitarianism social welfare functionals of social choice theory. We also provide discussion of cases when the implementation of social choice-theoretic axioms may fail to lead to long-run optimal outcomes.