Programs (2019-2020)

1) Games, Decisions, Risk and Reliability (GDRR)

  • Year 2019-2020: Game theory and adversarial risk analysis topics, relate these to decision theory, and also apply decision theory to risk analysis. An exciting aspect of the program will be to address non-standard utility functions that take account of the cost of memory, computation, and human effort to set up the analysis—these considerations are directly relevant to issues that arise in machine learning and data science.

2) Deep Learning

  • Fall 2019: The program will focus on statistical strategies for improving machine learning. There is vast interest in automated methods for complex data analysis. However, there is a lack of consideration of: (1) interpretability; (2) uncertainty quantification; (3) applications with limited training data; and (4) selection bias. Statistical methods can achieve (1)-(4) through a change in focus.

3) Causal Inference

  • Spring 2020: Medical and health applications will be a significant theme, but other applications will be considered. Much of the new work in causal inference entails modern machine learning tools, and this perspective will be important to the program.

Upcoming Programs (2020-2021)

1) Program on Numerical Analysis in Data Science

  • Fall of 2020: Novel and efficient numerical techniques are undeniably needed to process and interpret massive data sets generated by modern technological and scientific developments; e.g., surveillance, space observation, medical data. Three overlapping themes in emerging numerical methods for this program are (i) analysis of deep learning (DL) techniques; (ii) finding underlying dynamics of time-dependent data sets; (iii) Randomized Numerical Linear Algebra (RandNLA) algorithms.

2) Program on Combinatorial Probability

  • Spring 2021: Placing a probability distribution over rankings and decomposition of rankings is also a probability model with combinatorial parameters, and can be used to extend classically deterministic optimization-based methods to stochastic models. Partition parameters arise in modeling gerrymandering in voting districts, where one is interested in distributions of demographic, political affiliation, and social affiliation variables conditional on the partition. Topological data analysis will be part of this program.

3) Program on Data Science in the Social and Behavioral Sciences

  • Spring 2021: Major themes will include social network analysis and network neuroscience; comparisons and synthesis in causal inference and statistical modeling across structural equation models, directed acyclic graphs, and counterfactual approaches; consideration of new forms of digital data and the methods to analyze such data.

Participation in Workshops

SAMSI organizes numerous workshops, scientific and educational. Application forms can be found on the pages of the individual workshops.