SOCR News JMM DC Session 2021

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SOCR News & Events: 2021 JMM/AMS Special Session on Foundations of Data Science: Mathematical Representation, Computational Modeling, and Statistical Inference

Overview

The volume, heterogeneity, and velocity of digital information is increasing exponentially and faster than our ability to manage, interpret and analyze it. Novel mathematical algorithms, reliable statistical techniques, and powerful computational tools are necessary to cope with the enormous proliferation of data in all aspects of human experiences. There are a number of mathematical strategies to represent, model, analyze, interpret and visualize complex, voluminous, and high-dimensional data. The talks in this session will present advanced and alternative mathematical strategies to handle difficult data science challenges using differential equations, topological embeddings, tensor-based, analytical, numerical optimization, algebraic, multiresolution, variational, probabilistic, statistical, and artificial intelligence methods. Biomedical, environmental, and imaging examples will demonstrate the applications of such mathematical techniques to longitudinal, complex-valued, complex-time indexed, and incongruent observations.


Organizer

Session Logistics

Speakers

  • To be announced in September 2020
  • Caroline Uhler (MIT): Multi-Domain Data Integration: From Observations to Mechanistic Insights
Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (manufacturing, advertisement, education, genomics, etc.). In order to obtain mechanistic insights from such data, a major challenge is the integration of different data modalities (video, audio, interventional, observational, etc.). Using genomics and in particular the problem of identifying drugs for the repurposing against COVID-19 as an example, I will first discuss our recent work on coupling autoencoders in the latent space to integrate and translate between data of very different modalities such as sequencing and imaging. I will then present a framework for integrating observational and interventional data for causal structure discovery and characterize the causal relationships that are identifiable from such data. We end by a theoretical analysis of autoencoders linking overparameterization to memorization. In particular, I will characterize the implicit bias of overparameterized autoencoders and show that such networks trained using standard optimization methods implement associative memory. Collectively, our results have major implications for planning and learning from interventions in various application domains.

Resources

  • Slides/papers: TBD





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