BayesDB For Data-Centric Scientific Discovery

Abstract

This work was performed under an AFRL contract for developing data-driven methods to accelerate scientific discovery and robust design. Our MIT research team transitioned 4 results we believe constitute SD2 program-level accomplishments: 1. Automated data integrity via online learning of probabilistic programs, via Fail-Fast. 2. Fast exact symbolic inference for probabilistic programs, using SPPL. 3. Gen: A scalable, general-purpose probabilistic programming platform. 4. Learning whole-genome generative models from high-dimensional wet lab data to detect unwanted host-gene interactions and predict knockout effects on sample growth rate.

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Document Details

Document Type
Technical Report
Publication Date
Feb 10, 2022
Accession Number
AD1162978

Entities

People

  • Alex Lew
  • Alexey Radul
  • Amanda Brower
  • Cameron Freer
  • Desiree Dudley
  • Feras Saad
  • Harish Tella
  • Jonathan Rees
  • Joshua Tenebaum
  • Joshua Thayer
  • Marco Cusumano-towner
  • Rachael Paiste
  • Shivam Handa
  • Ulrich Schaecthle
  • Veronica Weiner
  • Vikash Mansinghka
  • Zane Shelby

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Biology
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Data Analysis
  • Data Mining
  • Data Science
  • Domain Specific Programming Languages
  • Information Science
  • Machine Learning
  • Network Science
  • Probability
  • Programming Languages
  • Statistics
  • Synthetic Biology
  • Systems Biology

Readers

  • Aerospace Engineering.
  • Neural Network Machine Learning.
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms