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Models, Inference & Algorithms (MIA)

The Models, Inference & Algorithms (MIA) Initiative at the Broad Institute supports learning and collaboration across the interface of biology and medicine with mathematics, statistics, machine learning, and computer science. Our weekly meeting features a primer, breakfast, seminar, and discussion; these are open and pedagogical, emphasizing lucid exposition of computational ideas over rapid-fire communication of results. Other MIA functions include hosting workshops, developing educational resources, advising leadership, and supporting the computational community.
 

Write mia-team@broadinstitute.org to be added to our mailing list.
 

MIA's efforts are driven by the Steering Committee: Alex Bloemendal (co-chair), Gopal Sarma (co-chair), Mehrtash BabadiBrian ClearySam Friedman, Aleksandrina GoevaAllen Goodman, Anika GuptaDylan KotliarRay Jones, Debora MarksJames McFarland, Mollie Morg, Mor NitzanMax Shen, Emma Stickgold, and Elizabeth Wood.

Jon Bloom and Alex Bloemendal founded MIA in Fall 2015. Past Steering Committe members: David BenjaminJon Bloom, Hilary FinucaneSam RiesenfeldYakir Reshef, and Aviad Tsherniak.


MIA Overview: Watch this introduction to the past, present, and future of MIA, and how you can help drive the fusion of machine learning and biomedicine. Check out the Kendall Square Codebreakers in the Harvard Crimson.


MIA Playlist: Watch and share our growing library of MIA videos.


Talking Machines: Listen to interviews with Eric LanderAviv Regev, and Nick Patterson.

Fall 2020 Schedule:  10am Primer, 11am Seminar unless noted otherwise, all via Zoom. Please refer to the weekly email announcement for Zoom meeting information. Email morg@broadinstitute.org with questions and mia-team@broadinstitute.org to be added to our mailing list.

Date Speaker Affiliations Title
Sep 9 [Video]
  • Broderick Group, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology; ArbiLex
Primer: Gaussian processes: An introduction
Sep 9 [Video]
  • Dept. of Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Laboratory, Statistics and Data Science Center, Massachusetts Institute of Technology
Fast discovery of pairwise interactions in high dimensions using Bayes
Sep 16 [Video]
  • University of Alberta
Primer: Machines read, humans read: parallels between computer and human representations of meaning (Note: 11am start)
Sep 16 [Video]
  • Depts. of Computing Science, Psychology, University of Alberta; Canadian Institute for Advanced Research
Decoding word meaning from brain images collected during language production (Note: 12pm start)
Sep 23
  • Clinical Machine Learning Group, Massachusetts Institute of Technology
Primer: Learning personalized treatment policies from observational data
Sep 23
  • Dept. of Electrical Engineering and Computer Science, Institute for Medical Engineering & Science, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
Going beyond diagnosis and prognosis: Machine learning to guide treatment suggestions
Sep 30 [Video]
  • Research Laboratory of Electronics Computational Cardiovascular Research Group, Massachusetts Institute of Technology
Primer: Modeling cardiovascular physiology
Sep 30 [Video]
  • Dept. of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Computer Science and Artificial Intelligence Laboratory, MIT; Massachusetts General Hospital
Physiology-inspired machine learning models for predicting adverse cardiovascular outcomes
Oct 7 [Video]
  • Dyno Therapeutics
Primer: Biological sequence design through machine-guided exploration
Oct 7 [Video]
  • Dyno Therapeutics
Machine-guided capsid engineering for gene therapy
Oct 14
No primer
Oct 14 [Video]
  • Dept. of Electrical Engineering and Computer Sciences, Center for Computational Biology, Berkeley AI Research Lab, UC Berkeley; Chan Zuckerberg Biohub
Machine learning-based design of proteins (and small molecules and beyond) (Note: 12pm start)
Oct 21
  • Pyro team, Broad Institute
Primer: Stochastic gradient-based variational inference
Oct 21
  • Pyro team, Broad Institute
Deep probabilistic programming with Pyro
Oct 28
  • Engelhardt Group, Princeton University
Primer: Generalized linear models and latent factor models
Oct 28
  • Dept. of Biostatistics, Harvard University
Inference in generalized bilinear models
Nov 4
NO MEETING THIS WEEK
Nov 11
NO MEETING THIS WEEK
Nov 18
Primer: TBD
Nov 18
  • Mahadevan Group, Harvard University
TBD
Nov 25
NO MEETING THIS WEEK
Dec 2
Primer: TBD
Dec 2
  • Dept. of Physics, Boston University
TBD
Dec 9
Primer: TBD
Dec 9
  • Goate Lab, Icahn School of Medicine at Mount Sinai
TBD