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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.

 

Current co-chairs: Alex Bloemendal (since fall 2015), Aleksandrina Goeva (since Jan 2021)

MIA's efforts are driven by the Steering Committee: Anika GuptaArnav MehtaBrian ClearyElizabeth Wood, Emma Stickgold, Juan CaicedoMartin JankowiakMarzieh HaghighiMax ShenMehrtash Babadi, Mollie Morg, Mor NitzanRay Jones, and Sam Friedman.

Jon Bloom and Alex Bloemendal founded MIA in Fall 2015. Jon Bloom served as a co-chair through July 2019. Past Steering Committe members: Allen GoodmanDebora MarksDavid BenjaminDylan KotliarGopal Sarma (co-chair, Jul 2019 - Jan 2021), James McFarlandJon 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 2021 Schedule: 9am Primer, 10am Meeting, 10:50am Discussion unless noted otherwise, via Zoom. Please refer to our MIAcast (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 Title
Sep 8
No primer
Sep 8
Multimodal single-cell data, open benchmarks, and a NeurIPS 2021 competition (Note: 9am start)
Sep 15
No meeting this week
Sep 22
Visual recognition from one or more images
Sep 22
Representation learning for single-cell, image-based phenotyping
Sep 29
  • Institute of Computational Biology, Helmholtz Zentrum München
  • Institute of Computational Biology, Helmholtz Zentrum München
Primer: Latent space learning in single cell genomics: Current approaches and challenges
Sep 29
Deep interpretable perturbation modeling in single cell genomics¹; Learning cell communication from spatial graphs of cells²
Oct 6
Primer: Advancements and challenges for deep learning in medical imaging
Oct 6
3KG: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations
Oct 13
  • Seung Lab, Princeton Neuroscience Institute, Princeton University
Scalable analysis of electron microscopy connectomics data: Revealing neural circuit properties using contrastive deep learning
Oct 13
No such thing as unlabeled: Self-supervised learning on medical data
Oct 20
Lightning talks (9am start, no primer)
Oct 27
  • Depts. of Biochemistry & Biophysics, Urology, University of California San Francisco
Primer: Capturing regulatory information encoded in RNA secondary structure
Oct 27
Computational tools for deciphering the RNA structural code
Nov 3
NO MEETING THIS WEEK
Nov 10
  • Van Allen Lab, Dana-Farber Cancer Institute; Broad Institute
Primer: Genomic tools for interpreting patterns of somatic driver and passenger mutations in cancer
Nov 10
Biologically informed deep neural network for prostate cancer discovery
Nov 17
NO MEETING THIS WEEK
Nov 24
NO MEETING THIS WEEK
Dec 1
Polygenic priority score for GWAS gene prioritization
Dec 1
  • Broderick Group, Massachusetts Institute of Technology
A new approach for high-dimensional hierarchical modeling
Dec 8
Primer: TBD (Note: 10am start)
Dec 8
  • Depts. of Organismic and Evolutionary Biology, Molecular and Cellular Biology, Harvard University
TBD (Note: 11am start)