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:Salil Bhate (since Jan 2023) & Orr Ashenberg (since Sep 2023).

MIA's efforts are driven by the Steering Committee: Aleksandrina Goeva (Jan 2021 - Dec 2022) Alex Bloemendal (co-chair, Fall 2015 - Jan 2022), Lateisha Copeland-Guadarrama, Elizabeth Wood, Emma Stickgold, Marzieh HaghighiMehrtash BabadiMor NitzanRay Jones, and Sam Friedman, Wengong Jin, Salil Bhate, Orr Ashenberg, Eli Bingham, Matthew Amodio, David Fischer, Ashley Conard.

Jon Bloom and Alex Bloemendal founded MIA in the Fall of 2015. Jon Bloom served as a co-chair through July 2019. Past Steering Committee members: Allen GoodmanDebora MarksDavid BenjaminDylan KotliarGopal Sarma (co-chair, Jul 2019 - Jan 2021), James McFarland, Jon Bloom, Hilary FinucaneSam RiesenfeldYakir Reshef, Aviad Tsherniak, Max Shen, Brian Cleary, Mollie Morg, and Anika GuptaMartin Jankowiak, Arnav Mehta.

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.

Spring 2023 Schedule: 9 am Primer, 10 am Meeting, 10:50 am Discussion unless noted otherwise, in-person in 75A-M1-Acadia. Please refer to our MIAcast (weekly email announcement) for Zoom meeting information. Email with questions and to be added to our mailing list.

MIA Talks Search

Date Speaker Title
Sep 20
  • Xinyi Zhang

    MIT EECS; Eric and Wendy Schmidt Center, Broad Institute

Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease
Sep 20
Seurat v5, cross-modality mapping and large-scale clustering of single-cell data
Oct 18
  • McGill University, Electrical and Computer Engineering, Montréal, Canada

Theoretical background regarding GANs and Causal GAN
Oct 18
  • Amin Emad, Prof

    McGill University and Mila (Quebec AI Institute)

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks
Oct 25
  • Microsoft Research New England

Towards Meaningful Pretrained Models for Biology
Oct 25
  • School of Engineering and Applied Sciences, Harvard University

Oct 25
  • Stanley Hua

    University of Toronto

Meaningful choice/curation of pre-training data in alignment with a downstream task
Nov 1
  • Ava Amini

Nov 15
  • Katherine Xue

    David Relman lab and Dmitri Petrov lab, Stanford University

Nov 29
  • Jiaqi Zhang

Nov 29
  • Regev Lab, Genentech & Pritchard Lab, Stanford

Dec 6
  • Department of Developmental Biology and Genetics, Washington University in St. Louis