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, emphasising 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: Orr Ashenberg (since Sep 2023), and Pinar Demetci (since Jan 2024).

MIA's efforts are driven by the Steering Committee: Salil Bhate (co-chair, Jan 2023 - May 2024) Alex Bloemendal (co-chair, Fall 2015 - Jan 2022), Lateisha Copeland-GuadarramaElizabeth Wood, Ray Jones, Sam Friedman, Wengong Jin, Salil Bhate, Orr Ashenberg, Eli Bingham, David FischerPinar Demetci, Tavor Baharav, and Marie-Madlen Pust.

Jon Bloom and Alex Bloemendal founded MIA in the Fall of 2015.

Past Steering Committee members: Aleksandrina Goeva (co-chair, Jan 2021 - Dec 2022), Gopal Sarma (co-chair, Jul 2019 - Jan 2021),  Jon Bloom (co-chair, Sep 2015 - July 2019), Allen Goodman, Anika Gupta, Arnav Mehta, Ashley Conard, Aviad Tsherniak, Brian Cleary, David Benjamin, Debora Marks, Dylan Kotliar, Emma Stickgold, Hilary Finucane, James McFarland, Martin Jankowiak, Marzieh Haghighi, Matthew Amodio, Max Shen, Mollie Morg, Mor Nitzan, Sam Riesenfeld, and Yakir Reshef.


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 2024 Schedule: 9 am Primer, 10 am Meeting, 10:50 am Discussion, in-person in 75A-M1-Acadia unless noted otherwise. Please refer to our MIAcast (weekly email announcement) for Zoom meeting information. Email lcopelan@broadinstitute.org with questions and mia-team@broadinstitute.org to be added to our mailing list.

MIA Talks Search

Date Speaker Title
Feb 14
  • Pascal Notin

Hybrid protein language models for fitness prediction and design
[Video]
Feb 14
Unsupervised viral antibody escape prediction for future-proof vaccines
[Video]
Mar 6
  • Sandeep Kambhampati

  • Philipp Schneider

  • Kai Cao

  • Bojan Karlas

Postdoc flash talks
Mar 13
  • Žiga Avsec

    Google DeepMind

Accurate proteome-wide missense variant effect prediction with Alpha Missense
[Video]
Mar 13
  • Jun Cheng

    Google DeepMind

Alpha Missense
[Video]
Mar 20
Testing data-driven hypotheses post-clustering
[Video]
Mar 20
  • Assistant Professor of Statistics

    University of British Columbia

Data thinning to avoid double dipping
[Video]
Apr 3
  • Postdoctoral fellow Eric and Wendy Schmidt Center Broad Institute

Statistical and algorithmic challenges in reference-free analysis
[Video]
Apr 3
SPLASH unifies genomic analysis and discovery through a paradigm shift to statistics-first
[Video]
Apr 10
  • Aparna Nathan

    Lecturer on Biomedical Informatics, Harvard Medical School

Single-cell models for state-dependent eQTL analysis
[Video]
Apr 10
    • Raychaudhuri Lab, Division of Genetics/Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital.
    • Department of Medicine, Harvard Medical School.
Scalable single-cell models for robust cell-state-dependent eQTL mapping
[Video]
May 1
  • Sergey Ovchinnikov

    MIT

Protein language models learn evolutionary statistics of interacting sequence motifs
May 1
  • Simon Kozlov

    MIT

Combining protein language and structure models to redesign E. coli proteome with a reduced amino acid alphabet
May 8
  • Matthew McPartlon

    Chai Discovery, AI Research, VantAI

Protein Design with Deep Learning: Progress, Challenges, and Next Steps
May 8
  • Joshua Meier

    Chai Discovery

Unlocking Generative AI for Drug Discovery with Zero-shot Models
May 29
  • Marinka Zitnik

    Assistant Professor of Biomedical Informatics, Harvard Medical School

Geometric deep learning and generative models for protein target discovery
[Video]
May 29
  • Owen Queen

    Research Associate, Harvard Medical School

  • Yepeng Huang

    PhD Student, Harvard Medical School

  • Marinka Zitnik

    Assistant Professor of Biomedical Informatics, Harvard Medical School

Multimodal protein language models for deciphering protein function
[Video]