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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.
 

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

MIA's efforts are driven by the Steering Committee: Mehrtash BabadiBrian ClearySam FriedmanAllen Goodman, Gopal Sarma (co-chair, Jul 2019 - Jan 2021), Anika GuptaDylan KotliarRay JonesJames McFarland, Mollie Morg, Mor NitzanMax Shen, Emma Stickgold, and Elizabeth Wood.

Jon Bloom and Alex Bloemendal founded MIA in Fall 2015. Jon Bloom served as a co-chair through July 2019. Past Steering Committe members: Debora MarksDavid 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.

Spring 2021 Schedule:  10am Primer, 11am Seminar unless noted otherwise, all 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
Jan 27
  • Depts. Organismic and Evolutionary Biology, Physics, Harvard University
Primer: Genomic investigations of evolutionary dynamics and epistasis in microbial evolution experiments
Jan 27
  • NSF-Simons Center for Mathematical and Statistical Analysis of Biology; Harvard University
Predictable patterns in phenotypic evolution
Feb 3
No primer
Feb 3
  • Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center
The 3D genome and predictive models of gene regulation
Feb 10
No primer
Feb 10
Bayesian methods for adaptive experimental design
Feb 17
NO MEETING THIS WEEK
Feb 24
Primer: Locality sensitive hashing: A sort of history and introduction
Feb 24
Viral diagnostic design with model-based optimization
Mar 3
Primer: Integrating heterogeneous measurements in single cells
Mar 3
  • Division of Biology and Biological Engineering, California Institute of Technology
Single-cell biology in a software 2.0 world
Mar 10
  • Center for Communicable Disease Dynamics, Harvard Chan School of Public Health
Primer: Using viral loads and within-host models to improve COVID-19 surveillance
Mar 10
  • Dept. of Statistics, Wharton School, University of Pennsylvania
Simple, flexible and effective pooled testing via hypergraph factorization
Mar 17
NO MEETING THIS WEEK
Mar 24
Primer: Generative models of antibodies for functionally optimized library design
Mar 24
Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning
Mar 31
Primer: Density-aware visualization and sketching of single-cell transcriptomic data
Mar 31
Low dimensional embeddings of words and documents (and how they might apply to single-cell data)
Apr 7
No primer
Apr 7
  • Loh Lab, Harvard Medical School; Division of Genetics, Brigham and Women's Hospital; Broad Institute
Imputed repeat polymorphisms point to protein-coding variants driving genetic associations
Apr 14
NO MEETING THIS WEEK
Apr 21
NO MEETING THIS WEEK
Apr 28
NO MEETING THIS WEEK
Apr 29
No primer
Apr 29
  • Paul G. Allen School of Computer Science and Engineering, University of Washington
Deep learning of immune differentiation (Note: 12pm start)
May 5
Primer: Estimation and testing with generative nonparametric Bayesian models
May 5
Building and evaluating generative models of biological sequences, from proteins to whole genomes
May 12
  • Bioinformatics and Integrative Genomics Program, Zitnik Lab, Harvard Medical School
Primer: Deep learning for biomedical networks: Methods, challenges, and frontiers
May 12
  • Dept. of Biomedical Informatics, Harvard University; Broad Institute
Actionable machine learning for drug discovery and development
May 19
NO MEETING THIS WEEK
May 26
NO MEETING THIS WEEK
Jun 2
Primer: Optimal thinning of mcmc output with application to cardiac electrophysiology
Jun 2
  • Depts. of Statistics and Computer Science, Stanford University; Microsoft Research New England
Probabilistic inference and learning with Stein’s method