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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: Aleksandrina Goeva (since Jan 2021) & Thouis Jones (since Jan 2022)

MIA's efforts are driven by the Steering Committee: Alex Bloemendal (co-chair, Fall 2015 - Jan 2022), Arnav MehtaElizabeth Wood, Emma Stickgold, Juan Caicedo, Lois Doolittle, Martin JankowiakMarzieh HaghighiMehrtash BabadiMor NitzanRay Jones, and Sam Friedman, Wengong Jin, and Salil Bhate.

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 ReshefAviad TsherniakMax Shen, Brian Cleary, Mollie Morg, and Anika Gupta .


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 2022 Schedule: 9am Primer, 10am Meeting, 10:50am Discussion unless noted otherwise, in-person in Monadnock. Please refer to our MIAcast (weekly email announcement) for Zoom meeting information. Email lrosinsk@broadinstitute.org with questions and mia-team@broadinstitute.org to be added to our mailing list.

Date Speaker Title
Sep 14
Primer: An Introduction to Bayesian Variable Selection
Sep 14
Applications of Bayesian Variable Selection to Bioinformatics
Sep 21
Primer: Gene expression prediction from DNA sequences
Sep 21
  • Assistant Professor, School of Biomedical Engineering UBC
Using deep learning regulatory models and random DNA for evolutionary inference
Sep 28
  • Postdoctoral Fellow in the Weissman Lab of MIT and Whitehead Institute
Towards predictive spatiotemporal modeling of single cells
Sep 28
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Oct 5
  • ML/NLP for healthcare, Postdoc at Brigham and Women's Hospital / Harvard Medical School
Primer: Promise and Challenges of Language Models in the Clinical Domain
Oct 5
Unlocking the Power of Electronic Health Record data using Deep Learning based Natural Language Processing
Oct 12
Primer: The envelope of sequence bioinformatics in 2022
Oct 12
  • Department of Molecular Genetics; Donnelly Centre for Cellular and Biomolecular Research; University of Toronto
The limits of Virus Discovery, and how to overcome them
Oct 19
  • Camargo Lab, Boston Children's Hospital
Primer: Lineage tracing for tissue development and cell differentiation
Oct 19
  • Damon Runyon Computational Biology Fellow, Harvard Medical School
Learning cell differentiation dynamics from lineage tracing datasets
Oct 26
No Primer
Oct 26
Deep learning based morphological profiling for rare disease genomic medicine
Nov 2
NO MEETING
Nov 16
  • Department of Cellular and Tissue Genomics - Oncology, Genentech
Primer: Analytical challenges and opportunities for studying cell state transitions at the single cell level
Nov 16
Neural Optimal Transport for Inferring Single-Cell Responses to Perturbations
Nov 23
NO MEETING WEEK
Dec 7
Primer: An Introduction to Causal Discovery and Inference
Dec 7
Deep End-to-end Causal Inference