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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.
 

MIA's efforts are driven by the Steering Committee: Alex Bloemendal (co-chair), Aleksandrina Goeva (co-chair), Gopal Sarma (co-chair), Mehrtash BabadiBrian ClearySam FriedmanAllen Goodman, 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. 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 Affiliations 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
TBD
Feb 10
  • Dept. Computer Science, Harvard University
Primer: TBD
Feb 10
  • Sabeti Lab, Broad Institute
Improved viral diagnostics using machine learning-based design
Feb 17
NO MEETING THIS WEEK
Feb 24
  • TBD
Primer: TBD
Feb 24
  • Goate Lab, Icahn School of Medicine at Mount Sinai
TBD
Mar 3
  • TBD
Primer: TBD
Mar 3
  • Division of Biology and Biological Engineering, California Institute of Technology
TBD
Mar 10
No primer
Mar 10
  • Dept. of Computer Science, University of California, Berkeley
TBD
Mar 17
NO MEETING THIS WEEK
Mar 24
NO MEETING THIS WEEK
Mar 31
  • TBD
Primer: TBD
Mar 31
  • Tutte Institute for Mathematics and Computing
TBD
Apr 7
  • TBD
Primer: TBD
Apr 7
  • Dept. of Immunology, CBDM Lab, Harvard Medical School; Broad Institute
TBD
Apr 14
  • TBD
Primer: TBD
Apr 14
  • TBD
TBD
Apr 21
NO MEETING THIS WEEK
Apr 28
  • TBD
Primer: TBD
Apr 28
  • Paul Allen School of Computer Science and Engineering, University of Washington
TBD
May 5
  • TBD
Primer: TBD
May 5
  • Marks Lab, Harvard Medical School; Harvard University
TBD
May 12
  • Bioinformatics and Integrative Genomics Program, Harvard Medical School
Primer: TBD
May 12
  • Dept. of Biomedical Informatics, Harvard University; Broad Institute
TBD
May 19
  • Van Allen Lab, Dana-Farber Cancer Institute; Harvard Medical School
Primer: TBD
May 19
  • Broad Institute; Harvard Medical School; Dana-Farber Cancer Institute
TBD
May 26
  • TBD
Primer: TBD
May 26
  • Depts. of Statistics and Computer Science, Stanford University; Microsoft Research New England
TBD