Models, Inference & Algorithms (MIA)

Please fill out this brief form to receive our weekly emails, including all relevant details for upcoming talks. If you have any other questions, please email mia-team@broadinstitute.org.

Schedule changes will be noted on the website and in MIAcast. MIA is in-person with a virtual option available:

9:00 am Primer

10:00 am Meeting

10:50 am Discussion

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 '23), and Pinar Demetci (since Jan '24).

MIA's efforts are driven by the Steering Committee: Alex Bloemendal (co-chair, Fall '15 - Jan '22), David Fischer, Elizabeth Wood, Eli Bingham, Lateisha Copeland-Guadarrama, Lauren Alex Golden, Marie-Madlen Pust, Orr Ashenberg, Pinar Demetci, Salil Bhate (co-chair, Jan '23 - May '24), Sam Friedman, Sumaiya Iqbal, Tavor Baharav, and Wengong Jin.

Jon Bloom and Alex Bloemendal founded MIA in the Fall of 2015.
Past Steering Committee members: Aleksandrina Goeva (co-chair, Jan '21 - Dec '22), Ray Jones (co-chair, Jan '21 - Dec '22), Gopal Sarma (co-chair, Jul '19 - Jan '21), Jon Bloom (co-chair, Sep '15 - July '19), 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 Lander, Aviv 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
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]
May 29	Marinka Zitnik Assistant Professor of Biomedical Informatics, Harvard Medical School	Geometric deep learning and generative models for protein target discovery [Video]
May 8	Joshua Meier Chai Discovery	Unlocking Generative AI for Drug Discovery with Zero-shot Models
May 8	Matthew McPartlon Chai Discovery, AI Research, VantAI	Protein Design with Deep Learning: Progress, Challenges, and Next Steps
May 1	Simon Kozlov MIT	Combining protein language and structure models to redesign E. coli proteome with a reduced amino acid alphabet
May 1	Sergey Ovchinnikov MIT	Protein language models learn evolutionary statistics of interacting sequence motifs
Apr 10	Jose Alquicira-Hernandez 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]
Apr 10	Aparna Nathan Lecturer on Biomedical Informatics, Harvard Medical School	Single-cell models for state-dependent eQTL analysis [Video]
Apr 3	Julia Salzman Stanford University	SPLASH unifies genomic analysis and discovery through a paradigm shift to statistics-first [Video]
Apr 3	Tavor Baharav Postdoctoral fellow Eric and Wendy Schmidt Center Broad Institute	Statistical and algorithmic challenges in reference-free analysis [Video]
Mar 20	Lucy Gao Assistant Professor of Statistics University of British Columbia	Data thinning to avoid double dipping [Video]
Mar 20	Yiqun Chen Data Science Postdoctoral Fellow Zou Lab, Stanford University	Testing data-driven hypotheses post-clustering [Video]
Mar 13	Jun Cheng Google DeepMind	Alpha Missense [Video]
Mar 13	Žiga Avsec Google DeepMind	Accurate proteome-wide missense variant effect prediction with Alpha Missense [Video]
Mar 6	Sandeep Kambhampati Philipp Schneider Kai Cao Bojan Karlas	Postdoc flash talks
Feb 14	Noor Youssef Debora Marks/ Harvard Medical School/ Systems Biology Sarah Faye Gurev Debora Marks Lab (HMS), MIT	Unsupervised viral antibody escape prediction for future-proof vaccines [Video]
Feb 14	Pascal Notin	Hybrid protein language models for fitness prediction and design [Video]