Models, Inference & Algorithms (MIA)

Please email mia-team@broadinstitute.org with a line of introduction to receive our weekly emails, including all relevant details for upcoming talks.

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

9:00am Primer

10:00am Meeting

10:50am 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, 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:Salil Bhate (since Jan 2023) & Orr Ashenberg (since Sep 2023).

MIA's efforts are driven by the Steering Committee: Aleksandrina Goeva (Jan 2021 - Dec 2022) Alex Bloemendal (co-chair, Fall 2015 - Jan 2022), Lateisha Copeland-Guadarrama, Elizabeth Wood, Emma Stickgold, Marzieh Haghighi, Mehrtash Babadi, Mor Nitzan, Ray Jones, and Sam Friedman, Wengong Jin, Salil Bhate, Orr Ashenberg, Eli Bingham, Matthew Amodio, David Fischer, Ashley Conard.

Jon Bloom and Alex Bloemendal founded MIA in the Fall of 2015. Jon Bloom served as a co-chair through July 2019. Past Steering Committee members: Allen Goodman, Debora Marks, David Benjamin, Dylan Kotliar, Gopal Sarma (co-chair, Jul 2019 - Jan 2021), James McFarland, Jon Bloom, Hilary Finucane, Sam Riesenfeld, Yakir Reshef, Aviad Tsherniak, Max Shen, Brian Cleary, Mollie Morg, and Anika Gupta, Martin Jankowiak, Arnav Mehta.

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 2023 Schedule: 9 am Primer, 10 am Meeting, 10:50 am Discussion unless noted otherwise, in-person in 75A-M1-Acadia. 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
Sep 20	Xinyi Zhang MIT EECS; Eric and Wendy Schmidt Center, Broad Institute	Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease [Video]
Sep 20	Yuhan Hao Neptune Bio	Seurat v5, cross-modality mapping and large-scale clustering of single-cell data [Video]
Oct 11		Lighting talks
Oct 18	Yazdan Zinati McGill University, Electrical and Computer Engineering, Montréal, Canada	Theoretical background regarding GANs and Causal GAN [Video]
Oct 18	Amin Emad, Prof McGill University and Mila (Quebec AI Institute)	GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks [Video]
Oct 25	Alex Lu Microsoft Research New England	Towards Meaningful Pretrained Models for Biology [Video]
Oct 25	Alexander Lin School of Engineering and Applied Sciences, Harvard University	Disentangling Meaningful Signal from Experimental Noise within Deep Learning Models [Video]
Oct 25	Stanley Hua University of Toronto	Meaningful choice/curation of pre-training data in alignment with a downstream task [Video]
Nov 1	Sarah Alamdari BioML Group, Microsoft Research	An introduction to diffusion models for protein design
Nov 1	Ava Amini Microsoft Research	Bridging Biophysics and AI to Optimize Protein Design
Nov 15	Katherine Xue David Relman lab and Dmitri Petrov lab, Stanford University	Tracking strains in the human gut microbiome [Video]
Nov 15	Katherine Xue David Relman lab and Dmitri Petrov lab, Stanford University	Dynamics of colonization and transmission in the human gut microbiome [Video]
Nov 29	Jiaqi Zhang	Causal representation learning of genetic perturbations: identifiability and combinatorial extrapolation
Nov 29	Romain Lopez Regev Lab, Genentech & Pritchard Lab, Stanford	Large-Scale Differentiable Causal Discovery of Factor Graphs
Dec 6	Samantha Morris Department of Developmental Biology and Genetics, Washington University in St. Louis	Dissecting cell identity via network inference and in silico gene perturbation
Dec 6	Kenji Kamimoto Samantha Morris Lab, Washington University in St.Louis	Dissecting cell identity via network inference and in silico gene perturbation