MIA Talks Search

Spring 2025
Date	Speaker	Title
May 28	Harvard Division of Medical Sciences	Multimodal, Generative, and Agentic AI for Pathology	Spring 2025
May 21	Google DeepMind	Diffusion models, Schrodinger bridges and applications in bio	Spring 2025
May 14	Ophelia Venturelli	TBD	Spring 2025
May 14	Jaron Thompson University of Wisconsin-Madison, Venturelli Lab	Variational inference for uncertainty quantification of microbial community dynamics	Spring 2025
Apr 9	Alexandru Dumitrescu Aalto University Dani Korpela Aalto University	Diffusion for molecule generation []	Spring 2025
Apr 9	Harri Lähdesmäki Aalto University	Structured variational autoencoders for prediction and optimization []	Spring 2025
Apr 2	Principal ML Scientist (Group Leader) ReLU,	A primer on DNA foundation modeling	Spring 2025
Apr 2	Senior ML Scientist ReLU,	Nona, a novel multimodal masked modeling framework for functional genomics	Spring 2025
Mar 19	Head of Science & Co-Founder at FutureHouse, Associate Professor of Chemical Engineering at University of Rochester	A progress report on the automation of science	Spring 2025
Mar 19	Member of Technical Staff at FutureHouse, Ph.D. Candidate at University of Rochester	Automating scientific discovery at scale	Spring 2025
Mar 12	Princeton University Department of Computer Science	Heterogeneous reconstruction in cryo-EM	Spring 2025
Mar 12	Princeton University	Machine learning for visualizing structural landscapes inside the cell	Spring 2025
Mar 5	Cancer Data Science, ӳ��ý	GeneTEA: gene-term enrichment with natural language processing	Spring 2025
Mar 5	Iqbal Lab \| Ladders 2 Cures Accelerator, ӳ��ý	Comparing protein language models through embedding space comparison []	Spring 2025
Mar 5	Eric and Wendy Schmidt Center, ӳ��ý	Interpreting gene expression models with sparse autoencoders []	Spring 2025
Mar 5	Irizarry Lab, Harvard School of Public Health	Identifying spatially variable genes by projecting to morphologically relevant coordinates []	Spring 2025
Mar 5	Eric and Wendy Schmidt Center, ӳ��ý	Mapping the topography of spatial gene expression with interpretable deep learning []	Spring 2025
Mar 5	Berger Lab, MIT and Popic Lab, ӳ��ý	Minimizer-space computation in genomics	Spring 2025
Feb 26	University of Maryland - College Park -- Department of Computer Science and Center for Bioinformatics and Computational Biology	Counting is not easy: Assessing and quantifying uncertainty in abundance inferences from high-throughput sequencing data []	Spring 2025
Feb 26	University of Maryland -- Department of Computer Science and Center for Bioinformatics and Computational Biology	Uncertainty-aware analysis of RNA-Seq data using a tree-based framework []	Spring 2025

Spring 2025
Date	Speaker	Title
Feb 12	Aleksandra Walczak CNRS ENS	Immune repertoires: specificity and dynamics []	Spring 2025
Feb 12	Andrea Mazzolini Laboratoire de Physique de l'École Normale Supérieure (LPENS), Paris, France	Extracting dynamical properties of the immune repertoire from noisy sequencing data []	Spring 2025

Fall 2024
Date	Speaker	Title
Nov 20	Abraham Gihawi University of East Anglia	Separating Fact from Fiction – Microbial DNA in Tumours	Fall 2024
Nov 6	Marnix Medema Bioinformatics Group, Wageningen University & Research	Deciphering the Chemical Language of Microbiomes []	Fall 2024
Nov 6	Victoria Pascal Epigenetics in cancer and cell differentiation group in Germans Trias i Pujol Research Institute (IGTP)	Algorithms for metabolic pathway discovery and analysis in the human microbiome []	Fall 2024
Oct 30	Assistant Professor of Applied Mathematics University of Massachusetts Boston	Primer: Tensor Methods for Omics Data Analysis []	Fall 2024
Oct 30		C-ZIPTF: stable tensor factorization for zero-inflated multi-dimensional genomics data []	Fall 2024
Oct 23	Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign	Using Advanced Molecular Dynamics Simulation Techniques to Characterize Large-Scale Conformational Transitions in Transporters []	Fall 2024
Oct 23	Ashkan Fakharzadeh Ghaan Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois, Urbana-Champaign	Using Advanced Molecular Dynamics Simulation Techniques to Characterize []	Fall 2024
Oct 16	Ph.D. student Department of Computer Science Yale University	Primer: Large Language Models and Biological Foundation Models []	Fall 2024
Oct 16	Assistant Professor, Dept. of Computer Science & Dept. of Int. Medicine, Yale University	Single-cell analysis in the age of LLMs []	Fall 2024
Oct 9	Faculty of Computing and Data Sciences, Boston University	Flux Balance Analysis and shadow prices in constrained optimization []	Fall 2024
Oct 9	Ph.D. candidate, Boston University	Modeling genotype to phenotype: a search for the right variables and the “natural” objects of selection []	Fall 2024
Oct 2	MIA Steering Committee	Community Day	Fall 2024
Sep 25	Postdoctoral Fellow at Harvard Medical School Postdoctoral Fellow at the Whitehead Institute Schmidt Center Postdoctoral Fellow at the ӳ��ý of MIT & Harvard Ph.D. candidate at MIT Ph.D. candidate at MIT Postdoctoral Fellow at MIT	Lightning Talks []	Fall 2024

Spring 2024
Date	Speaker	Title
May 29	Marinka Zitnik Assistant Professor of Biomedical Informatics, Harvard Medical School	Geometric deep learning and generative models for protein target discovery []	Spring 2024
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 []	Spring 2024
May 8	Matthew McPartlon Chai Discovery, AI Research, VantAI	Protein Design with Deep Learning: Progress, Challenges, and Next Steps	Spring 2024
May 8	Joshua Meier Chai Discovery	Unlocking Generative AI for Drug Discovery with Zero-shot Models	Spring 2024
May 1	Sergey Ovchinnikov MIT	Protein language models learn evolutionary statistics of interacting sequence motifs	Spring 2024

Spring 2024
Date	Speaker	Title
May 1	Simon Kozlov MIT	Combining protein language and structure models to redesign E. coli proteome with a reduced amino acid alphabet	Spring 2024
Apr 10	Aparna Nathan Lecturer on Biomedical Informatics, Harvard Medical School	Single-cell models for state-dependent eQTL analysis []	Spring 2024
Apr 10	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 []	Spring 2024
Apr 3	Postdoctoral fellow Eric and Wendy Schmidt Center ӳ��ý	Statistical and algorithmic challenges in reference-free analysis []	Spring 2024
Apr 3	Stanford University	SPLASH unifies genomic analysis and discovery through a paradigm shift to statistics-first []	Spring 2024
Mar 20	Data Science Postdoctoral Fellow , Stanford University	Testing data-driven hypotheses post-clustering []	Spring 2024
Mar 20	Assistant Professor of Statistics University of British Columbia	Data thinning to avoid double dipping []	Spring 2024
Mar 13	Žiga Avsec Google DeepMind	Accurate proteome-wide missense variant effect prediction with Alpha Missense []	Spring 2024
Mar 13	Jun Cheng Google DeepMind	Alpha Missense []	Spring 2024
Mar 6	Sandeep Kambhampati Philipp Schneider Kai Cao Bojan Karlas	Postdoc flash talks []	Spring 2024
Feb 14	Pascal Notin	Hybrid protein language models for fitness prediction and design []	Spring 2024
Feb 14	Debora Marks/ Harvard Medical School/ Systems Biology Debora Marks Lab (HMS), MIT	Unsupervised viral antibody escape prediction for future-proof vaccines []	Spring 2024

Fall 2023
Date	Speaker	Title
Dec 6	Department of Developmental Biology and Genetics, Washington University in St. Louis	Dissecting cell identity via network inference and in silico gene perturbation []	Fall 2023
Dec 6	Kenji Kamimoto Samantha Morris Lab, Washington University in St.Louis	Dissecting cell identity via network inference and in silico gene perturbation []	Fall 2023
Nov 29		Causal representation learning of genetic perturbations: identifiability and combinatorial extrapolation []	Fall 2023
Nov 29	Regev Lab, Genentech & Pritchard Lab, Stanford	Large-Scale Differentiable Causal Discovery of Factor Graphs []	Fall 2023
Nov 15	David Relman lab and Dmitri Petrov lab, Stanford University	Tracking strains in the human gut microbiome []	Fall 2023
Nov 15	David Relman lab and Dmitri Petrov lab, Stanford University	Dynamics of colonization and transmission in the human gut microbiome []	Fall 2023
Nov 1	BioML Group, Microsoft Research	An introduction to diffusion models for protein design []	Fall 2023


Date	Speaker	Title
Nov 1		diffusion models

Fall 2023
Date	Speaker	Title

Fall 2023
Date	Speaker	Title
Nov 1	Microsoft Research	Bridging Biophysics and AI to Optimize Protein Design []	Fall 2023
Oct 25	Microsoft Research New England	Towards Meaningful Pretrained Models for Biology []	Fall 2023
Oct 25	Stanley Hua University of Toronto	Meaningful choice/curation of pre-training data in alignment with a downstream task []	Fall 2023
Oct 25	School of Engineering and Applied Sciences, Harvard University	Disentangling Meaningful Signal from Experimental Noise within Deep Learning Models []	Fall 2023
Oct 18	McGill University, Electrical and Computer Engineering, Montréal, Canada	Theoretical background regarding GANs and Causal GAN []	Fall 2023
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 []	Fall 2023
Oct 11		Lighting talks []	Fall 2023
Sep 20	Xinyi Zhang MIT EECS; Eric and Wendy Schmidt Center, ӳ��ý	Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease []	Fall 2023
Sep 20	Neptune Bio	Seurat v5, cross-modality mapping and large-scale clustering of single-cell data []	Fall 2023

Spring 2023
Date	Speaker	Title
May 10	ECE, Georgia Tech	A Fourier Tour of Protein Function Prediction []	Spring 2023
May 10	Dyno Therapeutics	Leveraging the Sparsity of Epistatic Interactions to Understand and Improve Models of Fitness Functions []	Spring 2023
Apr 26	Ben Deverman ӳ��ý	ML-compatible experimental approaches to accelerate AAV engineering	Spring 2023
Apr 26	Fatma Elzahraa Eid ӳ��ý	Multi-trait protein engineering - a synergistic ML-wet lab approach to AAV engineering	Spring 2023


Date	Speaker	Title
Apr 19	James Zou	Generative AI for biomedicine

Spring 2023
Date	Speaker	Title
Apr 19		Generative AI for biomedicine []	Spring 2023
Apr 19		How to evaluate medical AI []	Spring 2023
Apr 12	Gladstone Institutes	Current techniques for case-control comparisons in high-throughput transcriptomics and the need for contrastive methods []	Spring 2023
Apr 12	University of North Carolina at Chapel Hill	Contrastive latent variable models to expose changes in case-control sequencing experiments []	Spring 2023
Apr 5	Google Research, Brain Team	Intro to machine learning for molecules, small and large	Spring 2023
Mar 22	Massachusetts General Hospital, Harvard Medical School Associate Member ӳ��ý of MIT and Harvard	Image2Omics: Generating Omics Data from Images []	Spring 2023

Spring 2023
Date	Speaker	Title
Mar 22	Charles Comiter Massachusetts Institute of Technology Massachusetts General Hospital, Harvard Medical School ӳ��ý of MIT and Harvard	Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF) []	Spring 2023
Mar 8	NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark	Variational autoencoders for analysis and integration of multi-omics and multi-modal data []	Spring 2023
Mar 8	NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark	Deep dive into multi-omics variational autoencoding []	Spring 2023
Mar 1	Haoran Zhang MIT	Primer: Group Fairness in Chest X-ray Diagnosis: Helpful or Harmful? []	Spring 2023


Date	Speaker	Title
Mar 1		Hiding in plain sight – What does AI’s ability to detect patterns not visible to radiologists mean?

Spring 2023
Date	Speaker	Title
Mar 1	Emory University School of Medicine	Hiding in plain sight – What does AI’s ability to detect patterns not visible to radiologists mean? []	Spring 2023
Feb 15	Bruno Correia Laboratory of Protein Design & Immunoengineering (LPDI), EPFL	Computational protein design []	Spring 2023
Feb 15	Casper Goverde Laboratory of Protein Design & Immunoengineering (LPDI), EPFL	De novo design of proteins []	Spring 2023
Feb 8	Eric and Wendy Schmidt Center	Primer: Charting the Landscape of 3D Genome Organization with Graph Representation Learning []	Spring 2023
Feb 8	Bo Xia Gene Regulation Observatory, ӳ��ý; Society of Fellows, Harvard University	High-Throughput In Silico Genetic Screen for Discovering Novel 3D Genome Organization Regulation []	Spring 2023
Feb 1	Stanford University School of Medicine	Learning to read and write protein evolution []	Spring 2023

Fall 2022
Date	Speaker	Title
Dec 7	Principal Researcher at Microsoft Senior Researcher in Machine Intelligence at Microsoft Research	Primer: An Introduction to Causal Discovery and Inference	Fall 2022
Dec 7	Senior Researcher in Machine Intelligence at Microsoft Research Principal Researcher at Microsoft	Deep End-to-end Causal Inference	Fall 2022
Nov 23		NO MEETING WEEK	Fall 2022
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 []	Fall 2022
Nov 16	Computer Science, ETH Zurich	Neural Optimal Transport for Inferring Single-Cell Responses to Perturbations []	Fall 2022
Nov 2		NO MEETING	Fall 2022
Oct 26		No Primer	Fall 2022
Oct 26	Columbia University Irving Medical Center	Deep learning based morphological profiling for rare disease genomic medicine	Fall 2022
Oct 19	Camargo Lab, Boston Children's Hospital	Primer: Lineage tracing for tissue development and cell differentiation	Fall 2022

Fall 2022
Date	Speaker	Title
Oct 19	Damon Runyon Computational Biology Fellow, Harvard Medical School	Learning cell differentiation dynamics from lineage tracing datasets []	Fall 2022
Oct 12	Institut Pasteur & CNRS	Primer: The envelope of sequence bioinformatics in 2022 []	Fall 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 []	Fall 2022
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 []	Fall 2022
Oct 5	ӳ��ý of MIT and Harvard ӳ��ý of MIT and Harvard	Unlocking the Power of Electronic Health Record data using Deep Learning based Natural Language Processing []	Fall 2022
Sep 28	Postdoctoral Fellow in the Weissman Lab of MIT and Whitehead Institute	Towards predictive spatiotemporal modeling of single cells	Fall 2022
Sep 28	University of California, San Francisco	Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq []	Fall 2022
Sep 21	Calico Labs	Primer: Gene expression prediction from DNA sequences []	Fall 2022
Sep 21	Assistant Professor, School of Biomedical Engineering UBC	Using deep learning regulatory models and random DNA for evolutionary inference []	Fall 2022
Sep 14	ӳ��ý of MIT and Harvard	Primer: An Introduction to Bayesian Variable Selection []	Fall 2022
Sep 14	ӳ��ý of MIT and Harvard	Applications of Bayesian Variable Selection to Bioinformatics []	Fall 2022

Spring 2022
Date	Speaker	Title
May 25		No Primer	Spring 2022
May 25	Columbia University Irving Medical Center	Deep learning based morphological profiling for rare diseases genomic medicine	Spring 2022
May 18		NO MEETING THIS WEEK	Spring 2022
May 11	PhD student, Mathematics Department, MIT	Primer: Scaling microbial dynamics with Bayesian nonparametrics	Spring 2022
May 11	Division of Computational Pathology, Brigham and Women’s Hospital, Harvard Medical School; Massachusetts Institute of Technology	Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at ecosystem-scale	Spring 2022
May 4		NO MEETING THIS WEEK	Spring 2022
Apr 27		Primer: TBD	Spring 2022
Apr 27	Dept. of Molecular Cell Biology, Weizmann Institute of Science	Design principles of hormone circuits	Spring 2022
Apr 20		NO MEETING THIS WEEK	Spring 2022

Spring 2022
Date	Speaker	Title
Apr 13	Dept. of Biology, Stanford University High Meadows Environmental Institute, Princeton University	The impact of climate, social setting, and susceptibility on dengue dynamics: a case study using compartmental models, empirical dynamic modeling, and meta-analysis; Part I []	Spring 2022
Apr 13	Dept. of Biology, Stanford University; Center for Computational, Evolutionary and Human Genomics Dept. of Biology, Stanford University; University of British Columbia	The impact of climate, social setting, and susceptibility on dengue dynamics: a case study using compartmental models, empirical dynamic modeling, and meta-analysis; Part II []	Spring 2022
Apr 6		No Primer	Spring 2022
Apr 6	Deep Learning Group, Microsoft Research	Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer []	Spring 2022
Mar 30		No Primer	Spring 2022
Mar 30	Eric and Wendy Schmit Center Postdoctoral Fellow, Caicedo and Uhler labs, ӳ��ý	Towards semantic representations of tissue organization from high-parameter imaging data []	Spring 2022
Mar 23		NO MEETING THIS WEEK	Spring 2022
Mar 16		NO MEETING THIS WEEK	Spring 2022
Mar 9	Harvard Medical School	Primer: Capturing structure in high-dimensional data using K nearest neighbor graphs []	Spring 2022
Mar 9	Raychaudhuri Lab, Bioinformatics and Integrative Genomics Program, Harvard Medical School Raychaudhuri Lab, Harvard Medical School; Brigham and Women’s Hospital	Quantifying axes of inter-sample variability among transcriptional neighborhoods in single-cell datasets []	Spring 2022
Mar 2	Apple; Center for Research in Economics and Statistics, Institut Polytechnique de Paris	Primer: From matchings to optimal transport, use cases and algorithms []	Spring 2022
Mar 2	Dept. of Computational Health, Helmholtz Munich; Dept. of Mathematics, Technical University of Munich	Moscot: A scalable toolbox for optimal transport problems in single cell genomics []	Spring 2022
Feb 23		NO MEETING THIS WEEK	Spring 2022
Feb 16	Computational Biology and Data Science group, Vesalius Therapeutics	Primer: Clarifying confusion in scRNA-seq analysis []	Spring 2022
Feb 16	Dept. of Human Genetics, and the Research Computing Center, University of Chicago	Learning the "parts" of cells using topic models []	Spring 2022
Feb 9	Popic Lab, ӳ��ý Victoria Popic ӳ��ý	Primer: A deep learning approach to structural variant discovery []	Spring 2022
Feb 9	Popic Lab, ӳ��ý Victoria Popic ӳ��ý	Cue: A framework for cross-platform structural variant calling and genotyping with deep learning []	Spring 2022
Feb 2	Dept. of Biomedical Engineering, McGill University; Mila Quebec AI Institute SUNY Downstate Medical Center	Primer: Canonical correlation analysis and the structure of psychedelic experience; Towards a neurophenomenological cartography of the cortex	Spring 2022
Feb 2	Dept. of Biomedical Engineering, McGill University; Mila Quebec AI Institute SUNY Downstate Medical Center Data Sciences Platform, ӳ��ý	Trips and neurotransmitters; Discovering principled patterns across 6850 hallucinogenic experiences	Spring 2022
Jan 26		NO MEETING THIS WEEK	Spring 2022

Spring 2022
Date	Speaker	Title
Jan 19	Dunn Lab, Yale University	Primer: Comparing gene expression data across species using evolutionary methods (note: 10am start) []	Spring 2022
Jan 19	Depts. of Organismic and Evolutionary Biology, Molecular and Cellular Biology, Harvard University; Howard Hughes Medical Institute	Developing a systems approach to understanding adaptive evolutionary change using Hawaiian Drosophila as a model clade (note: 11am start)	Spring 2022

Fall 2021
Date	Speaker	Title
Dec 1	Finucane Lab, ӳ��ý ӳ��ý; Massachusetts General Hospital	Polygenic priority score for GWAS gene prioritization []	Fall 2021
Dec 1	Broderick Group, Massachusetts Institute of Technology	A new approach for high-dimensional hierarchical modeling []	Fall 2021
Nov 24		NO MEETING THIS WEEK	Fall 2021
Nov 17		NO MEETING THIS WEEK	Fall 2021
Nov 10	Van Allen Lab, Dana-Farber Cancer Institute; ӳ��ý	Primer: Genomic tools for interpreting patterns of somatic driver and passenger mutations in cancer []	Fall 2021
Nov 10	Van Allen Lab, Dana-Farber Cancer Institute; ӳ��ý	Biologically informed deep neural network for prostate cancer discovery []	Fall 2021
Nov 3		NO MEETING THIS WEEK	Fall 2021
Oct 27	Depts. of Biochemistry & Biophysics, Urology, University of California San Francisco	Primer: Capturing regulatory information encoded in RNA secondary structure []	Fall 2021
Oct 27	Goodarzi Lab, University of California San Francisco; Vector Institute Goodarzi Lab, University of California San Francisco	Computational tools for deciphering the RNA structural code []	Fall 2021
Oct 20	Department of Computer Science and Technology, University of Cambridge ӳ��ý ӳ��ý	Lightning talks (9am start, no primer) []	Fall 2021
Oct 13	Seung Lab, Princeton Neuroscience Institute, Princeton University	Scalable analysis of electron microscopy connectomics data: Revealing neural circuit properties using contrastive deep learning []	Fall 2021
Oct 13	ӳ��ý	No such thing as unlabeled: Self-supervised learning on medical data []	Fall 2021
Oct 6	Dept. of Biomedical Informatics, Harvard Medical School	Primer: Advancements and challenges for deep learning in medical imaging []	Fall 2021
Oct 6	Stanford University Stanford University Stanford University	3KG: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations []	Fall 2021
Sep 29	Institute of Computational Biology, Helmholtz Zentrum München Institute of Computational Biology, Helmholtz Zentrum München	Primer: Latent space learning in single cell genomics: Current approaches and challenges []	Fall 2021
Sep 29	Technical University of Munich Institute of Computational Biology, Helmholtz Zentrum München	Deep interpretable perturbation modeling in single cell genomics¹; Learning cell communication from spatial graphs of cells² []	Fall 2021
Sep 22	Facebook AI	Visual recognition from one or more images []	Fall 2021
Sep 22	Juan Caicedo ӳ��ý	Representation learning for single-cell, image-based phenotyping []	Fall 2021

Fall 2021
Date	Speaker	Title
Sep 15		NO MEETING THIS WEEK	Fall 2021
Sep 8		No primer	Fall 2021
Sep 8	Cellarity Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital; Harvard Medical School; ӳ��ý Chan Zuckerberg Biohub Cellarity Institute of Computational Biology, Helmholtz Zentrum Munich	Multimodal single-cell data, open benchmarks, and a NeurIPS 2021 competition (Note: 9am start) []	Fall 2021

Spring 2021
Date	Speaker	Title
Jun 2	King's College London	Primer: Optimal thinning of mcmc output with application to cardiac electrophysiology []	Spring 2021
Jun 2	Depts. of Statistics and Computer Science, Stanford University; Microsoft Research New England	Probabilistic inference and learning with Stein’s method []	Spring 2021
May 26		NO MEETING THIS WEEK	Spring 2021
May 19		NO MEETING THIS WEEK	Spring 2021
May 12	Bioinformatics and Integrative Genomics Program, Zitnik Lab, Harvard Medical School	Primer: Deep learning for biomedical networks: Methods, challenges, and frontiers []	Spring 2021
May 12	Dept. of Biomedical Informatics, Harvard University; ӳ��ý	Actionable machine learning for drug discovery and development []	Spring 2021
May 5	Marks Lab, Harvard Medical School	Primer: Estimation and testing with generative nonparametric Bayesian models []	Spring 2021
May 5	Marks Lab, Harvard Medical School	Building and evaluating generative models of biological sequences, from proteins to whole genomes []	Spring 2021
Apr 29		No primer	Spring 2021
Apr 29	Paul G. Allen School of Computer Science and Engineering, University of Washington	Deep learning of immune differentiation (Note: 12pm start) []	Spring 2021
Apr 28		NO MEETING THIS WEEK	Spring 2021
Apr 21		NO MEETING THIS WEEK	Spring 2021
Apr 14		NO MEETING THIS WEEK	Spring 2021
Apr 7		No primer	Spring 2021
Apr 7	Loh Lab, Harvard Medical School; Division of Genetics, Brigham and Women's Hospital; ӳ��ý	Imputed repeat polymorphisms point to protein-coding variants driving genetic associations []	Spring 2021
Mar 31	ӳ��ý	Primer: Density-aware visualization and sketching of single-cell transcriptomic data []	Spring 2021
Mar 31	Tutte Institute for Mathematics and Computing	Low dimensional embeddings of words and documents (and how they might apply to single-cell data) []	Spring 2021

Spring 2021
Date	Speaker	Title
Mar 24	Marks Lab, Harvard Medical School	Primer: Generative models of antibodies for functionally optimized library design []	Spring 2021
Mar 24	Marks Lab, Harvard Medical School Marks Lab, Harvard Medical School	Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning []	Spring 2021
Mar 17		NO MEETING THIS WEEK	Spring 2021
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 []	Spring 2021
Mar 10	Dept. of Statistics, Wharton School, University of Pennsylvania	Simple, flexible and effective pooled testing via hypergraph factorization []	Spring 2021
Mar 3	Van Valen Lab, California Institute of Technology	Primer: Integrating heterogeneous measurements in single cells []	Spring 2021
Mar 3	Division of Biology and Biological Engineering, California Institute of Technology	Single-cell biology in a software 2.0 world []	Spring 2021
Feb 24	Dept. Computer Science, Harvard University	Primer: Locality sensitive hashing: A sort of history and introduction []	Spring 2021
Feb 24	Sabeti Lab, ӳ��ý	Viral diagnostic design with model-based optimization []	Spring 2021
Feb 17		NO MEETING THIS WEEK	Spring 2021
Feb 10		No primer	Spring 2021
Feb 10	Pyro Team, ӳ��ý	Bayesian methods for adaptive experimental design []	Spring 2021
Feb 3		No primer	Spring 2021
Feb 3	Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center	The 3D genome and predictive models of gene regulation	Spring 2021
Jan 27	Depts. Organismic and Evolutionary Biology, Physics, Harvard University	Primer: Genomic investigations of evolutionary dynamics and epistasis in microbial evolution experiments []	Spring 2021
Jan 27		Predictable patterns in phenotypic evolution []	Spring 2021

Fall 2020
Date	Speaker	Title
Dec 9	Dept. of Statistics, Harvard University	Large-scale Bayesian inference for GWAS with coupled Markov chain Monte Carlo []	Fall 2020
Dec 9	Liu Group, Regev Lab, ӳ��ý; Dept. of Computational and Systems Biology, Massachusetts Institute of Technology	Determinants of base editing outcomes from target library analysis and machine learning []	Fall 2020
Dec 2	¹Physics of Living Systems, Massachusetts Institute of Technology ²Dept. of Physics, Boston University	Primer: Introduction to ecological models for microbiomes []	Fall 2020
Dec 2	Dept. of Physics, Boston University	Toward a statistical mechanics of microbiomes []	Fall 2020

Fall 2020
Date	Speaker	Title
Nov 25		NO MEETING THIS WEEK	Fall 2020
Nov 18		No primer	Fall 2020
Nov 18	Mahadevan Group, Harvard University	Patterns on pollen: a polysaccharide phase transition process (Note: 11am start) []	Fall 2020
Nov 11		NO MEETING THIS WEEK	Fall 2020
Nov 4		NO MEETING THIS WEEK	Fall 2020
Oct 28	Engelhardt Group, Princeton University	Primer: Generalized linear models and latent factor models []	Fall 2020
Oct 28	Dept. of Biostatistics, Harvard University	Inference in generalized bilinear models []	Fall 2020
Oct 21	Pyro team, ӳ��ý	Primer: Stochastic gradient-based variational inference []	Fall 2020
Oct 21	Pyro team, ӳ��ý	Deep probabilistic programming with Pyro []	Fall 2020
Oct 14		No primer	Fall 2020
Oct 14	Dept. of Electrical Engineering and Computer Sciences, Center for Computational Biology, Berkeley AI Research Lab, UC Berkeley; Chan Zuckerberg Biohub	Machine learning-based design of proteins (and small molecules and beyond) (Note: 12pm start) []	Fall 2020
Oct 7	Dyno Therapeutics	Primer: Biological sequence design through machine-guided exploration []	Fall 2020
Oct 7	Dyno Therapeutics	Machine-guided capsid engineering for gene therapy []	Fall 2020
Sep 30	Research Laboratory of Electronics Computational Cardiovascular Research Group, Massachusetts Institute of Technology	Primer: Modeling cardiovascular physiology []	Fall 2020
Sep 30	Dept. of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Computer Science and Artificial Intelligence Laboratory, MIT; Massachusetts General Hospital	Physiology-inspired machine learning models for predicting adverse cardiovascular outcomes []	Fall 2020
Sep 23	Clinical Machine Learning Group, Massachusetts Institute of Technology	Primer: Learning personalized treatment policies from observational data []	Fall 2020
Sep 23	Dept. of Electrical Engineering and Computer Science, Institute for Medical Engineering & Science, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology	Going beyond diagnosis and prognosis: Machine learning to guide treatment suggestions []	Fall 2020
Sep 16	University of Alberta	Primer: Machines read, humans read: parallels between computer and human representations of meaning (Note: 11am start) []	Fall 2020
Sep 16	Depts. of Computing Science, Psychology, University of Alberta; Canadian Institute for Advanced Research	Decoding word meaning from brain images collected during language production (Note: 12pm start) []	Fall 2020
Sep 9	Broderick Group, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology; ArbiLex	Primer: Gaussian processes: An introduction []	Fall 2020

Fall 2020
Date	Speaker	Title
Sep 9	Dept. of Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Laboratory, Statistics and Data Science Center, Massachusetts Institute of Technology	Fast discovery of pairwise interactions in high dimensions using Bayes []	Fall 2020

Spring 2020
Date	Speaker	Title
May 13	TBD	Primer: TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
May 13	Sabeti Lab, ӳ��ý	TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
May 6	TBD	Primer: TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
May 6	Massachusetts Institute of Technology	TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Apr 29	Dept. of Mathematics, Lehman College of the City University of New York	Primer: TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Apr 29	Dept. of Computer Science, Hunter College of the City University of New York	TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Apr 22	TBD	Primer: TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Apr 22	Uber AI; Pyro	TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Apr 15		NO MEETING	Spring 2020
Apr 8	TBD	Primer: TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Apr 8	Dept. of Physics, Boston University	TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Apr 6	Dept. of Computing, Imperial College London; Twitter	Geometric deep learning for functional protein design (Note: Joint CC&E/MIA Special Seminar, Monday at 9:30AM. See abstract for Zoom instructions) []	Spring 2020
Apr 1	Ek Group, Dept. of Computer Science, Technical University of Munich; Siemens AG	Primer: TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Apr 1	Dept. of Computer Science, University of Bristol	TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Mar 25		NO MEETING	Spring 2020
Mar 18	TBD	Primer: TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Mar 18	Dept. of Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Laboratory, Statistics and Data Science Center, Massachusetts Institute of Technology	TBD (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Mar 11	Dyno Therapeutics	Primer: Biological sequence design through machine-guided exploration (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020
Mar 11	Dyno Therapeutics	Machine-guided capsid engineering for gene therapy (NOTE: POSTPONED UNTIL FURTHER NOTICE)	Spring 2020

Spring 2020
Date	Speaker	Title
Mar 4	Data Sciences Platform, ӳ��ý	Primer: Only connect: The variety and splendor of neural network architectures	Spring 2020
Mar 4	Medical and Population Genetics, ӳ��ý; Division of Cardiology, Massachusetts General Hospital	Deep learning enables efficient genetic analysis of the human thoracic aorta	Spring 2020
Feb 26	Alex Bloemendal Neale Lab, Lander Lab, ӳ��ý; Analytic and Translational Genetics Unit, Massachusetts General Hospital	Primer: The multiple testing problem []	Spring 2020
Feb 26	Paninski Lab, Blei Lab, Dept. of Statistics, Data Science Institute, Columbia University	Smoothed nested testing on directed acyclic graphs []	Spring 2020
Feb 19		NO MEETING	Spring 2020
Feb 12	Dept. of Computer Science, University of Toronto; Vector Institute	Primer: Enforcing Lipschitz constraints for neural networks []	Spring 2020
Feb 12	Grosse Group, University of Toronto; Vector Institute Grosse Group, University of Toronto; Vector Institute	Efficient Lipschitz-constrained neural networks []	Spring 2020
Feb 5	University of Amsterdam; Qualcomm Technologies; Canadian Institute for Advanced Research Lander Lab, ӳ��ý	Screening: VAEs & Deep Inverse Modeling (Note: 10-11am in the Auditorium)	Spring 2020
Jan 29		No primer	Spring 2020
Jan 29	Alex Bloemendal Hilary Finucane	Topics Groups Nucleation Event (10-11am in Monadnock)	Spring 2020

Fall 2019
Date	Speaker	Title
Dec 18	Holiday Mixer	Holiday Mixer (Note: 10am in Monadnock)	Fall 2019
Dec 6	Theis Lab, Institute of Computational Biology	RNA velocity generalized to transient cell states through dynamical modeling (Note: 2:30-3:30pm in Monadnock) []	Fall 2019
Dec 4	Computer Science & Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology	Primer: An overview of some issues in Bayesian robustness []	Fall 2019
Dec 4	Dept. of Biostatistics, Harvard University; Dana-Farber Cancer Institute; ӳ��ý	Using bagged posteriors for robust inference and model criticism []	Fall 2019
Nov 20	Stephen Fleming Data Sciences Platform, ӳ��ý	Primer: CellBender remove-background: A deep generative model for unsupervised removal of background noise from scRNA-seq datasets []	Fall 2019
Nov 20	Mehrtash Babadi Data Sciences Platform, ӳ��ý	CellBender droplet-time-machine: Reversing mixed-template cDNA amplification artifacts in droplet-based 3' scRNA-seq assays []	Fall 2019
Nov 13	CANCELLED	CANCELLED	Fall 2019
Nov 8	Dept. of Electrical Engineering and Computer Sciences, UC Berkeley	Regev Lab, Cell Circuits and Epigenomics, and MIA special seminar: Deep generative modeling for single-cell transcriptomics (12:30-1:30pm in Monadnock)	Fall 2019
Nov 6	Crawford Lab, Brown University	Primer: Integrating topological data analysis (TDA) with statistical learning techniques []	Fall 2019
Nov 6	Center for Statistical Sciences, Center for Computational Molecular Biology, Brown University	Statistical pipeline for identifying features that differentiate classes of 3D shapes []	Fall 2019

Fall 2019
Date	Speaker	Title
Oct 30	Courant Institute, New York University Courant Institute, New York University	Primer: How do we build neural networks we can trust? []	Fall 2019
Oct 30	Courant Institute, New York University	Loss valleys, uncertainty, and generalization in deep learning []	Fall 2019
Oct 23	Depts. of Chemistry and Chemical Biology, Physics, Harvard University	Primer: Computational challenges in optical electrophysiology (Note: Auditorium) []	Fall 2019
Oct 23	Regev Lab, ӳ��ý	New imaging data types and computational challenges at the optical profiling platform (Note: Auditorium) []	Fall 2019
Oct 22	Depts. of Computer Science, Statistical Sciences, University of Toronto	It's time to talk about irregularly-sampled time series (Note: 12-1pm in Serengeti) []	Fall 2019
Oct 16	Dept. of Brain and Cognitive Science, McGovern Institute, Massachusetts Institute of Technology	Primer: Cognitive maps for navigation in the brain []	Fall 2019
Oct 16	Center for Brain Science, Harvard University	Manifold discovery of cognitive maps []	Fall 2019
Oct 9	Jon Bloom Hail Team, Neale Lab, ӳ��ý ӳ��ý	Primer: The geometry of linear regression, privacy-preserving linear algebra, and multi-party GWAS []	Fall 2019
Oct 9	Dept. of Mathematics, Massachusetts Institute of Technology	Key ideas in linear algebra []	Fall 2019
Oct 2	Carpenter Lab, Imaging Platform, ӳ��ý	Primer: Intro to computer vision and its relationship to machine learning []	Fall 2019
Oct 2	Data Sciences Platform, ӳ��ý Carpenter Lab, Imaging Platform, ӳ��ý Beth Cimini Carpenter Lab, Imaging Platform, ӳ��ý	Computer vision for hearts and cells []	Fall 2019
Sep 25	Goldenberg Lab, SickKids Research Institute; Dept. of Computer Science, University of Toronto; Vector Institute	Primer: Learning biological patterns across domains: Investigating and integrating information across data types and sources []	Fall 2019
Sep 25	SickKids Research Institute; Dept. of Computer Science, University of Toronto; Vector Institute	From predicting to explaining biology using machine learning []	Fall 2019
Sep 18	Sunyaev Lab, Dept. of Biomedical Informatics, Harvard Medical School; Division of Genetics, Brigham and Women's Hospital	Primer: DNA damage bypass is a major source of clustered mutations []	Fall 2019
Sep 18	Kharchenko Lab, Dept. of Biomedical Informatics, Harvard Medical School	Population sequencing data reveal a compendium of mutational processes in human germline []	Fall 2019
Sep 11	Ahmed Badran Infectious Disease & Microbiome Program, Chemical Biology & Therapeutic Sciences Program, ӳ��ý	Primer: Impact of mutagenesis efficiency and selection stringency modulation during continuous directed evolution []	Fall 2019
Sep 11	Dept. of Biomedical Engineering, UC Irvine	Synthetic genetic systems for rapid mutation and continuous evolution in vivo []	Fall 2019

Spring 2019
Date	Speaker	Title
May 29	Eddy Lab, Harvard University	Interpretable convolutional networks for regulatory genomics []	Spring 2019
May 22	Marks Lab, Harvard Medical School, ӳ��ý	Primer: Generative models from NLP for sequence data []	Spring 2019
May 22	Dept. of Systems Biology, Harvard Medical School; ӳ��ý	Alignment-free models for protein and antibody design []	Spring 2019

Spring 2019
Date	Speaker	Title
May 15	MIT Media Lab, Massachusetts Institute of Technology	Primer: Mechanisms for generalized learning across tasks and environments []	Spring 2019
May 15	Depts. of Statistics, Computer Science, Harvard University	Personalized HeartSteps: A reinforcement learning algorithm for optimizing physical activity []	Spring 2019
May 8	Dept. of Systems Biology, Harvard Medical School	Primer: Inference of high-dimensional dynamics []	Spring 2019
May 8	Dept. of Systems Biology, Harvard Medical School	Lineage tracing on transcriptional landscapes links state to fate during differentiation []	Spring 2019
May 6	Chan Zuckerberg Biohub	Blind denoising by self-supervision []	Spring 2019
May 1	Dept. of Medicine and Division of Genetics, Brigham and Women's Hospital, Harvard Medical School; ӳ��ý	Primer: Hidden Markov models in phasing and imputation []	Spring 2019
May 1	Loh Lab, Dept. of Medicine and Division of Genetics, Brigham and Women's Hospital, Harvard Medical School; ӳ��ý	Imputing genomic repeat variants and assessing their phenotypic effects []	Spring 2019
Apr 24	Dept. of Statistics, University of Oxford	Primer: Introduction to the tree sequence toolchain []	Spring 2019
Apr 24	Big Data Institute, University of Oxford	Succinct tree sequences for megasample genomics []	Spring 2019
Apr 17	Galen Ballentine Drexel University College of Medicine	Primer: The human brain's default mode network []	Spring 2019
Apr 17	Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University	Algorithms to understand default brain function []	Spring 2019
Apr 10	Google Brain	Primer: Extracting causal signal from high-dimensional data: challenges and techniques []	Spring 2019
Apr 10	Rabadan Lab, Dept. of Systems Biology, Data Science Institute, Columbia University	Interpreting and learning from black box models []	Spring 2019
Apr 3	Slavov Lab, Northeastern University	Primer: Quantifying proteins by mass-spec []	Spring 2019
Apr 3	Dept. of Bioengineering, Northeastern University	Understanding biological systems: In search of direct causal mechanisms []	Spring 2019
Mar 27	Aryee Lab, Zhang Lab; Dana-Farber Cancer Institute, Harvard Medical School, Massachusetts General Hospital	Modeling the 3D organization of chromosomes in the cancer cell nucleus []	Spring 2019
Mar 20	Datta Lab, Harvard Medical School	Primer: Learning structure in mouse behavior using motion sequenceing (MoSeq) []	Spring 2019
Mar 20	Datta Lab, Sabatini Lab, Harvard Medical School	Using machine learning to understand how the brain implements moment-to-moment action selection []	Spring 2019
Mar 13	Alex Bloemendal Neale Lab, Lander Lab, ӳ��ý; Analytic and Translational Genetics Unit, Massachusetts General Hospital	Primer: Random matrix theory []	Spring 2019
Mar 13	Neale Lab, ӳ��ý	Controlling for stratification in (meta-)GWAS with PCA: Theory, applications, and implications []	Spring 2019

Spring 2019
Date	Speaker	Title
Mar 6	Harvard University	Primer: Generative REgularized ModeLs of proteINs	Spring 2019
Mar 6	Harvard Medical School	End-to-end differentiable learning of protein structure []	Spring 2019
Feb 27	Cotton Seed Hail Team, Neale Lab, ӳ��ý Jon Bloom Hail Team, Neale Lab, ӳ��ý	Primer: From Morse theory to geometric ensembling via the topology of PCA []	Spring 2019
Feb 27	Stanford University Macosko Lab, ӳ��ý	Regularized linear autoencoders, probabilistic PCA, and backpropagation in the brain []	Spring 2019
Feb 13	Harvard University	Inferring geometric embeddings for single cell data []	Spring 2019
Feb 13	Zhang Lab, Regev Lab, ӳ��ý	Optics-free spatio-genetic imaging with DNA microscopy []	Spring 2019
Feb 6	Dept. of Mathematics, University of Texas at Austin	Primer: Analyzing scientific data with topological data analysis []	Spring 2019
Feb 6	Dept. of Mathematics, University of Texas at Austin	Using random matrix theory to extract signals from single-cell expression data []	Spring 2019
Jan 30	Talking Machines; Collective Next; Neural Information Processing Systems (neurIPS)	Primer: Super deep generative networks for cat robots: or how I learned to start worrying more about the public conversation []	Spring 2019
Jan 30	Talking Machines; Collective Next; Neural Information Processing Systems (neurIPS)	Group exercise: The story algorithm	Spring 2019

Fall 2018
Date	Speaker	Title
Dec 12	Dept. of Statistics, Harvard University	Primer: Challenges in high-dimensional variable selection []	Fall 2018
Dec 12	Dept. of Statistics, Harvard University	Using knockoffs to find important variables with statistical guarantees []	Fall 2018
Dec 5	Massachusetts General Hospital; Harvard Medical School; ӳ��ý Pinello Lab, Massachusetts General Hospital (MGH); Joung Lab, MGH, Harvard Medical School; Collins Lab, Massachusetts Institute of Technology	Primer: A deconvolution framework for the analysis of CRISPR tiling screen data []	Fall 2018
Dec 5	Massachusetts General Hospital; Harvard Medical School; ӳ��ý Pinello Lab, Massachusetts General Hospital; Harvard Medical School	Single-cell trajectory reconstruction, exploration and mapping from omics data []	Fall 2018
Nov 14	Macosko Lab, ӳ��ý	Primer: Intro to non-negative matrix factorization []	Fall 2018
Nov 14	Sabeti Lab, Dept. of Systems Biology, Harvard Medical School	Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq []	Fall 2018
Nov 7	Data Sciences Platform, ӳ��ý	Primer: Intro to topic models []	Fall 2018
Nov 7	Regev Lab, ӳ��ý; Kuchroo Lab, Harvard Medical School	Topic modeling the transcriptional spectrum in innate lymphoid cells []	Fall 2018
Oct 31	Engelhardt Group, Depts. of Computer Science, Quantitative and Computational Biology, Princeton University	Primer: Robust nonlinear manifold learning for single cell RNA-seq data []	Fall 2018
Oct 31	Engelhardt Group, Depts. of Computer Science, Quantitative and Computational Biology, Princeton University	Experimental design for maximizing cell-type discovery in single-cell data []	Fall 2018

Fall 2018
Date	Speaker	Title
Oct 24	Broderick Lab, Machine Learning Group, Massachusetts Institute of Technology; Regev Lab, ӳ��ý Florez Lab, Massachusetts General Hospital, ӳ��ý Broderick Lab, Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology Marks Lab, The Biophysics Graduate Program, Harvard University	Lightning talk social	Fall 2018
Oct 24	Brian Cleary Regev Lab, Lander Lab, ӳ��ý	Studying cell and tissue physiology with random composite experiments []	Fall 2018
Oct 17	Krishnaswamy Lab, Depts. of Genetics, Computer Science, Yale University	Primer: Manifold learning and graph signal processing of high-dimensional, high-throughput biological data []	Fall 2018
Oct 17	Depts. of Genetics, Computer Science, Yale University	Manifold learning yields insight into cellular state space under complex experimental conditions []	Fall 2018
Oct 10	Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology	Biomedical data sharing and analysis with privacy []	Fall 2018
Oct 3	Google Brain Google Brain	What might machine learners learn from probabilistic programming? []	Fall 2018
Sep 26	Depts. of Computer Science and Government, Harvard University	Primer: Submodular maximization and machine learning []	Fall 2018
Sep 26	Dept. of Computer Science, Harvard University	Maximizing submodular functions exponentially faster []	Fall 2018
Sep 19	University of Pennsylvania	Primer: Why is deep learning so deep? []	Fall 2018
Sep 19	University of Pennsylvania	Mapping the brain with machine learning []	Fall 2018
Jul 11	Stanford Medicine	Transcriptomic modeling of chemotherapy side effects using human iPSC-derived cardiomyocytes []	Fall 2018

Spring 2018
Date	Speaker	Title
May 23	Depts. of Computer Science, University of Copenhagen and IT University of Copenhagen Depts. of Computer Science, University of Copenhagen and IT University of Copenhagen	Primer: Learning from molecular structure []	Spring 2018
May 23	Depts. of Computer Science, University of Copenhagen and IT University of Copenhagen Depts. of Computer Science, University of Copenhagen and IT University of Copenhagen	Convolutional models of molecular structure []	Spring 2018
May 16	Lander Lab, ӳ��ý	Primer: How philosophy of science can help us better deploy machine learning in biology []	Spring 2018
May 16	Lander Lab, ӳ��ý	Interpreting sequence models	Spring 2018
May 9	Depts. of Biomedical Data Science, Computer Science, and Electrical Engineering, Stanford University	Primer: Contrastive PCA []	Spring 2018
May 9	Depts. of Biomedical Data Science, Computer Science, and Electrical Engineering, Stanford University	AI audit to uncover blind spots of data	Spring 2018
May 2	Shantanu Singh Carpenter Lab, Imaging Platform, ӳ��ý Carpenter Lab, Imaging Platform, ӳ��ý Juan Caicedo Carpenter Lab, Imaging Platform, ӳ��ý Carpenter Lab, Imaging Platform, ӳ��ý	How to make a picture worth a thousand numbers: Models and methods in biological image analysis []	Spring 2018
Apr 25	Program for Evolutionary Dynamics, Harvard University	Primer: Hamilton's rule makes no prediction and cannot be tested empirically []	Spring 2018
Apr 25	Program for Evolutionary Dynamics, Harvard University	Evolutionary dynamics []	Spring 2018

Spring 2018
Date	Speaker	Title
Apr 11	Marks Lab, Harvard Medical School	Learning protein structure with a differentiable simulator []	Spring 2018
Apr 4	Center for Genomics and Systems Biology, New York University	Primer: Inference of biological networks with biophysically motivated methods []	Spring 2018
Apr 4	Bonneau Lab, New York University	Multitask learning approaches to biological network inference: linking model estimation across diverse related datasets []	Spring 2018
Mar 28	Data Sciences Platform, ӳ��ý	Variant filtering and calling with convolutional neural networks []	Spring 2018
Mar 21	Dept. of Mathematics, Emmanuel College; Program for Evolutionary Dynamics, Harvard University	Evolutionary dynamics on any population structure []	Spring 2018
Mar 7	Indigo Agriculture	Inferring microbial phenotypes through latent representations of biological diversity	Spring 2018
Feb 28	Lander Lab, ӳ��ý	Primer: Kernel methods and the kernel "trick" []	Spring 2018
Feb 28	Price Group, Harvard School of Public Health	Linking gut microbiomes, genomes and phenotypes via linear mixed models and kernel methods []	Spring 2018
Feb 21	Dept. of Medicine and Division of Genetics, Brigham and Women's Hospital, Harvard Medical School; ӳ��ý McCarrol Lab, ӳ��ý; Dept. of Genetics, Harvard Medical School	Leveraging long range phasing to detect mosaicism in blood at ultra-low allelic fractions	Spring 2018
Feb 14	Institute for Medical Engineering & Science, Massachusetts Institute of Technology	Primer: Causal inference []	Spring 2018
Feb 14	Dept. of Electrical Engineering and Computer Science, Institute for Medical Engineering & Science, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology	AI for health needs causality []	Spring 2018
Feb 7	Institute for Medical Engineering and Science, Dept. of Civil and Environmental Engineering, Massachusetts Institute of Technology	Rapid bacterial adaptation within individual human microbiomes	Spring 2018

Fall 2017
Date	Speaker	Title
Dec 13	Dept. of Biomedical Informatics, Harvard Medical School; Dept. of Biostatistics and Computational Biology, Dana-Farber Cancer Institute Harvard Medical School; Dana-Farber Cancer Institute	In search of lost time: Reconstructing the evolutionary history of cancer genomes []	Fall 2017
Dec 6	Troyanskaya Lab, Lewis-Sigler Institute for Integrative Genomics, Princeton University	Primer: Integrated, tissue-specific analysis of biological data []	Fall 2017
Dec 6	Lewis-Sigler Institute for Integrative Genomics, Princeton University; Center for Computational Biology, Flatiron Institute	From genome to networks: A data-driven, tissue-specific view of human disease	Fall 2017
Nov 29	Lander Lab, ӳ��ý	Primer: Introduction to Hi-C	Fall 2017
Nov 29	Center for Genomic Architecture, Baylor College of Medicine; Depts. of Computational and Applied Mathematics and Computer Science, Rice University	A 3D Code in the human genome	Fall 2017
Nov 15	Microsoft Research	Machine-learning-based CRISPR guide design []	Fall 2017
Nov 8	Microsoft Research	Automated Machine Learning []	Fall 2017
Nov 1	Dept. of Mathematics, Massachusetts Institute of Technology	Message passing algorithms for cryo-EM and synchronization []	Fall 2017

Fall 2017
Date	Speaker	Title
Oct 25	MIT CSAIL, HMS, Harvard CS Dept. of Computer Science, Harvard University; Harvard/MIT MD-PhD Program, Harvard Medical School	Primer: Hypothesis testing and measures of dependence []	Fall 2017
Oct 25	MIT CSAIL, HMS, Harvard CS Dept. of Computer Science, Harvard University; Harvard/MIT MD-PhD Program, Harvard Medical School	Detecting novel associations in large data sets []	Fall 2017
Oct 11	Michael Rooney Neon Therapeutics	Primer: Intro to tumor immunity []	Fall 2017
Oct 11	Hacohen Lab, ӳ��ý, Massachusetts General Hospital Neon Therapeutics	Improving endogenous antigen prediction to support personalized cancer vaccine development []	Fall 2017
Oct 4	Hilary Finucane Medical and Population Genetics, ӳ��ý	Insight into the biology of common diseases using summary statistics of large genome-wide association studies []	Fall 2017
Sep 27	Dept. of Mathematics, Ecole Normale Supérieure Paris	Primer: A tutorial on optimal transport []	Fall 2017
Sep 27	ӳ��ý; Statistics and Data Science Center, Massachusetts Institute of Technology	Learning developmental landscapes from single-cell gene expression with optimal transport []	Fall 2017
Sep 20	Nick Patterson ӳ��ý; Reich Lab, Harvard University	Learning phylogeny through f-statistics []	Fall 2017
Sep 13	Finucane Lab, ӳ��ý	Primer: Generalized least squares []	Fall 2017
Sep 13	Dept. of Computer Science, Harvard University; Harvard/MIT MD-PhD Program, Harvard Medical School	Detecting effects of transcription factors on disease []	Fall 2017
Sep 6	Data Sciences Platform, ӳ��ý	Primer: Classifying genomic sequences with convolutional neural networks []	Fall 2017
Sep 6	Calico Life Sciences	Reading the rules of gene regulation from the human noncoding genome []	Fall 2017

Fall 2016
Date	Speaker	Title
Dec 14	Hail Team, Neale Lab, ӳ��ý	What is a compiler? []	Fall 2016
Dec 14	The Programming Languages Group, Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS)	Compiling probabilistic programs []	Fall 2016
Dec 7	Karsten Krug Proteomics Platform, ӳ��ý	Mass spectrometry-based proteomics []	Fall 2016
Dec 7	Proteomics Platform, ӳ��ý	Spectral unmixing for next-generation mass spectrometry proteomics []	Fall 2016
Nov 16	Beck Lab, Harvard Medical School	Practical recommendations for training convolutional neural nets []	Fall 2016
Nov 16	Beck Lab, Harvard Medical School Dept. of Pathology, Harvard Medical School	Deep learning for computational pathology []	Fall 2016
Nov 9	Data Sciences Platform, ӳ��ý	Dirichlet processes []	Fall 2016
Nov 9	Donnelly Centre for Cellular and Biomedical Research, Depts. of Molecular Genetics, Computer Science, and Electrical and Computer Engineering, University of Toronto	Algorithms for reconstructing tumor evolution []	Fall 2016

Fall 2016
Date	Speaker	Title
Nov 2	Depts. of Genetics and Computer Science, Stanford University	Integrative, interpretable deep learning frameworks for regulatory genomics and epigenomics []	Fall 2016
Oct 26	Twitter Cortex	Automatic differentiation, the algorithm behind all deep neural networks []	Fall 2016
Oct 26	Churchman Lab, Harvard Medical School	FIDDLE: An integrative deep learning framework for functional genomic data inference []	Fall 2016
Oct 12	Cancer Program, ӳ��ý	Topological data analysis: What is persistent homology? []	Fall 2016
Oct 12	Dept. of Mathematics, Warren Center for Network and Data Sciences, University of Pennsylvania	Topological data analysis: What can persistent homology see? []	Fall 2016
Sep 28	Dept. of Systems Biology, Harvard University; Harvard-MIT Health Sciences and Technology	Experimental and computational techniques underlying RNA-seq []	Fall 2016
Sep 28	Dept. of Data Sciences, Dana-Farber Cancer Institute; Dept. of Biostatistics, Harvard School Public Health	Overcoming bias and batch effects in high-throughput data []	Fall 2016
Sep 21	Dept. of Computer Science, Princeton University	Probabilistic generative models and posterior inference []	Fall 2016
Sep 21	Dept. of Computer Science, Columbia University	Automated inference and the promise of probabilistic programming []	Fall 2016
Sep 14	Brian Cleary Regev Lab, Lander Lab, ӳ��ý; Dept. of Computational and Systems Biology, Massachusetts Institute of Technology	Composite measurements and molecular compressed sensing for efficient transcriptomics at scale []	Fall 2016
Sep 14	Alex Bloemendal Hail Team, Neale Lab, ӳ��ý; Analytic and Translational Genetics Unit, Massachusetts General Hospital	Introduction to compressed sensing	Fall 2016

Spring 2016
Date	Speaker	Title
Jun 1	Depts. of Genome Sciences, Electrical Engineering, and Computer Science, University of Washington	Identifying molecular markers for cancer treatment from big data	Spring 2016
May 25		MIA Breakfast Social	Spring 2016
May 18	Hail Team, Neale Lab, ӳ��ý Jon Bloom Hail Team, Neale Lab, ӳ��ý	Basic introduction to distributed computation []	Spring 2016
May 18	Dept. of Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology; Databricks	Scaling data analysis with Apache Spark []	Spring 2016
May 11	Data Sciences Platform, ӳ��ý	Variational Bayesian inference []	Spring 2016
May 11	Depts. of Statistics and Computer Science, Data Science Institute, Columbia University	Scaling and generalizing variational inference []	Spring 2016
May 4	Harvard Intelligent Probabilistic Systems, Harvard University; Harvard Medical School; Massachusetts Institute of Technology	Gaussian processes []	Spring 2016
May 4	Dept. of Computer Science, Princeton University	Bayesian structured sparsity: Rethinking sparse regression []	Spring 2016
Apr 27	Genetic Perturbation Platform, ӳ��ý	Linear codes []	Spring 2016

Spring 2016
Date	Speaker	Title
Apr 27	Dept. of Computer Science, Columbia University; NY Genome Center	Compressed experiments []	Spring 2016
Apr 27	Dept. of Electrical Engineering, Stanford University; Dept. of Electrical Engineering and Computer Sciences, UC Berkeley	The science of information: Case studies from DNA and RNA assembly []	Spring 2016
Apr 20	Daly Lab, Massachusetts General Hospital; ӳ��ý	Multiple testing and false discovery rate	Spring 2016
Apr 20	Zhang Lab, ӳ��ý	DNA microscopy and the sequence-to-image inverse problem	Spring 2016
Apr 13	Connectivity Map, ӳ��ý	t-dist. stochastic neighbor embedding (t-SNE)	Spring 2016
Apr 13	Shantanu Singh Carpenter Lab, ӳ��ý Imaging Platform, ӳ��ý Carpenter Lab, ӳ��ý	Information in cell images: Targeting diseases and characterizing compounds	Spring 2016
Apr 6	Rinn Lab, Dept. of Stem Cell and Regenerative Biology, Harvard University	Convolutional neural nets	Spring 2016
Apr 6	Atomwise	AtomNet: a deep convolutional neural net for bioactivity prediction in structure-based drug discovery	Spring 2016
Mar 30	Dept. of Mathematics, Massachusetts Institute of Technology	Non-negative matrix factorization (NMF)	Spring 2016
Mar 30	Dept. of Organismic and Evolutionary Biology, Harvard University	The effects of population pedigrees on gene genealogies	Spring 2016
Mar 23	Alex Bloemendal Hail Team, Neale Lab, ӳ��ý	Linear models III: regularization, LASSO and sparsity	Spring 2016
Mar 23	Google Brain, Google Research Cambridge	A quick introduction to TensorFlow and related API's	Spring 2016
Mar 16	Harvard Intelligent Probabilistic Systems, Harvard University; Harvard Medical School; Massachusetts Institute of Technology	Linear models II: Regularization and ridge	Spring 2016
Mar 16	Janelia Research Campus; Howard Hughes Medical Institute	Open source tools for large-scale neuroscience []	Spring 2016
Mar 9	Hilary Finucane Price Lab, Harvard School of Public Health; Dept. of Mathematics, Massachusetts Institute of Technology	Linear models I: Ordinary least squares	Spring 2016
Mar 9	Price Lab, Harvard School of Public Health	Haplotype phasing in large cohorts: Modeling, search, or both?	Spring 2016
Mar 2	McCarrol Lab, ӳ��ý; Dept. of Genetics, Harvard Medical School	Hidden Markov models II	Spring 2016
Mar 2	Dept. of Physics, Institute for Medical Engineering and Science, Massachusetts Institute of Technology	Polymer models of chromosomes	Spring 2016
Feb 24	McCarrol Lab, ӳ��ý; Dept. of Genetics, Harvard Medical School	Hidden Markov models I	Spring 2016
Feb 24	Dept. of Statistics, UC Berkeley	Sparse inverse problems	Spring 2016

Spring 2016
Date	Speaker	Title
Feb 17	Hail Team, Neale Lab, ӳ��ý; Dept. of Mathematics, Massachusetts Institute of Technology	Frequentist vs Bayesian inference	Spring 2016
Feb 17	Dept. of Electrical Engineering and Computer Science, Institute for Data, Systems and Society (IDSS), Massachusetts Institute of Technology	Gene regulation in space and time []	Spring 2016
Feb 10	Data Sciences Platform, ӳ��ý	Primer: Principal component analysis (PCA)	Spring 2016
Feb 10	Dept. of Systems Biology, Harvard University	Systems biology: Can mathematics lead experiments?	Spring 2016
Feb 3	Dept. of Biomedical Informatics, Harvard University; Brigham and Women's Hospital; ӳ��ý	Judging the importance of human mutations using evolutionary models	Spring 2016
Jan 27	Depts. of Electrical and Computer Engineering, Computer Science, and Medical Research, University of Toronto; Deep Genomics	Genomic medicine: Will software eat bio?	Spring 2016

Fall 2015
Date	Speaker	Title
Nov 30	Datta Lab, Harvard Intelligent Probabilistic Systems, Harvard University	TS II. Modeling structure in time series	Fall 2015
Nov 23	Datta Lab, Harvard Intelligent Probabilistic Systems, Harvard University; Twitter	TS I. Mapping sub-second structure in mouse behavior	Fall 2015
Nov 16	Rinn Lab, Dept. of Stem Cell and Regenerative Biology, Harvard University	Harvard Stem Cell NN III. Learning the regulatory code of the accessible genome with deep convolutional neural nets	Fall 2015
Nov 12	The Original HIPSter, Twitter Cortex, Talking Machines	Machine learning and the life sciences: Beyond data analysis []	Fall 2015
Nov 9	Harvard Intelligent Probabilistic Systems, Harvard University	NN II. Convolutional networks on graphs for learning molecular fingerprints	Fall 2015
Nov 2	Harvard Intelligent Probabilistic Systems, Harvard University	NN I. Reverse-mode differentiation and autograd	Fall 2015
Oct 19	Harvard Intelligent Probabilistic Systems, Harvard University	DM II. Discrete models with continuous latent structure: A new hope	Fall 2015
Oct 13	ӳ��ý	DM I. Bayesian logistic regression and mixed models: Revenge of the Gibbs	Fall 2015
Sep 21	Alex Bloemendal ӳ��ý; Analytic and Translational Genetics Unit, Massachusetts General Hospital	CS2: Compressed sensing	Fall 2015
Sep 21	Dept. of Earth and Planetary Sciences, Harvard University; Google Research	CS1: Exploiting sparse and quantized signals to solve linear systems	Fall 2015
Sep 14	Dept. of Bioengineering, Northeastern University	Quantifying protein isoforms	Fall 2015

Summer 2015
Date	Speaker	Title
Jul 27	ӳ��ý; Harvard Medical School; Massachusetts General Hospital	Challenges in normalization of RNAseq data	Summer 2015
Jul 20	ӳ��ý	LD score regression for distinguishing confounding from polygenicity	Summer 2015
Jul 13	Mark Flaherty Data Sciences Platform, ӳ��ý	Introduction to evolutionary algorithms and NEAT	Summer 2015

Summer 2015
Date	Speaker	Title
Jul 6	Data Sciences Platform, ӳ��ý	The Chinese restaurant process and Indian buffet process	Summer 2015
Jun 29	Data Sciences Platform, ӳ��ý	Introduction to Dirichlet processes	Summer 2015
Jun 22	ӳ��ý	Conjugate priors and Hardy-Weinberg equilibrium (Bishop, Ch2)	Summer 2015
Jun 15	ӳ��ý	Discussion of Pachter's p-value prize	Summer 2015
Jun 8	ӳ��ý	Choosing priors in Bayesian inference	Summer 2015
Jun 1	ӳ��ý	Graph-based genetic sequence representation	Summer 2015

Spring 2015
Date	Speaker	Title
May 4	ӳ��ý	Introduction to Gaussian processes and Bayesian optimization (Bishop, Ch6)	Spring 2015
Apr 28	ӳ��ý	Connectivity map and challenges in data normalization	Spring 2015
Apr 14	ӳ��ý	Linear mixed models for genetic association analysis	Spring 2015
Apr 7	Data Sciences Platform, ӳ��ý	Genetic fingerprints and contamination estimation	Spring 2015
Mar 30	Alex Bloemendal Dept. of Mathematics, Harvard University	Markov Chain Monte Carlo and Gibbs sampling on Gaussian mixture models (Bishop, Ch11)	Spring 2015
Mar 23	Laura Gauthier MacArthur Lab, Data Sciences Platform, ӳ��ý ӳ��ý	Variant quality score recalibration	Spring 2015
Mar 16	Cotton Seed Dept. of Mathematics, Massachusetts Institute of Technology	Variational Bayes and inference on Gaussian mixture models (Bishop, Ch10)	Spring 2015
Mar 9	Dept. of Mathematics, Massachusetts Institute of Technology; ӳ��ý	Expectation maximization and inference on Gaussian mixture models (Bishop, Ch9)	Spring 2015
Mar 2	Dept. of Mathematics, Massachusetts Institute of Technology; ӳ��ý	Introduction to Bayesian graphical models: the Gaussian mixture model (Bishop, Ch8)	Spring 2015
Feb 24	Alex Bloemendal Dept. of Mathematics, Harvard University Dept. of Mathematics, Massachusetts Institute of Technology; ӳ��ý ӳ��ý Dept. of Mathematics, Massachusetts Institute of Technology	Comparison of dimensional reduction methods: PCA, ICA, NMF, tSNE, and diffusion maps	Spring 2015
Jan 26	ӳ��ý	Independent component analysis (ICA) and projection pursuit	Spring 2015

Fall 2014
Date	Speaker	Title
Dec 15	Dept. of Mathematics, Massachusetts Institute of Technology; ӳ��ý Dept. of Mathematics, Massachusetts Institute of Technology	Non-linear dimensional reduction: tSNE and diffusion maps	Fall 2014
Dec 8	Alex Bloemendal Dept. of Mathematics, Harvard University	Non-negative matrix factorization (NMF)	Fall 2014
Nov 24	Alex Bloemendal Dept. of Mathematics, Harvard University	Principle component analysis (PCA) and the Marchenko-Pastur law	Fall 2014

Fall 2014
Date	Speaker	Title
Nov 3	Dept. of Mathematics, Massachusetts Institute of Technology; ӳ��ý ӳ��ý	Puzzle day: Drunk Monty Hall, the two envelopes, the bloody crime scene, and Simpson's paradox	Fall 2014
Oct 27	Dept. of Mathematics, Massachusetts Institute of Technology; ӳ��ý	Optimal coverage in rare variant association studies	Fall 2014
Oct 20	ӳ��ý	Detecting sample swap	Fall 2014
Oct 6	ӳ��ý	Contingency tables III: Examples in genetics	Fall 2014
Sep 29	ӳ��ý	Contingency tables II: Correlation, Pearson chi-squared test, and Fisher exact test	Fall 2014
Sep 22	ӳ��ý	Contingency tables I: t-test and z-test	Fall 2014

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