Enhanced Insights for Rare Diseases: Leveraging External Controls and Longitudinal Data in snSMART Designs
2024-11-21 16:00 - 17:00 (America/New_York)
HSPH Biostatistics/DFCI Data Science Colloquium
Thursday, November 21 at 4:00pm
Harvard TH Chan School of Public Health
FXB-G01
Kelley Kidwell, PhD
Professor, Biostatistics
Associate Chair for Academic Affairs, Biostatistics
University of Michigan, Ann Arbor

Spatial Transcriptomics Deconvolution Analysis
2024-12-05 10:00 - 12:00
The Harvard Chan Bioinformatics Core in partnership with The Cancer Data Sciences Program at DF/HCC are very excited to present the Fall seminar series:
Novel Applications in Single Cell Omics

Ligand receptor analysis in a spatial context
November 7th
Sizun Jiang, PhD
Beth Israel Deaconess Medical Center Harvard Medical School
Martin Hemberg, PhD and Jingyi Cao, PhD Brigham and Women’s Hospital
Harvard Medical School
Trajectory inference in single cell data
November 21st
Luca Pinello, PhD and Cameron Ray Smith, MD, PhD
Massachusetts General Hospital
Spatial transcriptomics deconvolution analysis
December 5th
Kevin Wei, MD, PhD Brigham and Women’s Hospital
Ruben Dries, PhD and George Chen Boston University School of Medicine
All seminar sessions are hybrid events being held from 10am to noon.
Location: CLSB Building, 3 Blackfan Circle, Boston MA 02115

Register now at: https://bit.ly/fall2024_speaker_series

EVOCANCERGPT: Generating Cancer Progression and Treatment Responses with Single-Cell RNA Sequences
2024-12-11 12:00 - 13:00
CompBio Connection Seminar Series
Wednesday December 11 at 12:00pm
Zelen Commons, 11th floor
Center for Life Sciences Building
Sam Wang, Computational Biologist, Department of Data Science
Lunch is provided.

Interpretable and Data-driven Machine Learning Models for Analyzing High-dimensional Biological Data
2024-12-19 12:00 - 13:00
Data Science Seminar
Thursday December 19, 2024 – 12pm-1pm
Center for Life Sciences Building, room 11081
Junchen Yang
Department of Computational Biology and Bioinformatics, Yale University

Nice-Driven Cell Identities in the Self-Renewing Stomach Corpus Epithelium
2025-02-05 12:00 - 13:00
CompBio Connections Seminar
Wednesday February 5 at 12:00pm
Center for Life Sciences Building, Zelen Commons
Ke Li, PhD, Research Fellow, Dana-Farber Cancer Institute and Harvard Medical School
Lunch is provided.

Choosing Good Subsamples for Regression Modelling: Nearly-True Models?
2025-02-06 16:00 - 17:00
Harvard Biostatistics Colloquium Series
Thursday February 6th
4:00-5:00PM
Harvard TH Chan School of Public Health, FXB G12
Thomas Lumley, PhD, Chair in Biostatistics, University of Aukland, New Zealand; Affiliate Professor, University of Washington, Department of Biostatistics

Analysis and Design of RNA sequences with Deep Learning
2025-02-13 11:00 - 12:00
Data Science Seminar
Thursday February 13th at 11am
Center for Life Sciences Building, 111081
Joseph Valencia, Oregon State University

Current Methods in Single Cell FFPE Analysis
2025-02-19 12:00 - 13:00
CompBio Connections
February 19, 2025 at 12pm
DFCI Center for Life Science Building, Zelen Commons
Anthony Anselmo
Lead Bioinformatician
Center for Cancer Genomics, DFCI
Lunch is provided.

Single-cell Multi-sample Multi-condition Data Integration to Uncover Disease Signatures
2025-02-27 16:00 - 17:00 | Harvard TH Chan School of Public Health, FXB G13, 677 Huntington Ave, Boston, MA, United States
HSPH Biostatistics and DFCI Data Science Colloquium
Thursday February 27th at 4pm
HSPH FXB Room G13
Yingxin Lin, PhD
Postdoctoral Associate in the Department of Biostatistics at the Yale School of Public Health
The recent emergence of multi-sample multi-condition single-cell multi cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. In this talk, I will present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. scMerge2 is generalized to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large data collection with over five million cells from 1000+ individuals, we demonstrate that the integration of multi-sample multi-condition scRNAseq from multiple cohorts reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression.

How Do Neural Networks Learn Features From Data?
2025-03-03 16:00 - 17:00 | Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115
HSPH Biostatistics and DFCI Data Science Colloquium
Monday March 3rd at 4:00pm
HSPH Kresge G2
Adityanarayanan Radhakrishnan
Eric and Wendy Schmidt Center Postdoctoral Fellow, Broad Institute of MIT and Harvard, Harvard School of Engineering and Applied Sciences
Abstract: Understanding how neural networks learn features, or relevant patterns in data, is key to accelerating scientific discovery. In this talk, I will present a unifying mechanism that characterizes feature learning in neural network architectures. Namely, features learned by neural networks are captured by a statistical operator known as the average gradient outer product (AGOP). More generally, the AGOP enables feature learning in machine learning models that have no built-in feature learning mechanism (e.g., kernel methods). I will present two applications of this line of work. First, I will show how AGOP can be used to steer LLMs and vision-language models, guiding them towards specified concepts and shedding light on vulnerabilities in these models. I will then discuss how AGOP can be used to discover cellular programs (sets of genes whose expressions exhibit dependencies across cell subpopulations) from millions of sequenced cells. I will show how AGOP identified programs that reflect the heterogeneity found in various cell types, subtypes, and states in this data. Overall, this line of work advances our fundamental understanding of how neural networks extract features from data, leading to the development of novel, interpretable, and effective methods for use in scientific applications.

Universal Prediction of Cell-cycle Position Using Transfer Learning
2025-03-06 16:00 - 17:00 | Harvard TH Chan School of Public Health, FXB G13, 677 Huntington Ave, Boston, MA, United States
HSPH Biostatistics and DFCI Data Science Colloquium
Thursday, March 6, 2025
4:00pm
Harvard TH Chan School of Public Health, FXB G13
Kasper Hansen, PhD
Associate Professor, McKusick-Nathans Insitute of Genetic Medicine, Department of Biostatistics, Johns Hopkins University
A significant barrier to progress in biomedical data science is the development of prediction models that work across contexts such as different instruments, facilities or hospitals. This is particularly difficult for predictions based on genomics data. Here, we present an example of a generalizable prediction model.
The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data.
Here, we present tricycle, an R/Bioconductor package, which addresses this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays.

Decoding Aging at Spatial and Single-cell Resolution with Machine Learning
2025-03-10 16:00 - 17:00 | Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115
﻿HSPH Biostatistics and DFCI Data Science Colloquium
Monday March 10th at 4:00pm
HSPH Kresge G2
Eric Sun
PhD Candidate, Department of Biomedical Informatics
Stanford University
Aging is a highly complex process and the greatest risk factor for many chronic diseases including cardiovascular disease, dementia, stroke, diabetes, and cancer. Recent spatial and single-cell omics technologies have enabled the high-dimensional profiling of complex biology including that underlying aging. As such, new machine learning and computational methods are needed to unlock important insights from spatial and single-cell omics datasets. First, I present the development of high-resolution machine learning models (‘spatial aging clocks’) that can measure the aging of individual cells in the brain. Using these spatial aging clocks, I discovered that some cell types can dramatically influence the aging of nearby cells. Next, I present new computational and statistical methods for overcoming the gene coverage limitations of existing spatially resolved single-cell omics technologies, which have enabled the discovery of gene pathways underlying the spatial effects of brain aging.

Dissecting Tumor Transcriptional Heterogeneity from Single-cell RNA-seq Data by Generalized Binary Covariance Decomposition
2025-03-11 16:00 - 17:00 | Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115
HSPH Biostatistics and DFCI Data Science Colloquium
Tuesday March 11th at 4:00pm
HSPH FXB G12
Yusha Liu, PhD
Research Assistant Professor
Department of Biostatistics
The University of North Carolina at Chapel Hill
Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce therapeutically relevant insights. However, strong inter-tumor heterogeneity can obscure more subtle patterns that are shared across tumors. In this talk, I will introduce a novel statistical method, generalized binary covariance decomposition (GBCD), to address this problem. GBCD can decompose transcriptional heterogeneity into interpretable components — including patient-specific, dataset-specific and shared components relevant to disease subtypes — and that, in the presence of strong inter-tumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD produced a refined characterization of existing tumor subtypes, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma.

Data Integration in Spatial and Single Cell Omics: What is Erased, and Can you Recover it?
2025-03-27 16:00 - 17:00 | Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115
HSPH Biostatistics and DFCI Data Science Colloquium
Thursday, March 27, 2025
4:00pm
Harvard TH Chan School of Public Health, FXB G13

Nancy Zhang, PhD
Ge Li and Ning Zhao Professor, Professor of Statistics and Data Science, Vice Dean of Wharton Doctoral Programs, The Wharton School, University of Pennsylvania
In single-cell and spatial biology, data integration refers to the alignment of cells across samples and modalities, and is an ubiquitous challenge affecting all downstream analyses. The goal in cell integration is to find cells across data sets that share the same biological state that may be obscured by technical differences.
In this talk, I will cast the cell integration problem on a continuum of weak to strong linkage, depending on the strength of feature sharing between experiments. First, I will examine integration across data modalities of weak linkage. This arises when there are few shared features between the data being integrated, for example, between single-cell RNA sequencing data and spatial proteomics data. For this, I will present MaxFuse, a method that leverages higher order relationships between all features, including unshared features, to achieve accurate integration. Next, we consider the scenario of data alignment across the same modality in clinical scale studies. For this setting, I will show that existing paradigms are overly aggressive, erasing disease and treatment effects and introducing severe data distortion. I will introduce a “pool-of-controls” experimental design concept to disentangle biological variation from unwanted variation. Based on this, I will describe CellANOVA, a novel statistical model and scalable algorithm that recovers biological signals lost during batch integration and corrects integration related data distortion. Through these two contrasting paradigms, I will share the key lessons learned and the remaining challenges in this field.

Fréchet Regression of Random Objects on Vector Covariates and Its Applications for Single Cell RNA-seq Data Analysis
2025-04-03 16:00 - 17:00 | Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115
HSPH Biostatistics and DFCI Data Science Colloquium
Thursday, April 3, 2025
4:00pm
Harvard TH Chan School of Public Health, FXB G13
Hongzhe Li, PhD
Perelman Professor of Biostatistics, Epidemiology and Informatics
Director, Center for Statistics in Big Data Vice Chair for Research Integration, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
Population-level single-cell RNA-seq data captures gene expression profiles across thousands of cells from each individual in a sizable cohort. This data facilitates the construction of cell-type- and individual-specific gene co-expression networks by estimating covariance matrices. Investigating how these co-expression networks relate to individual-level covariates provides critical insights into the interplay between molecular processes and biological or clinical traits. This talk introduces Fréchet regression, modeling covariance matrices as outcomes and vector covariates as predictors, using the Wasserstein distance between covariance matrices as a metric instead of the Euclidean distance. A test statistic is proposed based on the Fréchet mean and covariate-weighted Fréchet mean, with its asymptotic null distribution derived. Analysis of large-scale single-cell RNA-seq data reveals an association between the co-expression network of genes in the nutrient-sensing pathway and age, highlighting perturbations in gene co-expression networks with aging. Additionally, a robust local Fréchet regression approach, leveraging neural unbalanced optimal transport, is briefly discussed to explore how cells are temporally organized during the differentiation of human embryonic stem cells into embryoid bodies.

Modeling Multiscale Genome and Cellular Organization
2025-04-15 16:00 - 17:00
HSPH Biostatistics and DFCI Data Science Seminar
Tuesday April 15 at 4:00pm
Dana-Farber Cancer Institute
Center for Life Sciences Building, 11th Floor, Room 11081
Jian Ma, PhD
Ray and Stephanie Lane Professor of Computational Biology
Carnegie Mellon University

The intersection of Al/ML and biomedicine is entering a transformative era, with growing potential to
impact both basic research and translational medicine. Yet, despite remarkable advances in high-
throughput technologies across genomics and cell biology, our understanding of the diverse cell types
in the human body and the underlying principles of intracellular molecular organization and
intercellular spatial interactions remains incomplete. A central challenge lies in developing
computational frameworks that can integrate molecular, cellular, and tissue-level data to advance cell
biology at an unprecedented scale. In this talk, I will present our recent work on machine learning
approaches for regulatory genomics, with a focus on single-cell 3D epigenomics. We introduce methods
that connect different layers of 3D genome architecture and cellular function at single-cell resolution,
including graph- and hypergraph-based models that capture spatial genome organization. I will also
highlight our latest efforts in developing self-supervised learning frameworks to delineate multiscale
cellular interactions within complex tissues, enabling the discovery of previously unrecognized spatially
organized patterns. Together, these Al-driven models provide a foundation for integrative, multiscale
representations of cellular systems, offering new insights into genome structure, gene regulation, and
cell-cell communication. This line of work opens new opportunities toward building cohesive multiscale
cellular models applicable across a broad range of contexts in health and disease.

Complex Disease Modeling And Efficient Drug Discovery With Large Language Models
2025-04-29 11:00 - 12:00
HSPH Biostatistics and DFCI Data Science Seminar
Tuesday April 29 from 11:00-12:00pm
Zoom only (Link to be posted shortly)
Yu Li, PhD
Assistant Professor, CSE
The Chinese University of Hong Kong
Large language models, which can integrate and process large amounts of data in biomedicine, have great potential in modeling complex diseases and discovering functional biomolecules for potential therapeutics. To model complex diseases and identify the potential drug targets for such diseases, we built a language model trained on the insurance claims of around 123 million US people. With the model, we can give a unified representation of all the common complex diseases, which enables us to predict the genetic parameters of the diseases and discover unique genetic loci related to them efficiently. Then, we developed models based on protein language models to efficiently discover remote homologs and functional biomolecules from nature, such as signal peptides and antimicrobial peptides. With the model, we can identify remote homologs 22 times faster than PSI-BLAST and discover diverse functional peptides with sequence similarity lower than 20% against the known ones. Finally, we developed an RNA language model to model the RNA sequence and structure relation, which enables us to perform RNA structure prediction and reverse design effectively. Within two months, we designed and experimentally validated 19 RNA aptamers that are structurally similar, yet sequence dissimilar, to known light-up aptamers. More importantly, 10 designed aptamers show higher fluorescence than the native Mango-I. The above projects demonstrate the great potential of large language models in promoting fundamental computational biological research and potential transformational development.

Preference Inference for Language Models Debiased by Fisher Random Walk Models
2025-09-11 16:00 - 17:00 | Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115
﻿HSPH Biostatistics & DFCI Data Science Colloquium Series
September 11 at 4:00PM
Harvard TH Chan School of Public Health, FXB-301
Junwei Lu, PhD
Associate Professor of Biostatistics, Harvard TH Chan School of Public Health
Human preference alignment has been shown to be effective in training the large language models (LMs). It allows the LLM to understand human feedback and preferences. Despite the extensive literature dealing with algorithms aligning the rank of human preference, uncertainty quantification for the ranking estimation still needs to be explored and is of great practical significance. For example, it is important to overcome the problem of hallucination for LLM in the medical domain, and an inferential method for the ranking of LM answers becomes necessary. In this talk, we will present a novel framework called “Fisher random walk” to conduct semi-parametric efficient preference inference for language models and illustrate its application in the language models for medical knowledge.

Reproducible Research - Tools and a case study with NHANES
2025-09-18 16:00 - 17:00 | Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115
HSPH Biostatistics & DFCI Data Science Colloquium Series
September 18, 2025
4:00 PM
HSPH FXB-301
Robert Gentleman, PhD
Principal Research Scientist
Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute
I will discuss how new technologies and statistical methodologies can help enhance our ability to perform reproducible research. I will demonstrate how these could be used in a real world setting by examining questions, primarily of an epidemiological nature, using data from the NHANES surveys. I will describe one version of an Environment Wide Association Study (EnWAS) and show how this methodology can potentially be employed to interrogate large complex data resources.

Navigate the Crossroad of Statistics, Generative AI and Genomic Health
2025-10-02 16:00 - 17:00
HSPH Biostatistics & DFCI Data Science Colloquium Series
Thursday October 2, 2025
4:00pm ET
HSPH FXB-301
Xihong Lin, PhD, Department of Biostatistics and Department of Statistics, Harvard University
Integrating statistics with generative Al provides unprecedent opportunities to empower statistical science and accelerate trustworthy scientific discovery by leveraging the potential of generative Al models alongside rigorous statistical principles that account for uncertainty and enhance interpretability. In this talk, I will discuss the challenges and opportunities as we navigate the crossroad of statistics, generative Al, and genomic health science. I will highlight how synthetic data from generative models, such as diffusion models and transformers, can be used to enable robust and powerful statistical analyses, while ensuring valid inference even when generative Al models are misspecified and treated as black-box tools. I will illustrate such synthetic data powered statistical inference with generative ML/Al through large scale analyses of the UK biobank in the presence of missing data, and discuss its connection with prediction powered inference (PPI). I will also discuss how to build an end-to-end autonomous, scalable and interpretable large-scale whole genome sequencing (WGS) analysis ecosystem. These efforts will be illustrated using the analysis of the TOPMed WGS samples of 200,000 samples, the UK biobank of 500,000 subjects on the cloud platform RAP and as well the All of Us data of 400,000 subjects in the NIH cloud platform AnVIL.

Flexible Adaptive Procedures for Testing Multiple Treatments, Endpoints or Populations in Confirmatory Clinical Trials
2025-10-09 16:00 - 17:00
HSPH Biostatistics & DFCI Data Science Colloquium Series
Thursday October 9, 2025
4:00pm ET
HSPH FXB-301
Cyrus Mehta, President and Co-Founder of Cytel, Inc, Adjunct Professor, Department of Biostatistics, Harvard TH Chan School of Public Health
The statistical methodology for the classical two-arm group sequential design has advanced vastly over the past three decades to incorporate, adaptive design changes, multiple treatments and multiple endpoints, while nevertheless preserving strong control of the family wise error rate. The graph based approach to multiple testing is an intuitive method that enables a clinical trial study team to represent clearly, through a directed graph, its priorities for hierarchical testing of multiple hypotheses, and for propagating the available type-1 error from rejected or dropped hypotheses to hypotheses yet to be tested. Although originally developed for single stage non-adaptive designs, we show how it may be extended to two-stage designs that permit early identification of efficacious treatments, adaptive sample size re-estimation, dropping of hypotheses, and changes in the hierarchical testing strategy at the end of stage one. We will present the statistical methodology for controlling the family wise error rate in the presence of these adaptive changes, and will generate the operating characteristics of different underlying scenarios and adaptive decision rules through a large simulation experiment.

Estimation and Inference of Two Doubly Robust Functionals in High Dimensions
2025-10-16 16:00 - 17:00
HSPH Biostatistics & DFCI Data Science Colloquium Series
Thursday October 16, 2025
4:00pm ET
HSPH FXB-301
Rajarshi Mukherjee, Associate Professor of Biostatistics, Harvard T.H. Chan School of Public Health
Website

Addressing Statistical Challenges in Long COVID Research: Auxiliary Variable-Dependent Sampling Designs and Clustering of Complex Data Types
2025-11-13 16:00 - 17:00
HSPH Biostatistics & DFCI Data Science Colloquium Seminar Series
November 13, 2025 at 4:00pm
HSPH, FXB 301
Speakers: Joint presentation by Tony Harrison & Thaweethai Reeder

The Single Arm Changing to Randomized Design (SACRED)
2025-11-20 16:00 - 17:00
HSPH Biostatistics & DFCI Data Science Colloquium Seminar Series
Harvard TH Chan School of Public Health, FXB 301
November 21st, 4:00-5:00pm
Glen Laird, Head of Biostatistics, Methodology and Innovation, Vertex Pharmaceuticals

Stay tuned for 2026 events!
2026-01-30 08:00 - 17:00
Please watch our Events page for the schedule of seminars and workshops starting in February 2026!

Data Integration and Time-informed Methods for the Electronic Health Record
2026-02-05 16:00 - 17:00
HSPH Biostatistics and DFCI Data Science Colloquium
Thursday February 5 at 4PM
HSPH, FXB 301
Speaker: Parker Knight, PhD Candidate, Harvard TH Chan School of Public Health
Seminar Website.

Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
2026-02-12 16:00 - 17:00
HSPH Biostatistics and DFCI Data Science Colloquium
HSPH, FXB 301
Speaker: Keith Barnatchez, Harvard TH Chan School of Public Health
https://hsph.harvard.edu/department/biostatistics/seminars-events/colloquium-seminar-series/

Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning
2026-02-19 16:00 - 17:00
HSPH Biostatistics and DFCI Data Science Colloquium
HSPH, FXB 301
Nathan Cheng, PhD Student, Harvard TH Chan School of Public Health
https://hsph.harvard.edu/department/biostatistics/seminars-events/colloquium-seminar-series/

Spectral Methods for Spatial and Multi-omics data
2026-02-26 16:00 - 17:00
HSPH Biostatistics and DFCI Data Science Colloquium
Thursday February 26 at 4:00pm
HSPH, FXB 301
Phillip Nicol, PhD Student, Harvard TH Chan School of Public Health
https://hsph.harvard.edu/department/biostatistics/seminars-events/colloquium-seminar-series/

Integrating Pre-Trained Language Models into Topic Modeling
2026-03-05 16:00 - 17:00
HSPH Biostatistics and DFCI Data Science Colloquium
Thursday March 5 at 4:00pm
HSPH, FXB 301
Tracy Ke, PhD, Associate Professor of Statistics, Harvard University
https://hsph.harvard.edu/department/biostatistics/seminars-events/colloquium-seminar-series/
