Past CCB Seminars

May 2024 Seminars

Speaker: Rong Ma (HSPH Professor)

Talk Title: Principled and interpretable alignability testing and integration of single-cell data

When: Monday, May 13, 3:00 PM ET

Recording: View Here

Abstract: Aligning and integrating different datasets is a key challenge in single-cell research. However, existing methods suffer from several fundamental and under-appreciated limitations. First, we do not have a rigorous statistical test for determining whether two single-cell datasets should even be integrated. Moreover, popular methods substantially distort the data during alignment, making the downstream analysis subject to bias and difficult to interpret. We address both challenges with a unified spectral manifold alignment and inference (SMAI) framework. SMAI is a flexible and interpretable method for aligning datasets with the same type of features, equipped with an alignability test justified by statistical theory. It preserves within-data structures and improves downstream analyses, such as identification of differentially expressed genes and imputation of spatial transcriptomics

Bio: Rong Ma is an Assistant Professor of Biostatistics at Harvard T.H. Chan School of Public Health. He received his PhD in biostatistics from the University of Pennsylvania and was a postdoctoral scholar in statistics at Stanford University. His current research focuses on statistical inference for large random matrices, spectral methods, manifold learning, and applications in biomedical sciences, especially in single-cell integrative genomics and multiomics. He was a recipient of the 2022 Lawrence D. Brown Ph.D. Student Award from the Institute of Mathematical Statistics.

April 2024 Seminars

Speaker: Glenn Ko, SEAS Professor, Co-Founder and CEO of Stochastic AI

When: Monday, April 15, 3:00 PM ET

Topic: From Harvard Lab to Global Enterprises: The Journey of Stochastic’s Personalized AI

RecordingView here

Description: Glenn’s founding story of Stochastic AI as a Harvard academic, followed by some technical details behind Stochastic AI.

Bio: Glenn Ko is an AI systems researcher and entrepreneur focusing on building AI systems that prioritize personalization, privacy, and real-time interaction. He is currently the Co-Founder and CEO of Stochastic, an AI company spun off of his postdoctoral research at Harvard University School of Engineering and Applied Sciences, where he still holds an adjunct research faculty position. Stochastic provides personalized AI agents for automation of decision making and task execution in the realm of enterprise knowledge work such as customer support and internal document processing. He received his PhD on probabilistic computing from UIUC and spent time at IBM Research, Qualcomm and Samsung Electronics where he worked on AI/ML research and contributed to the design of mobile processors for smartphones.

Speaker: Micaela Consens, PhD Candidate, Vector Institute and University of Toronto

When: Monday April 15, 3:00 PM ET

Topic: To Transformers and Beyond: Large Language Models for the Genome

RecordingView Here

Slides: View Here

In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling complex computational challenges. This review focuses on the transformative role of Large Language Models (LLMs), which are mostly based on the transformer architecture, in genomics. Building on the foundation of traditional convolutional neural networks and recurrent neural networks, we explore both the strengths and limitations of transformers and other LLMs for genomics. Additionally, we contemplate the future of genomic modeling beyond the transformer architecture based on current trends in research. The paper aims to serve as a guide for computational biologists and computer scientists interested in LLMs for genomic data. We hope the paper can also serve as an educational introduction and discussion for biologists to a fundamental shift in how we will be analyzing genomic data in the future.

March 2024 Seminars

Speaker: Mariano Gabitto, PhD

Recording: Youtube Link

Topic: Deep Generative Models for the Analysis of Large Scale Multimodal Single-Cell Data


When: Monday, March 18, 2024, 10 AM- 11 AM ET


Since their inception, single-cell technologies have revolutionized biology, providing exciting opportunities to characterize cellular populations and track their changes with disease. These technologies enable the analysis of the transcriptional (scRNA-seq), chromatin accessibility (scATAC-seq), and surface-protein expression (CITE-seq) landscape of individual cells. How can we effectively discover structure in this complex high-dimensional multimodal data and integrate it into a common representation to better understand disease progression? I will introduce MultiVI, a deep generative model designed for the analysis of single-cell chromatin, transcriptional and protein expression information. MultiVI co-learns informative low-dimensional representations for individual and joint modalities, accurately capturing multimodal properties of individual cells. This approach offers a principled method to analyze samples in which single and multiple genomic data modalities are present. Finally, I will demonstrate how this tool was used within the Seattle Alzheimer’s Disease Cell Atlas (SEA-AD) Consortium to map cellular types in healthy and diseased stated and study their changes associated with Alzheimer’s Disease.

Mariano Gabitto is an Assistant Investigator at the Allen Institute for Brain Science. Mariano’s research focuses on applying statistical and machine learning methods to decipher the cellular and molecular programs giving rise to the cellular diversity observed in the brain and how cell types degenerate during neurological diseases. Before joining Allen, he was a research scientist at the Center for Computational Biology at the Flatiron Institute, Simons Foundation and at the Broad Institute of Harvard and MIT. Mariano was a visiting scientist at Mike Jordan’s Group at U.C. Berkeley, developing deep generative models to represent single-cell information. He completed his Ph.D. in Neuroscience at Columbia University in the laboratory of Charles Zuker where he established collaborations with the groups of Liam Paninski, Larry Abbott

January 2024 Seminars

Speaker: Shawn Murphy, MD, PhD

Topic: Instrumenting the Healthcare Enterprise for Discovery Research.

When: Monday, January 22, 2024, 10 AM- 11 AM ET

December 2023 Seminars

Speaker: Dr. Martin Hemberg

Topic: Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis

When: Monday, December 4, 2023, 10 AM- 11 AM ET

Abstract: In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cellcell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligandreceptor gene expression. Our results uncovered a marked change in ligandreceptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

Recording: Click Here

Speaker: Dr. Beth Cimini

Topic: Using high content analysis and deep learning to make the most of your microscopy

When: Monday, December 11, 2023, 10 AM – 11 AM ET

Where: Gordon Hall, 106 Waterhouse Conference RoomTime (Harvard ID required)

Abstract: In the age of the digital camera, microscopy images constitute a fantastically rich source of quantiative data. Yet, it currently remains difficult for most scientists to mine quantitative data from these images easily such that they can answer their important biological questions. In this talk, we will discuss open source tools that make quantitative image analysis both easier and more reproducible, as well as bioinformatic approaches allowing users to extract novel connections from their data.

Recording: Click Here

November 2023 Seminars

Speaker: Ricard Argelaguet, PhD

Topic: Principles and challenges in single-cell data integration

When: Monday, November 6, 2023, 3 PM- 4 PM ET

Abstract: The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for integrating data across different modalities. In the first part of the talk I will present an overview of principles and challenges in single-cell data integration. In the second part I will present a study on how to integrate gene expression and chromatin accessibility to predict transcription factor binding sites, infer gene regulatory networks and generate in silico predictions of transcription factor perturbations.

Recoding: Click Here

Speakers: Ismael Rodriguez and Piotr Suwara

Topic: A novel approach for identifying protein crystals using machine learning: a leap in precision and efficiency.

When: Monday, November 13, 2023, 10 AM- 11 AM ET

Abstract: Protein crystallization is still crucial in understanding the 3D structure of macromolecules such as proteins or antibodies. This step is essential in drug discovery and development, and often its main bottleneck. It is a time-consuming process which  requires conducting multiple experiments  at various conditions. Because of this, correctly identifying crystals is crucial for drug discovery. In this talk, we present the recently published crystal structure of pAC65, a macrocyclic peptide that blocks the PD-1/PD-L1 pathway with comparable potency to FDA-Approved antibodies. Additionally, we introduce a novel machine learning model that is able to identify protein crystals with high recall (missing <5% of crystals) while retaining high precision.

Recording: Click Here

October 2023 Seminars

Speaker: Dr. Gabriel Brat

Topic:Collaborative Informatics in Surgery: Infusing Algorithms with Surgeon Knowledge

When: Monday, October 2, 2023, 10 AM- 11 AM ET

Where: Countway Library 5th floor, Lahey Room 518

Abstract: Applications of AI and algorithms generally focus on replacing or facilitating human effort. For complex decision-making tasks, like surgical judgement and performance, there is an additional opportunity to build collaborative systems that explicitly integrate human and algorithmic capabilities. I will speak about my lab’s efforts to design and validate these hybrid systems and how such models can be used to facilitate implementation and quality. The value of these systems will be contextualized with the risks and strengths of Integrating human perception into algorithms.

Recording: Click here

Speaker: Charles Reilly, Ph.D.

Topic: Broad-spectrum Antiviral and Biostasis Therapeutics Enabled by Computational Design and Discovery

When: Monday, October 16, 2023, 10 AM- 11 AM ET

Where: Countway Library 5th floor, Lahey Room 518

Abstract: Broad-spectrum Antiviral and Biostasis Therapeutics are key programs under the Wyss Institute’s Computational Design and Discovery initiative. This initiative merges experimental data with in silico modeling, using predictive analytics, machine learning, and multiscale simulations. The Antiviral program harnesses simulation and machine learning to develop antiviral therapeutics for pandemic preparedness by targeting conserved viral protein changes essential for host cell entry. The Biostasis program focuses on discovering molecules to slow all biological activity, prolonging the window for emergency medical intervention after severe injuries or infections. These efforts extend beyond medicine and are being developed to minimize cold storage requirements and reduce food spoilage. This program also reveals innovative therapeutic strategies and insights into bioactive phytonutrients.

Recording: NA

Speaker: Luca Marconato, PhD student, EMBL Heidelberg, Germany

Topic: SpatialData: an open and universal data framework for spatial omics

When: Monday, October 23, 2023, 10 AM- 11 AM ET


The growing abundance of spatial profiling methods comes with a heterogeneity of file formats which creates barriers in spatial omics data analysis. Tasks such as data loading, processing, and visualization require laborious, ad-hoc manipulations and conversions. This, in turn, limits the ability to repurpose existing computational methods for new spatial technologies. Furthermore, the recent trends toward spatial multimodal profiling increase the complexity of file formats and analyses. To address these problems, we established a collaboration that unites research scientists from the single-cell (scverse) and imaging communities (OME, napari). Together, we developed an open, language-agnostic, cloud-ready file storage format that extends the OME-NGFF specification and is tailored for a flexible and efficient storage of spatial molecular data. We also developed a Python package, SpatialData, which provides modular, in-memory representations of the data, drawing on well-established libraries from the Python geospatial and single-cell ecosystems (Xarray, GeoPandas, AnnData, Dask). We also implemented general data manipulation operations, like coordinate transformations and spatial queries, abstracted to work interchangeably for general multi-modality spatial omics datasets. Finally, we developed a Napari plugin that enables the interactive visualization and annotation of spatial multi-omics datasets. We believe that our computational solutions for data storage, manipulation, and visualization have the potential to greatly reduce the technical overhead when dealing with spatial multi-modal datasets, and allow for easier cross-talk and repurposing of computational methods across programming languages.

Recording: Click Here

Speaker: Ayush Noori

Topic: Contextual deep learning on knowledge graphs for drug repurposing and precision medicine in neurological disorders

When: Monday, October 30, 2023, 3 PM – 4 PM ET

Where: Countway Library 5th floor, Lahey Room 518 (Harvard ID required)


Neurological disorders are the leading global cause of disability, yet new drugs are often developed by focusing on only a small handful of disease targets. Traditional therapeutics discovery strategies have been hampered by disease complexity and heterogeneity. To address these challenges, we developed CIPHER, a multimodal graph-language approach for contextually informed precision healthcare in neurological disease. We seek to understand network disruption in neurological disorders, predict drug repurposing opportunities, and address disease heterogeneity by enabling personalized decision-making for individual patients. CIPHER successfully predicts drug-disease indications and was validated on recent FDA repurposing approvals.

Recording: NA

September 2023 Seminar

Speaker: Anthony-Alexander Christidis

Topic: A New Ensemble Learning Framework for Modeling High-Dimensional Data

When: Monday, September 11, 2023

Abstract: Ensemble methods have gained a lot of popularity in computational statistics and machine learning due to their flexibility and generally superior predictive performance compared to single-model methods. However, state-of-the-art ensemble methods rely on randomization and other forms of heuristics to generate diverse base learners, and are thus considered uninterpretable backbox algorithms. In this talk, we will introduce an entirely new framework for ensemble learning, free of randomization and heuristics. The models in these ensembles are learned by optimizing by a global objective function that generates diverse base learners while maintaining a degree of interpretability. Some aspects related to robustness will be discussed.

Recording: Click Here

June 2023 Seminars

Speaker: Jonathan Foster, PhD, Chief Technical Officer, Glue solutions Inc

Topic: Glue Genes: Exploratory Data Analysis for Single Cell and Spatial Omics

When: Monday, June 26, 2023

Recording: Click here

Abstract: Glue genes is a new open source tool bringing the power of the glue visualization library to analysis of single-cell and spatial omics data. The core features of glue genes are (1) flexible linking of diverse, multi-dimensional datasets, (2) automatic connections across multiple visualizations, and (3) easy extensibility and integration with other open source Python tools. I will talk about the philosophy and design of glue genes, and show examples of how it can be used to explore linked datasets in real time, showcasing the desktop application as well as the in-development “glupyter” project, which puts the power of glue inside Jupyter Lab. glue genes is developed in partnership with the Jackson Laboratory.

Software/Project Page


Speaker:  Graham Heimberg, PhD, Principal Scientist, Genentech

Topic: Scalable cell search of human cell atlases via deep learning reveals commonalities across disease associated macrophages

When: Monday, June 5, 2023

Recording: Click here

Single-cell RNA-seq (scRNA-seq) studies have profiled over 100 million human cells across diseases, developmental stages, and perturbations to date. A singular view of this vast and growing expression landscape could help reveal novel associations between cell states and diseases, discover cell states in unexpected tissue contexts, and relate in vivo cells to in vitro models. However, these require a common, scalable representation of expression profiles, a general measure of their similarity, and an efficient way to query these data. Here, we present SCimilarity, a metric learning framework to learn and search a unified and interpretable representation that annotates cell types and instantaneously queries for a cell state across tens of millions of profiles. We demonstrate SCimilarity on a 22.7 million cell corpus assembled across 399 published scRNA-seq studies, showing accurate integration, annotation and querying. We experimentally validated SCimilarity by querying across tissues for a macrophage subset originally identified in interstitial lung disease, and showing that cells with similar profiles are found in other fibrotic diseases, tissues, and a 3D hydrogel system, which we then leveraged to yield this cell state in vitro. SCimilarity enables researchers to query for similar cellular states across the entire human body, providing a powerful tool for generating novel biological insights from the growing Human Cell Atlas.

May 2023 Seminars

SPECIAL: The Center for Computational Biomedicine (CCB) hosted a joint seminar series with the Image Analysis Collaboratory (IAC) at Harvard Medical School with a focus on best practices and leading tools for quantitative analysis of biomedical images.

Speaker:  Talley Lambert, PhD, Associate Director of Imaging Technology @ Nikon Imaging Center, Harvard Medical School; Core Developer, Napari

Topic: Napari: A multi-dimensional data viewer for Python

When: Monday, May 1, 2023

Recording: Click here

With the advent of new multiplexed tissue imaging technologies, there is a growing need for efficient tools for browsing, annotating, and analyzing large multi-dimensional images. In this seminar, Talley will discuss Napari, an open source, interactive, multi-dimensional data viewer for Python.

Speakers: Hector Corrada Bravo, PhD; and Jayaram Kancherla

Topic: The SingleCellHub at Genentech: how to manage and query data from 130M cells

When: Monday, April 3, 3:00 – 4:00 PM Eastern Time (United States and Canada)

Recording: Click here

Speakers: Marinka Zitnik and Michelle M. Li

Topic: Foundational AI for Genomic Medicine and Therapeutic Design

When : Monday, May 22, 3:00 – 4:00 PM Eastern Time (United States and Canada)

Recording: Click here

April 2023 Seminars

Speaker:  Aaron lun, Scientist at Genentech

Topic: Code, sweat, and tears: how the OSCA sausage was made

When: Monday, April 3, 3:00 – 4:00 PM Eastern Time (United States and Canada)

Recording: Click here

Speaker: Matthew McDermott, Ph.D.

Topic: Generalizable and Performant Software for Producing Medical Record Foundation Models

When: Apr 17, 2023 09:50 AM Eastern Time (US and Canada)

Recording: Click here

March 2023 Seminars

Speaker: Isabella Grabski, PhD Candidate in Biostatistics, Department of Biostatistics, Harvard School of Public Health

 Topic: Probabilistic approaches to cell type annotation and clustering in single-cell RNA-seq data

Recording: Click here

Speaker: Sebastian Lobentanzer, Postdoc at the Institute for Computational Biomedicine, Heidelberg University Hospital

Topic: Democratising Knowledge Representation with BioCypher

Relevant links:  arXiv,  Biocypher

Recording: Click here


Speaker: Dr. Mine Çetinkaya-Rundel, Director of Undergraduate Studies, Professor of the Practice, Department of Statistical Science, Duke University

Topic: Hello, Quarto: A World of Possibilities (for Reproducible Publishing)

Recording: Click here

February 2023 Seminars

Speaker: Brett Beaulieu-Jones, PhD, Assistant Professor, University of Chicago

Topic:  “Limits of Machine Learning Models Tied to Clinical Intuition: Implications for AI-based Clinical Decision Support”

Relevant link: Click here

Recording: pending approval

Speaker:  Kun-Hsing Yu, MD, PhD, Assistant Professor, DBMI HMS

Topic: Artificial Intelligence-Enabled Cancer Pathology Evaluation

Recording: Not available

Speaker: Bethany Hedt-Gauthier, PhD, Associate Professor of Global Health and Social MedicineAssociate Professor in the Department of Biostatistics, Harvard T.H. Chan School of Public Health, HMS and Siona Prasad, Undergraduate Researcher at Harvard College Department of Computer Science

Topic: Image-based algorithms for remote surgical site infection diagnoses in rural Rwanda

Recording: Not available

January 2023 Seminars

Speaker: Katharina Imkeller, PhD, Group leader Computational Immunology, University Hospital Frankfurt, Germany

Topic: Unraveling Immunogenomic Diversity in Single-Cell Data

Recording: Not available


Speaker:  Trevor Manz, PhD Candidate & NSF Research Fellow, DBMI HMS

 Topic: Interactive, web-based visualization of high-resolution multiplexed bioimaging data

Abstract:  Although the rapid innovation of biological imaging brings significant scientific value, the proliferation of technologies without unification of interoperable standards has created challenges that limit the analysis and sharing of results. In this talk, I will discuss our work on 1.) community-driven design and adoption of open-standard next-generation file formats and 2.) the open-source Viv bioimaging visualization library which supports OME-TIFF and OME-NGFF directly in the web browser. Viv addresses a critical limitation of most web-based bioimaging viewers by removing a dependency on server-side rendering, offering a flexible toolkit for browsing multi-terabyte datasets on both mobile and desktop devices—without software installation.

Recording: Click here

December 2022 Seminars

Speaker: Nicolas Matentzoglu, PhD, Independent Contractor, Semantic Web and Knowledge Graphs expert

 Topic: Open SSSOM – Unlocking the wealth of biomedical data using shared standardized entity mappings

Recording: Click here

November 2022 Seminars

Speaker: Dr. Matthew Crowson, MD MPA MASc FRCSCAssistant Professor, Investigator, Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear

 Topic: Computer Vision for Irritable Toddlers: Improving Diagnostic Accuracy for Ear Infections

Recording: Not available

Speaker:  Faisal Mahmood, PhD, Assistant Professor, Pathology, Harvard Medical School

Affiliation: Division of Computational Pathology, Brigham and Women’s Hospital; Data Science Program, Dana-Farber/Harvard Cancer Center; Cancer Program, Broad Institute of Harvard and MIT

 Topic: Data-efficient and multimodal computational pathology

Abstract Advances in digital pathology and artificial intelligence have presented the potential to build assistive tools for objective diagnosis, prognosis and therapeutic-response and resistance prediction. In this talk we will discuss our work on: 1) Data-efficient methods for weakly-supervised whole slide classification with examples in cancer diagnosis and subtyping (Nature BME, 2021), and allograft rejection (Nature Medicine, 2022) 2) Harnessing weakly-supervised, fast and data-efficient WSI classification for identifying origins for cancers of unknown primary (Nature, 2021). 3) Discovering integrative histology-genomic prognostic markers via interpretable multimodal deep learning (IEEE TMI, 2020; ICCV, 2021; Cancer Cell, 2022). 4) Self-supervised deep learning for pathology and image retrieval (CVPR, 2022; Nature BME, 2022). 5) Deploying weakly supervised models in low resource settings without slide scanners, network connections, computational resources, and expensive microscopes. 6) Bias and fairness in computational pathology algorithms.

Relevant links: 
Lu et al. Nat BME, 2021 Data-efficient and weakly supervised computational pathology on whole-slide images

Ming et al. Nature, 2021 AI-based pathology predicts origins for cancers of unknown primary
Lipkova et al. Nature Medicine, 2022 Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies

Chen et al. Cancer Cell, 2022 Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Recording: Not available

Speaker: Jeffrey Moffitt, PhD, Assistant Professor, Department of Microbiology, HMS

Topic: Building tissues atlases with MERFISH: The new opportunities and challenges of image-based single-cell transcriptomics

Recording: Not available

October 2022 Seminars

Speaker: Shila Ghazanfar, PhD, ARC Discovery Early Career Researcher, The University of Sydney, Faculty of Science, School of Mathematics and Statistics

Topic: StabMap: Mosaic single cell data integration using non-overlapping features

Recording: Click here

Speaker: Simon Norrelykke, Ph.D., Director of Image Analysis, Department of Systems Biology, HMS

Topic: Emerging Tools and Technologies in Bioimage Analysis and Processing

Recording: Not available

Speaker: Benjamin M. Gyori, Ph.D., Research Fellow, Director of Machine-assisted Modeling & Analysis, Laboratory of Systems Pharmacology, HMS

 Topic: Large-scale biomedical knowledge assembly for hypothesis generation and human-machine interaction

Recording: Not available

Speaker: Jeremy Manning, PhD, Assistant Professor of Psychological & Brain Sciences, Dartmouth College

 Topic: Geometric approaches to understanding the neural basis of human thought

Recording: Not available

September 2022 Seminars

Speaker: Antonios Lioutas, Ph.D., Research Associate in Genetics, Harvard Medical School

Topic: Super-resolution imaging of the human genome

Recording: Not available

Speaker(s): Laurent Gatto, PhD, Associate Professor of Bioinformatics, UCLouvain, Belgium and Charlotte Soneson, PhD, Research Associate, FMI, Switzerland

 Topic: The Bioconductor Education Committee and Its Initiatives

Related link: Bioconductor Education and Training

Recording: Not available

June 2022 Seminars

Speaker: Dan Triviglia, Director, DBMI IT Core, HMS

Title: Cloud Computing at HMS-DBMI

Recording: Click here

Speaker: Catia Pesquita, PhD,  Assistant Professor at LASIGE, Faculty of Sciences University of Lisbon and Vice President of

Title: Biomedical Semantic Similarity for Humans and Machines

Recording: Click here

Speaker: William “Gaby” Rodriguez, PhD, Computational Research Consultant & Amir Karger, PhD, Associate Director of Research Computing 

Title: The New Web Portal for HMS’ O2 Cluster: Simplifying the Experience of High Performance Computing with Open OnDemand

Recording: Click here

May 2022 Seminars

Speaker: Katy Börner, PhD, Victor H. Yngve Distinguished Professor of Intelligent Systems Engineering, Indiana University

Title: Human Reference Atlas Construction and Usage

Recording: Click here

Speaker: Robert Hoehndorf, PhD, King Abdullah University of Science and Technology

Title: Semantic Similarity and Machine Learning with Ontologies

Recording: Click here

Speaker: Jaclyn Mallard, PhD, CCB Center Administrator

Title: Filling in the Gaps: CCB’s Role in Quantitative Education at HMS

Recording: Click here

April 2022 Seminars

Speaker: Fabian Theis, PhD, Institute Director andResearch Group Leaderand Giovanni Palla, PhD Candidate at Helmholtz Munich

Title: Squidpy: A Scalable Framework for Spatial Omics Analysis

Recording: Click here

Speaker: Vincent Carey, PhD, Professor of Medicine at HMS and Associate Biostatistician at Channing Laboratory, Brigham And Women’s Hospital

Title: Open Computational eCosystems for Modern Genomics

Recording: Click here

Speaker: Artem Sokolov, PhD, HMS DBMI

Title: A Scalable, Modular Image-processing Pipeline for Multiplexed Tissue Imaging

Recording: Click here

Speaker: Constantin Ahlmann-Eltze, PhD student at the EMBL Heidelberg in the Huber group, European Molecular Biology Laboratory, Heidelberg, Germany

Title: Transformation and Preprocessing of Single-Cell RNA-Seq Data

Recording: Click here

March 2022 Seminars

Speaker: Nitesh Turaga, PhD, Scientist in the Department of Data Science, at the Dana Farber Cancer Institute of Harvard Medical School and a Core Team member at the Bioconductor Project, working at the intersection of Data Science, Computational biology, and Software Engineering

Title: Containerized and Parallel Computation in R/Bioconductor

Recording: N/A

Speaker: Paul Macklin, PhD, Associate Professor and Director of Undergraduate Studies in the Department of Intelligent Systems Engineering at Indiana University

Title: The Human Body at Cellular Resolution: the NIH Human Biomolecular Atlas Program

Recording: Click here

Speaker: Nils Eling, PhD, Postdoctoral Researcher at the Bodenmiller Laboratory at the University of Zurich

Title: Visualization and Analysis of Highly Multiplexed Imaging Data

Recording: Click here


February 2022 Seminars

Speaker: Sean Maden, Ph.D candidate in Computational Biology. Biomedical Engineering Dept (OHSU)

Title: Human Methylome Variation Across Infinium 450K Data on the Gene Expression Omnibus

Recording: N/A


Speaker: Marinka Zitnik, PhD, Assistant Professor of Biomedical Informatics, Harvard Medical School

Title: Leveraging the Cell Ontology to Classify Unseen Cell Types

Recording: Click here


Speaker: Levi Waldron, PhD, Associate Professor of Biostatistics at CUNY Graduate School of Public Health and Health Policy

Title: Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

Recording: Click Here

January 2022 Seminars

Speaker(s): Allon Klein, PhD, Assistant Professor of Systems Biology at HMS and Sahand Hormoz, PhD, Assistant Professor of Systems Biology at HMS

Title: Atlas of Human Population Variation in Bone Marrow Hematopoiesis

Recording: N/A

Date: January 31st, 2022

Speaker(s): Helena Crowell, PhD Candidate at University of Zurich and Dario Righelli, Assistant Researcher at Department of Statistics,University of Padua 

Title: Orchestrating Spatially Resolved Transcriptomics Analysis with Bioconductor

Recording:  Click here