Dana-Farber Cancer Institute

Training Session: Efficient Phase I Clinical Trial Design
2021-05-19 10:00 - 12:00 (America/New_York)
May 19, 2021
10:00am-12:00PM Eastern Time
Fangxin Hong, PhD
Senior Research Scientist
Department of Data Science, Dana-Farber Cancer Institute
Department of Biostatistics, Harvard T.H. Chan School of Public Health

Frontiers in Biostatistics: Single-Cell RNA-Seq Data Analysis Via a Regularized Zero-Inflated Mixture Model Framework
2021-05-11 13:00 - 14:00
Frontiers in Biostatistics Seminar
May 11, 2021
1:00PM
Jianhua Hu, PhD
Professor, Biostatistics (in Medicine and in the Herbert Irving Comprehensive Cancer Center)
Director, Cancer Biostatistics Program
Columbia University
Register at: http://bit.ly/FIBMay21
Abstract: Applications of single-cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease heterogeneity at the cellular level. However, unique characteristics of scRNA-seq data, including large dimensionality, high dropout rates, and possibly batch effects, bring great difficulty into the analysis of such data. Not appropriately addressing these issues obstructs true scientific discovery. Herein, we propose a unified Regularized Zero-inflated Mixture Model framework designed for scRNA-seq data (RZiMM-scRNA) to simultaneously detect cell subgroups and identify gene differential expression based on a developed importance score, accounting for both dropouts and batch effects. We conduct extensive empirical investigation to demonstrate the promise of RZiMM-scRNA in comparison to several popular methods, including K-means and Hierarchical clustering.
This seminar was not recorded as the research presented has not been published yet.

Data Science Zoominar: The Importance of Representative Samples in Clinical Trials
2021-03-23 13:00 - 14:00
Tuesday March 23, 2021
1:00PM Eastern Time
A conversation with Timothy Rebbeck
Vincent L. Gregory, Jr. Professor of Cancer Prevention, Epidemiology, Harvard T.H. Chan School Of Public Health
Professor, Medical Oncology, Dana-Farber Cancer Institute
Moderator: Rafael Irizarry
YouTube Link: https://www.youtube.com/watch?v=Q4uBibxGf20

Frontiers in Biostatistics: Distributed Statistical Learning and Inference in EHR and Other Healthcare Datasets
2021-03-09 13:00 - 14:00
Frontiers in Biostatistics Seminar
March 9, 2021
1:00PM
Rui Duan, PhD
Assistant Professor of Biostatistics
Harvard TH Chan School of Public Health
Distributed Statistical Learning and Inference in EHR and Other Healthcare Datasets
Abstract: The growth of availability and variety of healthcare data sources has provided unique opportunities for data integration and evidence synthesis, which can potentially accelerate knowledge discovery and enable better clinical decision making. However, many practical and technical challenges, such as data privacy, high-dimensionality and heterogeneity across different datasets, remain to be addressed. In this talk, I will introduce several methods for effective and efficient integration of electronic health records and other healthcare datasets. Specifically, we develop communication-efficient distributed algorithms for jointly analyzing multiple datasets without the need of sharing patient-level data. Our algorithms are able to account for heterogeneity across different datasets. We provide theoretical guarantees for the performance of our algorithms, and examples of implementing the algorithms to real-world clinical research networks.
YouTube Link: https://www.youtube.com/watch?v=IscQ3ruxl1o

Data Science Zoominar: Diversity and Ethics in Genomics
2021-02-23 13:00 - 14:00
Tuesday February 23, 2021
1:00PM Eastern Time
A conversation with Keolu Fox, PhD
Assistant Professor, Department of Anthropology, University of California, San Diego
Moderator: Aedin Culhane
YouTube Link: https://www.youtube.com/watch?v=VANlStOnFPY

Frontiers in Biostatistics: Group Sequential Design Assuming Delayed Benefit
2021-02-09 13:00 - 14:00
February 9, 2021
1:00PM ET
Keaven Anderson, PhD
Scientific AVP, Methodology Research, Biostatistics at Merck
Group Sequential Design Assuming Delayed Benefit
Abstract: We consider an asymptotic approach to design of group sequential trials with a potentially delayed effects. Logrank, weighted logrank tests and combination tests are of primary interest, but we also consider restricted mean survival. The asymptotic approach allows both quick derivation of study design properties that are also easily verified using simulation. We rely heavily on work done by Tsiatis, 1981 published while he was at the HSPH. The impact of a potential delay in treatment effect on timing of analyses, study boundaries and sample size are demonstrated. The value of a robust design that is well powered under a variety of assumptions is emphasized. Open source software is provided for implementation. Given the current regulatory climate, logrank testing may still be preferred for these trials, but we hope that efficiencies and Type I error control associated with alternatives may make other options more acceptable for future consideration.
YouTube Link: https://www.youtube.com/watch?v=DhwZOX5uMKU

Data Science Zoominar: Vaccine Prioritization Strategies
2021-01-26 13:00 - 14:00
Tuesday January 26, 2021
1:00PM ET
A conversation with Daniel Larremore
Assistant Professor, Department of Computer Science at University of Colorado-Boulder and BioFrontiers Institute
and
Kate Bubar
Student, Department of Applied Mathematics, University of Colorado-Boulder
Moderator: Rafael Irizarry
RSVP at https://bit.ly/DSJan26
YouTube Link: https://www.youtube.com/watch?v=fJuHNNP8TLg

Frontiers in Biostatistics: Statistical Modeling and Adjustment for Sampling Biases
2021-01-12 13:00 - 14:00
Frontiers in Biostatistics Seminar
January 12, 2021
1:00PM
Jing Ning, PhD
Associate Professor. Department of Biostatistics
Division of Quantitative Sciences
The University of Texas M.D. Anderson Cancer Center
Register at: https://bit.ly/FIBJan12
Abstract: Bias sampling mechanisms are commonly encountered in applications where the subjects in a target population are not given an equal chance to be selected, either accidentally, by natural circumstances, or intentionally by design. Statistical methods not properly accounting for such a challenge often lead to invalid inferences. For example, evidence combined from published studies may lead to overly optimistic conclusions due to publication bias, and the well-known length bias can cause the screening to appear to be more successful than it really is. In this talk, I will present our recent work to adjust the sampling biases in diverse applications such as the survivorship bias in prevalent cohort, the self-reporting bias in longitudinal analysis and the publication bias in meta-analysis.

Data Science Zoominar: Understanding COVID-19 Vaccine Trial Designs and Current Results
2020-12-21 14:00 - 15:00
Monday December 21, 2020
2:00PM ET
Understanding COVID-19 Vaccine Trial Designs and Current Results
A conversation with David Benkeser
Assistant Professor, Biostatistics and Bioinformatics
Rollins School of Public Health, Emory University
Moderator: Rafael Irizarry
RSVP at https://bit.ly/DSDec21

Data Science Zoominar: Data-Driven Policy in Puerto Rico
2020-12-15 13:00 - 14:00
Tuesday December 15, 2020
1:00PM ET
Data-Driven Policy in Puerto Rico
A conversation with Arnaldo Cruz
Director of Research and Policy of the Financial
Oversight and Management Board of Puerto Rico
Co-founder of ABRE Puerto Rico
Moderator: Rafael Irizarry
RSVP at https://bit.ly/DSDec15

DF/HCC Cancer Data Science Program &Harvard Chan Bioinformatics CoreJoint Symposium on scRNAseq Methodology
2020-12-14 15:00 - 16:30
Monday, December 14, 3:00-4:30 PM ET 
RSVP https://bit.ly/CDSBioDec14
Speakers:

Aedin Culhane, Senior Research Scientist, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
Isabella Grabski, PhD Student in Biostatistics, Harvard University
Probabilistic gene barcodes identify cell-types in single-cell RNA-sequencing data
Shannan Ho Sui, Senior Research Scientist, Harvard T.H. Chan School of Public Health
Radhika S Khetani, Research Scientist, Harvard T.H. Chan School of Public Health
Peter Kharchenko, Associate Professor of Biomedical Informatics, Harvard Medical School
X. Shirley Liu, Professor, Harvard T.H. Chan School of Public Health
Analysis of Single-Cell Data to Model Tumor and Immune Gene Regulation
Luca Pinello, Associate Professor of Pathology, Harvard Medical School and Mass General Cancer Center
Trajectory inference and visualization of single cell data

Data Science Zoominar: Data Science Books, Online Courses, Podcasts, and Blogs
2020-12-08 13:00 - 14:00
Tuesday December 8, 2020
1:00PM ET
Data Science Books, Online Courses, Podcasts, and Blogs
A conversation with
Roger Peng, PhD
Johns Hopkins Bloomberg School of Public Health
Moderator: Rafael Irizarry
RSVP at https://bit.ly/DSDec8

Postdoctoral Open House
2020-12-07 13:00 - 14:30
The Dana-Farber Cancer Institute Department of Data Science announces our first annual Postdoc Open House. Join us on Monday, December 7th at 1:00PM EST to explore postdoctoral opportunities in cancer research. Participating faculty include:

Rafael Irizarry, PhD, Professor and Department Chair
X. Shirley Liu, PhD, Professor
Giovanni Parmigiani, PhD, Professor
Franziska Michor, PhD, Professor
Lorenzo Trippa, PhD, Associate Professor
Heng Li, PhD, Assistant Professor
Sahand Hormoz, PhD, Assistant Professor
Nabihah Tayob, PhD, Member of the Faculty

You will also meet current and former department postdocs, and learn about the resources available at Dana-Farber and throughout the Boston area.
Due to limited space, we request that participants are actively seeking a postdoctoral position. Please complete this form to request event admission.
We look forward to welcoming you and introducing our department!
Open postdoctoral positions are posted on our website.

Data Science Zoominar: Communicating Statistical Findings Effectively
2020-11-17 13:00 - 14:00
Tuesday November 17, 2020
1:00PM ET
Communicating Statistical Findings Effectively
A conversation with
Professor Sir David Spiegelhalter
Chair, Winton Centre for Risk and Evidence Communication
Centre for Mathematical Sciences
Author: The Art of Statistics
Moderator: Rafael Irizarry
RSVP at https://bit.ly/DSNov17

Data Science Zoominar: Election Forecasting: How We Succeeded Brilliantly, Failed Miserably, or Landed Somewhere in Between
2020-11-10 13:00 - 14:00
Tuesday November 10, 2020
1:00PM ET
Election Forecasting: How We Succeeded Brilliantly, Failed Miserably, or Landed Somewhere in Between
A conversation with
Andrew Gelman, Ph.D.
Professor of Statistics and Political Science
Columbia University
Moderator: Rafael Irizarry
RSVP at https://bit.ly/DSNov10

Data Science Zoominar: Data Science and Academic Leadership
2020-10-20 13:00 - 14:00
Tuesday October 20, 2020
1:00PM ET
Data Science and Academic Leadership
A conversation with F. DuBois Bowman, PhD
Dean of the University of Michigan School of Public Health
Moderator: Rafael Irizarry
Video available on our YouTube Channel.

Computational Biology of DNA Repair in Cancer
2020-10-15 13:00 - 14:00
Data Science Seminar
October 15, 2020
1:00PM ET
Dominik Glodzik, PhD
Repare Therapeutics
Zoom link: https://bit.ly/DSOct15
Abstract: Whole genome sequences contain within them signatures of mutational processes. In particular, some of the mutation signatures relate to impaired DNA-repair in cancer cells. Accurate measurement of mutation signatures reveals the role of DNA-repair deficiencies in etiology and progression of cancer.
We extended the computational methods for analysis of mutation signatures in order to describe patterns of chromosomal rearrangements. In particular, the rearrangement signatures enable the assessment of proficiency of homologous recombination (HR). HRDetect, an algorithm we developed, predicts probability of HR-deficiency, and is based on holistic portrayal of mutational signatures across different classes of somatic mutations. Around 20% of breast cancers contain signatures of HR-deficiency, and this group is wider than the group of carriers of BRCA1/2 mutations. By contrast to adult cancers, pediatric cancers with known DNA-repair defects display variation of mutational signatures, hinting at tissue-specificity of mutational signatures. Finally, in the chromosomally unstable cancers, we identified structural rearrangements, in coding and non-coding regions, that can act as cancer drivers. Altogether, these results indicate that computational assessment of DNA-repair capacity of tumor cells is now possible. The methods will be crucial to understanding of the DNA-repair mechanisms and tissue-specificity of mutational processes.
Bio: Dominik Glodzik received his PhD in Computational Biology from the University of Edinburgh, and held a postdoctoral position at Wellcome Trust Sanger Institute, before moving to a staff scientist position at Memorial Sloan Kettering Cancer Center. Currently he is a Principal Bioinformatician at Repare Therapeutics in Cambridge.

Constructing Confidence Interval for RMST under Group Sequential Setting
2020-10-13 13:00 - 14:00
Frontiers in Biostatistics Seminar
October 13, 2020
1:00PM
Lu Tian, PhD
Associate Professor of Biomedical Data Science in the School of Medicine
Stanford University
It is appealing to compared survival distributions based on restricted mean survival time (RMST), since it generates a clinically interpretable summary of the treatment effect, which can be estimated nonparametrically without assuming restrictive model assumptions such as the proportional hazards assumption. However, there are special challenges in designing and analyzing group sequential study based on RMST, because the truncation timepoint of the RMST in the interim analysis often differs from that in the final analysis. A valid test controls the unconditional type one error has been developed in the past. However, there is no appropriate statistical procedure for constructing the confidence interval for the treatment effect measured by a contrast in RMST, while it is crucial for informative clinical decision making. In this talk, I will review some important design issues for study based on RMST. I will then discuss how to conduct hypothesis testing and how to construct confidence intervals for the difference RMST in a group sequential setting. Examples and numerical studies will be presented to illustrate the method.
A recording of this seminar is available on your YouTube Channel.

Marvin Zelen Symposium 2020
2020-09-30 13:00 - 16:30
In December of 2019, we planned the next Marvin Zelen Symposium for March of 2020. At that point, the year ahead seemed to be a data spectacle waiting to happen. Think about all the events planned for 2020! Here’s what we wrote.
The topic of this year’s Marvin Zelen Symposium has to do with the year itself — 2020. This first year of a new decade brings with it statistical spectacles on a massive scale. From the Decennial Census and the Presidential Election in the US, to the 50th anniversary of Earth Day, to the Summer Olympics in Tokyo. These big events are bristling with data and its analyses — shaping how they are experienced, discussed, debated, and even litigated. Their time scales range from fractions of a second (the difference between gold and silver) to millions of years (the length of the geological record needed to understand our changing climate). They can be influenced by the actions of one person or the cumulative behaviors of hundreds of millions (as voters, as competitors, as spectators, as consumers). The Marvin Zelen Symposium will look at the major data spectacles of 2020, each one having synergy with the others, but also adding something new in our understanding of how data establish and reinforce systems of power on our planet.
Well, since we organized the 2020 Symposium, the entire world has changed. The original Zelen event was replaced with a “Zoomposium” on Covid-19, attracting nearly 500 attendees, and we moved the program covering everything from the Olympics to Earth Day to September, 2020 — this month.
But now that the date of the rescheduled event is upon us, think about what Covid-19 has done to many of the events mentioned in our abstract — we have seen everything from complications to cancellations. Still, we have decided to go forward with the 2020 Zelen Symposium as we had originally planned it.
We expect the difficulties of this year will inflect each of the talks on the program. Marvin Zelen embraced the complexity of data and its relationship to lived experience — the same is true for this edition of the Symposium bearing his name.
Speakers:

Ben Hansen, University of Michigan, “The Test of Heightened Election-Year Scrutiny and the Tests in your Table 1”

Brendan Nyhan, Dartmouth College, “Selective Exposure to Misinformation: Understanding the Consumption of False News and Anti-Vaccine Content Online”
Regina Nuzzo, Gallaudet University, “Brain-Hacking Stats Communication: A Quick Tour”
Amy O’Hara, Georgetown University, “The 2020 Census: Expected vs. Actual”
Andrew Revkin, Columbia University, “Taming Social Media for Constructive Climate Crosstalk”

Charles Stewart, Massachusetts Institute of Technology, “Puzzling Through Demand for Mail Voting in the 2020 Election”
Daniel Webb, United States Olympic Committee, “Forecasting the Olympic Games: Team USA’s Medal Prediction Models”

Jeremy White, New York Times, “Visualizing the Performances of the World’s Greatest Athletes”

Register by September 29, 2020.
Audience is capped at 500 participants. Participate on Twitter: #Zelen2020.

3D Spatial Organization Within Tumors
2020-09-29 13:00 - 14:00
Data Science Seminar
September 29, 2020
1:00PM ET
Martin Aryee, PhD
Assistant Professor of Pathology, Harvard Medical School
Assistant Molecular Pathologist, Massachusetts General Hospital
Zoom link: https://dfci.zoom.us/j/95524743149?pwd=[REDACTED]
Abstract: The spatial organization of biological systems can impart additional functionality beyond that of the individual components. This is true at a range of scales – from cells in a tissue to individual genes and regulatory elements in a single genome. High-throughput assays that permit spatial measurements have advanced greatly in the past decade and revealed oncogenic architectural alterations in tumors at the tissue, cellular and chromosome levels. Here I will discuss tumor spatial organization in gastrointestinal malignancies at two different scales. First, I will describe how we used single-cell RNA-Seq and RNA in situ hybridization in pancreatic cancer to identify cell state and tissue architecture changes induced by contact with stromal cancer-associated fibroblasts (CAFs). We analyzed the spatial architecture of cell states in 195 pancreatic tumors, mapping over 300,000 individual cancer cells. We were able to classify different types of tumor glands based on their cell-type composition, and show that these functional units can be used to characterize tumors in ways not evident from analyses of dissociated single cells. Second, I will discuss findings from a recent study of 3D genome organization within colon cancer cell nuclei. We found oncogenic changes at the level of regulatory chromatin loops and topologically associated domains (TADs), but the most surprising and striking change involves a large-scale repackaging of heterochromatin that appears to restrain tumor progression, representing a failed anti-tumor epigenetic brake.

Cancer Development, Heterogeneity and Dynamics from Premalignancy to Drug Refractory Disease
2020-09-22 14:00 - 15:00
Data Science Seminar
September 22, 2020
2:00PM ET
Ignaty Leshchiner, PhD
Postdoctoral Fellow, Harvard Medical School/Brigham and Women’s Hospital
Zoom link: http://bit.ly/DSSept22
Abstract:
Real-time study of tumor emergence and progression in patients will help predict and ultimately change the course of the patient’s disease. This could be achieved by inferring genotypes of heterogeneous cell populations within the tumor, their fitness, growth rates, corresponding expression patterns and drug tolerance states. We have developed a set of computational methods to infer the order of tumor-initiating events and to follow the dynamics and competition of cancer cell populations during disease progression and treatment. The package, PhylogicNDT, uses tumor genomic data to reconstruct the process of tumor formation, natural growth kinetics, competition and spread of resistance clones. We applied this package to 2,658 primary cancers to reconstruct developmental trajectories and history of common tumor types in premalignancy and early malignancy state; reconstruct cancer cell populations and growth rates, fitness and kinetics of individual clones during natural progression of leukemia in vivo; analyze spatial progression of resistance clones and find new resistance mechanisms in a large cohort of rapid autopsy cases. By integrating blood biopsy (ctDNA), solid tissue biopsy and autopsy data we show that resistance often emerges in multiple distant metastatic sites simultaneously, with evidence of multiple resistance mutations present in the blood’s ctDNA at the same time. Finally, we combine bulk and single cell sequencing data to help identify genetically distinct clones and explain their phenotypic differences. We envision that treatment decisions will improve with better understanding of tumor development, clonal structure and microenvironment, and the path tumor takes to become malignant and progress after treatment.

A New Hybrid Phase I-II-III Clinical Trial Paradigm
2020-09-15 13:00 - 14:00
Frontiers in Biostatistics Seminar
Tuesday September 15, 2020 at 1:00PM Eastern Time
Peter F. Thall, PhD
Department of Biostatistics
University of Texas M.D. Anderson Cancer Center
Abstract: Conventional evaluation of a new drug, 𝐴, is done in three phases. Phase I relies on toxicity to determine a “maximum tolerable dose” (MTD) of 𝐴, in phase II it is decided whether 𝐴 at the MTD is “promising” in terms of response probability, and if so a large randomized phase III trial is conducted to compare 𝐴 to a control treatment, 𝐶, based on survival time or progression free survival time. This paradigm has many flaws. The first two phases may be combined by conducting a phase I-II trial, which chooses an optimal dose based on both efficacy and toxicity, with evaluation of 𝐴 at the optimal phase I-II dose then done in phase III. In this talk, I will describe a new paradigm, motivated by the possibility that the optimal phase I-II dose may not maximize mean survival time with 𝐴. A hybrid phase I-II-III design is presented that allows the optimal phase I-II dose of 𝐴 to be re-optimized based on survival time data after the first stage of phase III. The hybrid design relies on a mixture model for the survival time distribution as a function of efficacy, toxicity, and dose. A simulation study is presented to evaluate the design’s properties, including comparison to the more conventional approach that does not re-optimize the dose of 𝐴 in phase III.
A recording of this seminar is available on your YouTube Channel.

Data Science Zoominar: Data Visualization Literacy: Avoiding common mistakes and recognizing deceitful practices
2020-09-02 13:00 - 14:00
Wednesday September 2, 2020
1:00PM ET
A conversation with Alberto Cairo
Knight Chair in Visual Journalism at the School of Communication of the University of Miami
Moderator: Rafael Irizarry
A recording of the talk is available on our YouTube channel.

Training Sessions: InForm
2020-08-26 10:00 - 12:00
Wednesday August 26 from 10:00am to 12:00PM
Danny Quinn will demonstrate extracting DF/HCC clinical trials data from InForm (which is the electronic data capture system (eDC)) into SAS data sets. There will also be a discussion on the attributes of the data sets and various utilities which support the extraction process.

Data Science Zoominar: Increasing Diversity in Data Science
2020-08-11 13:00 - 14:00
A conversation with Emma Benn, DrPh
Associate Professor, Center for Biostatistics and Department of Population Health Science and Policy
Icahn School of Medicine at Mount Sinai
Moderator: Rafael Irizarry
A recording of the talk is available on our YouTube channel.

Data Science Zoominar: Data-driven Movement Monitoring During the Pandemic
2020-08-04 13:00 - 14:00
A conversation with Caroline Buckee, Associate Professor of Epidemiology, Harvard TH Chan School of Public Health
Moderator: Rafael Irizarry
A recording of the talk is available on our YouTube channel.

Data Science Zoominar: A Comparison of COVID-19 Prediction Models
2020-07-21 13:00 - 14:00
A conversation with Nicholas Reich, Associate Professor of Biostatistics,
University of Massachusetts Amherst
Moderator: Rafael Irizarry
https://bit.ly/DSJuly21
We are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy.
A recording of the talk is available on our YouTube channel.

Data Science Zoominar: The Latest on COVID-19 Testing: How Can Testing Help Us Reopen?
2020-07-14 13:00 - 14:00
A conversation with Michael Mina, Assistant Professor
Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health
Moderator: Rafael Irizarry
We are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy.
A recording of the talk is available on our YouTube channel.

Data Science Zoominar: A Debate About the Severity of the COVID-19 Pandemic
2020-07-07 13:00 - 14:00
A conversation with John P.A. Ioannidis, C. F. Rehnborg Professor In Disease Prevention In The School Of Medicine, Professor Of Medicine, Of Health Research And Policy (Epidemiology) And By Courtesy, Of Statistics And Of Biomedical Data Science, Stanford University
Moderator: Rafael Irizarry
We are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy.
https://bit.ly/DSJuly7
A recording of the talk is available on our YouTube channel.

Data Science Zoominar: Massachusetts Data from the COVID-19 Response
2020-06-30 13:00 - 14:00
A conversation with Gillian Haney, Director of Infectious Disease Surveillance and Informatics, Massachusetts Department of Public Health and Catherine (Katie) Brown, State Public Health Veterinarian, Massachusetts Department of Public Health
Moderator: Rafael Irizarry
We are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy.
https://bit.ly/DSJune30
A recording of the talk is available on our YouTube channel.
