Giovanni Parmigiani

About

Giovanni Parmigiani is a Professor of Biostatistics at Harvard University. His research interests include Bayesian decision theory, multi-study statistical methods, machine learning for precision prevention and treatment in health care, and statistical techniques in cancer biology. He is affiliated with the Department of Statistics and is involved in various academic activities, including teaching and research, within the department.

Research topics

• Computer Science
• Mathematics
• Artificial Intelligence
• Machine Learning
• Statistics
• Immunology
• Biology
• Medicine
• Demography
• Internal medicine

Selected publications

• ComBat-seq: batch effect adjustment for RNA-seq count data

  NAR Genomics and Bioinformatics · 2020 · 1648 citations

  The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-…

  DOI

• Influenza Vaccination and COVID19 Mortality in the USA

  medRxiv (Cold Spring Harbor Laboratory) · 2020 · 88 citations

  COVID-19 mortality rate is higher in the elderly and in those with preexisting chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infection, and thus annual influenza vaccination is recommended for them. In this study, we explore a possible area-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data until June 10, 2020 toge…

  DOI

• Development of hyperdiploidy starts at an early age and takes a decade to complete

  Blood · 2024-11-21 · 11 citations

  articleOpen access

  ABSTRACT: Nearly half of patients with multiple myeloma (MM) have hyperdiploidy (HMM) at diagnosis. Although HMM occurs early, the mutational processes before and after hyperdiploidy are still unclear. Here, we used 72 whole-genome sequencing samples from patients with HMM and identified pre- and post-HMM mutations to define the chronology of the development of hyperdiploidy. An MM cell accumulated a median of 0.56 mutations per megabase before HMM, and for every clonal pre-HMM mutation, 1.21 mu…

  PublisherOA PDFDOI

• Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations

  arXiv (Cornell University) · 2020 · 11 citations

  We investigate the power of censoring techniques, first developed for learning {\em fair representations}, to address domain generalization. We examine {\em adversarial} censoring techniques for learning invariant representations from multiple "studies" (or domains), where each study is drawn according to a distribution on domains. The mapping is used at test time to classify instances from a new domain. In many contexts, such as medical forecasting, domain generalization from studies in populou…

  DOI

• Bayesian multi-study non-negative matrix factorization for mutational signatures

  Genome biology · 2025-04-16 · 4 citations

  articleOpen accessSenior author

  Mutational signatures are typically identified from tumor genome sequencing data using non-negative matrix factorization (NMF). However, existing NMF techniques only decompose a single dataset, limiting rigorous comparisons of signatures across conditions. We propose a Bayesian NMF method that jointly decomposes multiple datasets to identify signatures and their sharing pattern across conditions. We propose a fully unsupervised "discovery-only" model and a semi-supervised "recovery-discovery" mo…

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01CA105090

  NIH · $997k · 2010

• Multi-study Genomic Data Analysis

  NSF · $212k · 2009–2011

• Training Grant in Quantitative Sciences for Cancer Research

  NIH · $504k · 1979–2016

Frequent coauthors

• Bert Vogelstein

  Howard Hughes Medical Institute

  422 shared

• Victor E. Velculescu

  University of Baltimore

  413 shared

• Kenneth W. Kinzler

  Johns Hopkins University

  411 shared

• Levi Waldron

  City University of New York

  329 shared

• D. Williams Parsons

  Altarum Institute

  320 shared

• Curtis Huttenhower

  Harvard University

  278 shared

• Siân Jones

  272 shared

• Michael J. Birrer

  Winthrop Rockefeller Foundation

  236 shared

Labs

• Sports Analytics Laboratory at Harvard UniversityPI

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Education

• PhD, Statistics

  Carnegie Mellon University

  1990

Similar researchers at Harvard University

• Susan Murphyh-188
• Donald B. Rubinh-151
• Kosuke Imaih-67