Mark Crovella

About

Mark Crovella is a Professor and former Chair in the Department of Computer Science at Boston University, where he has been a faculty member since 1994. His research interests center on improving the understanding, design, and performance of networks and networked computer systems, mainly through the application of data mining, statistics, and performance evaluation. He has made significant contributions to understanding the Internet and World Wide Web, social networks, and biological networks. Professor Crovella is a co-author of the book 'Internet Measurement: Infrastructure, Traffic, and Applications' and has authored over two hundred papers on networking and computer systems, which have garnered over 25,000 citations. He holds ten patents derived from his research. His professional service includes serving as Chair of ACM SIGCOMM from 2007 to 2009. He is a Fellow of both the ACM and the IEEE. His notable research includes work on self-similarity in web traffic, which received the 2010 ACM SIGMETRICS Test of Time Award, and on routing state distance, which won a 2013 IETF/IRTF Applied Networking Research Prize. Additionally, he has held visiting positions at LIP6, INRIA Paris, and LINCS, and served as Chief Scientist of Guavus, Inc. from 2012 to 2014.

Research topics

• Genetics
• Biology
• Computer Security
• Computational biology
• Medicine
• Computer Science
• Bioinformatics
• Neuroscience
• Cell biology
• World Wide Web

Selected publications

• Single-cell transcriptional networks in differentiating preadipocytes suggest drivers associated with tissue heterogeneity

  Nature Communications · 2020 · 50 citations

  White adipose tissue plays an important role in physiological homeostasis and metabolic disease. Different fat depots have distinct metabolic and inflammatory profiles and are differentially associated with disease risk. It is unclear whether these differences are intrinsic to the pre-differentiated stage. Using single-cell RNA sequencing, a unique network methodology and a data integration technique, we predict metabolic phenotypes in differentiating cells. Single-cell RNA-seq profiles of human…

  PublisherOA PDFDOI

• How YouTube Leads Privacy-Seeking Users Away from Reliable Information

  2020 · 20 citations

  Senior authorCorresponding

  Online media is increasingly selected and filtered by recommendation engines. YouTube is one of the most significant sources of socially-generated information, and as such its recommendation policies are important to understand. Because of YouTube's revenue model, the nature of its recommendation policies is fairly opaque. Hence, we present an empirical exploration of the nature of YouTube recommendations, concentrating on socially-impactful dimensions. First, we confirm that YouTube's recommend…

  DOI

• Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease

  Alzheimer s Research & Therapy · 2020 · 17 citations

  BACKGROUND: Identifying and understanding the functional role of genetic risk factors for Alzheimer disease (AD) has been complicated by the variability of genetic influences across brain regions and confounding with age-related neurodegeneration. METHODS: A gene co-expression network was constructed using data obtained from the Allen Brain Atlas for multiple brain regions (cerebral cortex, cerebellum, and brain stem) in six individuals. Gene network analyses were seeded with 52 reproducible (i.…

  PublisherOA PDFDOI

• State‐of‐the‐art computational methods to predict protein–protein interactions with high accuracy and coverage

  PROTEOMICS · 2023-07-03 · 14 citations

  reviewOpen accessSenior author

  Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction,…

  PublisherOA PDFDOI

• Ligand interaction landscape of transcription factors and essential enzymes in E. coli

  Cell · 2025-01-24 · 11 citations

  article
  PublisherDOI

Recent grants

• NeTS: Small: Analytic Tools for Evolving Path-Based Networks

  NSF · $500k · 2016–2020

• TC: Medium: Collaborative Research: Wide-Aperture Traffic Analysis for Internet Security

  NSF · $723k · 2009–2014

• III: Small: Structural Matrix Completion for Data Mining Applications

  NSF · $500k · 2014–2018

Frequent coauthors

• Paul Barford

  University of Wisconsin–Madison

  30 shared

• Andrew Emili

  Boston University

  29 shared

• Ahmed Youssef

  Center for Systems Biology

  20 shared

• Anukool Lakhina

  19 shared

• Azer Bestavros

  17 shared

• Eric D. Kolaczyk

  McGill University

  15 shared

• Christophe Diot

  Google (United States)

  15 shared

• Fei Bian

  Shandong Academy of Agricultural Sciences

  15 shared

Labs

• Principles of Programming and VerificationPI

Education

• Ph.D.

  Boston University

• M.S.

  Boston University

• B.S.

  Boston University

Awards & honors

• 2010 ACM SIGMETRICS Test of Time Award
• 2013 IETF/IRTF Applied Networking Research Prize
• Fellow of the ACM
• Fellow of the IEEE

Similar researchers at Boston University

• Kate Saenkoh-89
• Ran Canettih-78
• Stan Sclaroffh-69