Ana Maria Pinto da Silva

About

Ana Maria Pinto da Silva joins the School of Design at Carnegie Mellon University as the Head of School. She previously served as the Director of the Masters in Human Computer Interaction and Design program at the University of Washington in Seattle. In her role at the University of Washington, she provided strategic program leadership, supported academic and career advising, assisted with admissions, curriculum development, and oversaw the launch of student capstone projects. Pinto da Silva earned her bachelor’s degree in architecture from the University of California at Berkeley and her master’s degree in design studies from the Harvard University Graduate School of Design. She is an accomplished designer, technologist, educator, and public speaker, with a commitment to advancing the role of designers, engineers, and researchers in the development of future technology innovation centered on equity, inclusion, and innovation. She is dedicated to community service, founding the Seattle Pecha Kucha speaker series, co-chairing the Harvard GSD’s Global Design Impact initiative, and serving on numerous boards and committees including the Harvard GSD MDE program, MOHAI, Leadership Tomorrow, and the Nehemiah Initiative.

Research topics

• Psychology
• Biology
• Neuroscience
• Evolutionary biology
• Cognitive science

Selected publications

• A resource for the detailed 3D mapping of white matter pathways in the marmoset brain

  Nature Neuroscience · 2020-01-13 · 146 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Accelerating the Evolution of Nonhuman Primate Neuroimaging

  Neuron · 2020 · 124 citations

  DOI

• PERSPECTIVES ON THE APPLICATION OF AEROGELS IN TEXTILE MATERIALS FOR OIL ADSORPTION AND THEIR AI-ASSISTED IMPROVEMENT: A CONCISE REVIEW

  2025-01-01

  review
  PublisherDOI

• Random Effects Models for Understanding Variability and Association between Brain Functional and Structural Connectivity.

  PubMed · 2025-08-04

  articleOpen access

  The human brain is organized as a complex network, where connections between regions are characterized by both functional connectivity (FC) and structural connectivity (SC). While previous studies have primarily focused on network-level FC-SC correlations (i.e., the correlation between FC and SC across all edges within a predefined network), edge-level correlations (i.e., the correlation between FC and SC across subjects at each edge) has received comparatively little attention. In this study, w…

  PublisherOA PDF

• Denoising diffusion MRI data: Principal components meet non-local block-matching

  Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2025-09-16

  article

  Motivation: Diffusion MRI suffers from very low SNR, especially for high spatial resolution and/or diffusion gradient strength (high b-values). Goal(s): We aim to mitigate the noise in dMRI, improve over existing denoising algorithms, and suppress noise while preserving tissue details. Approach: We proposed a novel denoising method for dMRI that combines global singular value decomposition and non-local block-matching denoising for the Principal Components. Results: Our method outperformed sever…

  PublisherDOI

Recent grants

• NIH Grant Z01NS003041

  NIH · $2.5M

• NIH Grant Z01NS003012

  NIH · $2.5M

• Investigation of the Modulators of Cerebrovascular Coupling

  NIH · $15.4M

Frequent coauthors

• Cirong Liu

  Center for Excellence in Brain Science and Intelligence Technology

  94 shared

• Cecil Chern‐Chyi Yen

  National Institute of Neurological Disorders and Stroke

  91 shared

• David A. Leopold

  National Eye Institute

  74 shared

• Alan P. Koretsky

  62 shared

• Diego Szczupak

  53 shared

• Pascal Sati

  Cedars-Sinai Medical Center

  50 shared

• Daniel S. Reich

  National Institutes of Health

  46 shared

• Nathanael J. Lee

  44 shared

Education

• PhD, Bioengineering

  Carnegie Mellon University

  1996

• MS, Electrical Engineering

  Universidade Federal de Pernambuco

  1992

• BS, Electrical Engineering

  Universidade Federal de Pernambuco

  1990

Similar researchers at Carnegie Mellon University

• Jodi Forlizzih-74
• Jonathan Caganh-53
• John Zimmermanh-52