David S. Matteson

About

David S. Matteson is a faculty member in the Department of Computer Science at Cornell University. The provided page text does not include specific information about his research focus, background, or key contributions. Therefore, no detailed biography can be extracted from the available content.

Research topics

• Mathematics
• Data Mining
• Artificial Intelligence
• Computer Science
• Machine Learning
• Econometrics
• Geometry
• Mathematical economics
• Economics
• Physics

Selected publications

• Multivariate random forest prediction of poverty and malnutrition prevalence

  PLoS ONE · 2021 · 56 citations

  Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies' programming. However, state of the art models often rely on proprietary data and/or deep or transfer learning methods whose underlying mechanics may be challenging to interpret. We demonstrate how interpretable random forest models can produce estimates of a set of (potentially correlated) malnutrit…

  DOI

• Visualizing nanoparticle surface dynamics and instabilities enabled by deep denoising

  Science · 2025-02-27 · 28 citations

  article

  Materials functionalities may be associated with atomic-level structural dynamics occurring on the millisecond timescale. However, the capability of electron microscopy to image structures with high spatial resolution and millisecond temporal resolution is often limited by poor signal-to-noise ratios. With an unsupervised deep denoising framework, we observed metal nanoparticle surfaces (platinum nanoparticles on cerium oxide) in a gas environment with time resolutions down to 10 milliseconds at…

  PublisherDOI

• Vector <scp>AutoRegressive</scp> Moving Average Models: A Review

  Wiley Interdisciplinary Reviews Computational Statistics · 2025-01-13 · 17 citations

  reviewOpen access

  Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vector AutoRegressive (VAR) models, their popularity in empirical applications is dominated by the latter. Can this phenomenon be explained fully by the simplicity of VAR models? Perhaps many users of VAR models have not fully appreciated what VARMA models can provide. The goal of this review is to provide a comprehensive…

  PublisherOA PDFDOI

• Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study

  JMIR Formative Research · 2025-04-07 · 8 citations

  articleOpen access

  Background: Popularized by ChatGPT, large language models (LLMs) are poised to transform the scalability of clinical natural language processing (NLP) downstream tasks such as medical question answering (MQA) and automated data extraction from clinical narrative reports. However, the use of LLMs in the health care setting is limited by cost, computing power, and patient privacy concerns. Specifically, as interest in LLM-based clinical applications grows, regulatory safeguards must be established…

  PublisherDOI

• Widespread Advances in Corn and Soybean Phenology in Response to Future Climate Change Across the United States

  Journal of Geophysical Research Biogeosciences · 2025-04-01 · 4 citations

  article

  Abstract Crop phenology regulates seasonal carbon and water fluxes between croplands and the atmosphere and provides essential information for monitoring and predicting crop growth dynamics and productivity. However, under rapid climate change and more frequent extreme events, future changes in crop phenological shifts have not been well investigated and fully considered in earth system modeling and regional climate assessments. Here, we propose an innovative approach combining remote sensing im…

  PublisherDOI

Recent grants

• Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis

  NSF · $735k · 2019–2023

• Collaborative Research: Atomic Level Structural Dynamics in Catalysts

  NSF · $331k · 2019–2022

• New Frontiers in Time Series Analysis

  NSF · $300k · 2021–2025

Frequent coauthors

• David Ruppert

  Cornell University

  46 shared

• Ines Wilms

  19 shared

• Jacob Bien

  University of Southern California

  19 shared

• Yuchen Xu

  Shandong First Medical University

  16 shared

• Peter A. Crozier

  16 shared

• Nicholas A. James

  Mount Sinai Beth Israel

  15 shared

• Toryn L. J. Schafer

  Cornell University

  14 shared

• Benjamin B. Risk

  Emory University

  14 shared

Education

• PhD, Statistics

  University of Chicago

  2008

Awards & honors

• CAREER Award from the National Science Foundation
• Chancellor’s Award for Scholarship and Creative Activities f…
• inaugural Ann S. Bowers Research Excellence Award
• Faculty Research Awards from the Xerox/PARC Foundation and L…
• Fellow of the American Statistical Association (ASA)

Similar researchers at Cornell University

• Jon Kleinbergh-122
• Deborah Estrinh-120
• Wen Sunh-102