Matthew Harrison

About

Matthew Harrison is an Associate Professor of Applied Mathematics at Brown University. He earned his Ph.D. from Brown University in 2005. His research interests include conditional inference, robust inference, statistical methods in neuroscience, random graphs and tables, nonstationary time series, Bayesian nonparametrics, multiple hypothesis testing, and importance sampling. Harrison has contributed to the development of statistical techniques for neural data analysis, including methods for spike resampling, hypothesis testing, and neural ensemble analysis. His work has applications in neuroscience, particularly in understanding neural ensemble spiking precision, neuronal synchrony during seizures, and brain-computer interfaces.

Research topics

• Computer Science
• Biology
• Database
• Geography
• Ecology

Selected publications

• Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates

  Nature Communications · 2023-02-10 · 226 citations

  articleOpen accessCorresponding

  Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We…

  PublisherOA PDFDOI

• Carbon myopia: The urgent need for integrated social, economic and environmental action in the livestock sector

  Global Change Biology · 2021-07-27 · 153 citations

  reviewOpen access1st authorCorresponding

  Livestock have long been integral to food production systems, often not by choice but by need. While our knowledge of livestock greenhouse gas (GHG) emissions mitigation has evolved, the prevailing focus has been-somewhat myopically-on technology applications associated with mitigation. Here, we (1) examine the global distribution of livestock GHG emissions, (2) explore social, economic and environmental co-benefits and trade-offs associated with mitigation interventions and (3) critique approac…

  PublisherOA PDFDOI

• GENDER-SENSITIVE APPROACHES IMPROVE CHARACTERISATION OF RESILIENCE IN AFRICAN PASTORAL SYSTEMS

  Nomadic Peoples · 2025-09-09 · 4 citations

  articleOpen access

  Strategies for improving resilience in African pastoral systems face increasing scrutiny, particularly in the context of climate shocks. Here, we explore the influence of gender dynamics in relation to resilience of dryland socio-ecological systems and pastoralist communities in northern Kenya. The findings challenge contemporary perspectives of women’s adaptive capacity and traditional gender roles, highlighting women’s nuanced understanding of household needs and ability to innovate during cri…

  PublisherDOI

• Anthropogenic and natural influence on vegetation ecosystems from 1982 to 2023

  Environmental Research Letters · 2025-08-08 · 2 citations

  articleOpen access

  Abstract Vegetation greening trends are a critical and direct indicator to reflect photosynthetic activity of plants at the ecosystem scale. The monitoring of vegetation greening is crucial for assessing ecosystem health, sustaining biodiversity, improving soil health, and providing direction for effective environmental management. However, long-term changes in greening trends are rarely reported due to radiometric inconsistencies among different satellite sensors. Here, we used 12 machine learn…

  PublisherDOI

• Time-Averaged Drift Approximations are Inconsistent for Inference in Drift Diffusion Models

  ArXiv.org · 2025-12-11

  preprintOpen accessSenior author

  Drift diffusion models (DDMs) have found widespread use in computational neuroscience and other fields. They model evidence accumulation in simple decision tasks as a stochastic process drifting towards a decision barrier. In models where the drift rate is both time-varying within a trial and variable across trials, the high computational cost for accurate likelihood evaluation has led to the common use of a computationally convenient surrogate for parameter inference, the time-averaged drift ap…

  PublisherOA PDFDOI

Recent grants

• Conditional Inference Algorithms for Graphs, Tables, and Point Processes

  NSF · $120k · 2013–2016

Frequent coauthors

• Ke Liu

  University of Tasmania

  120 shared

• David A. Borton

  Providence College

  76 shared

• Andrew D. Moore

  60 shared

• Shah Fahad

  University of Swabi

  56 shared

• RP Rawnsley

  51 shared

• Franco Bilotto

  University of Tasmania

  42 shared

• Leigh R. Hochberg

  Harvard University

  38 shared

• Carla Ferreira

  Bolin Centre for Climate Research

  37 shared

Labs

• Applied MathematicsPI

Education

• PhD, Research School of Biological Sciences

  Australian National University

  2009

• Bachelor of Science (1st Class Hons), Research School of Biological Sciences

  Australian National University

  2006

• Bachelor of Applied Science (Biotechnology), Applied Science

  La Trobe University - Bendigo Campus

  2005

• Bachelor of Civil Engineering (1st Class Hons), Civil Engineering

  La Trobe University - Bendigo Campus

  2005

Similar researchers at Brown University

• Björn Sandstedeh-53
• Zhicheng Wangh-40
• Hongjie Dongh-38