Marcos E. Barreto (0000-0002-7818-1855)

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PhD in Computer Science (UFRGS, Porto Alegre, 2010). Post-doctoral research (Newton International Fellow, The Royal Society) on Health Data Science at the Institute of Health Informatics (University College London, UK, 2016-2018). PG Certificate in Data Science for Research on Health and Biomedicine (University College London, UK, 2017-2018). Currently, I am an Assistant Professor of Data Science at the Department of Statistics, London School of Economics and Political Science. From 2010 to 2020, I was an associate professor at UFBA (Federal University of Bahia). My research interests comprise big data linkage and analytics, artificial intelligence, and high-performance computing applied to healthcare and socioeconomic data. More information at https://marcosebarreto.github.io/

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London School of Economics: London, GB

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Marcos E. Barreto

https://orcid.org/0000-0002-7818-1855

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Employment (3)

London School of Economics: London, GB

London School of Economics and Political Science: London, Greater London, GB

Federal University of Bahia (UFBA): Salvador, BA, BR

Education and qualifications (3)

University College London: London, London, GB

University College London: London, London, GB

Universidade Federal do Rio Grande do Sul: Porto Alegre, RS, BR

Funding (1)

Integrating socioeconomic and health data to combat malaria

Works (50 of 61)

Exploring machine learning algorithms in sickle cell disease patient data

Approach Generative AI Tools Proactively or Risk Bypassing the Learning Process in Higher Education

To improve their courses, educators should respond to how students actually use AI

Data challenges for international health emergencies

K-means DTW Barycenter Averaging

A fundamental problem at the heart of data science teaching

Main and moderated effects of multimorbidity and depressive symptoms on cognition

Malaria-VisAnalytics: a tool for visual exploratory analysis of Brazilian public malaria data

Malaria-VisAnalytics: a tool for visual exploratory analysis of Brazilian public malaria data

Prioritizing nurturing care at the municipal and district level with the Brazilian Early Childhood Friendly Municipal Index (IMAPI)

Malaria-VisAnalytics: A Tool for Visual Exploratory Analysis of Brazilian Public Malaria Data

Cohort profile: The 100 Million Brazilian Cohort

Birthweight, gestational age and familial confounding in sex differences in infant mortality: a matched co-twin control study of Brazilian male-female twin pairs identified by population data linkage

Gamification Frameworks and Models for Health Contexts

Sociodemographic inequities in nurturing care for early childhood development across Brazilian municipalities

Nurturing care indicators for the Brazilian Early Childhood Friendly Municipal Index (IMAPI)

BioMeT and Algorithm Challenges: A Proposed Digital Standardized Evaluation Framework

Inteligência artificial e COVID-19

CIDACS-RL: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability

Sex Differences in Infant Mortality: A Population Data Linkage Study of Brazilian Male-Female Twin Pairs

Convolutional Support Vector Models: Prediction of Coronavirus Disease Using Chest X-rays

Local Processing of Massive Databases with R: A National Analysis of a Brazilian Social Programme

Matched-Pair Analysis Using Machine Learning to Predict 1-year Mortality in Newborn Twins

Wearables beyond borders: A case study of barriers to gait assessment in low-resource settings

Ontological concepts for information sharing in cloud robotics

Abstracts

A review and comparison of ontology-based approaches to robot autonomy

Providing Malaria Analytics as a Service

The Center for Data and Knowledge Integration for Health (CIDACS)

K-Fact: Using the Frequency Factor for Clustering Categorical Data

Prototyping and Validating the CORA Ontology: Case Study on a Simulated Reconnaissance Mission

Administrative Data Linkage in Brazil: Potentials for Health Technology Assessment

Exploring hybrid parallel systems for probabilistic record linkage

Applying term frequency-based indexing to improve scalability and accuracy of probabilistic data linkage

Linking surveillance and climate data to combat malaria

On the Accuracy and Scalability of Probabilistic Data Linkage Over the Brazilian 114 Million Cohort

A Machine Learning Trainable Model to Assess the Accuracy of Probabilistic Record Linkage

Accelerating Docking Simulation Using Multicore and GPU Systems

uStorage - A Storage Architecture to Provide Block-Level Storage Through Object-Based Storage

Ontology for autonomous robotics

Probabilistic Integration of Large Brazilian Socioeconomic and Clinical Databases

Towards a Robot Task Ontology Standard

Treating heterogeneity and uncertainty in data integration: study on Brazilian healthcare databases.

Assessing the accuracy of probabilistic record linkage of social and health databases in the 100 million Brazilian cohort

Design and evaluation of probabilistic record linkage methods supporting the Brazilian 100-million cohort initiative.

Auto-tuning TRSM with an asynchronous task assignment model on multicore, multi-GPU and coprocessor systems

A Spark-based workflow for probabilistic record linkage of healthcare data

Automatic routine tuning to represent landform attributes on multicore and multi-GPU systems

Support for bioinformatics applications through volunteer and scalable computing frameworks

Auto-tuning methodology to represent landform attributes on multicore and multi-GPU systems

Peer review (8 reviews for 2 publications/grants)