Chinasa T. Okolo
PhD candidate at Cornell University
Introducing a WE Local Buffalo Collegiate Competition Finalist, Chinasa T. Okolo! Chinasa is the Professional Development Co-Chair and Corporate Chair for Cornell GradSWE. Chinasa is also a blackcomputeHER Fellow and a recipient of the Community-Engaged Student Travel Grant from Engaged Cornell, the John Postel Scholarship from the North American Network Operators Group, the UNCF Oracle Academy Fellowship from Oracle Corporation, and the GEM Fellowship from The National GEM Consortium. She is also a Graduate Dean’s Scholar at Cornell University and a FLIP Fellow from the Diversifying Future Leadership in the Professoriate Alliance.
Chinasa’s research is in Smartphone-based Object Detection for Low-Resource Diagnostic Monitoring.
According to the World Health Organization, pneumonia accounts for 15% of deaths of children under the age of five, making it the single largest infectious cause of death for children worldwide. In Sub-Saharan Africa and other low-resource settings, poor infrastructure hampers pneumonia diagnosis and treatment, contributing to misdiagnoses of pneumonia as malaria or tuberculosis. In recent years, several methods have helped to make progress in diagnosing pneumonia in children living in low-resource regions, including fingertip pulse oximeters, acute respiratory infection (ARI) timers, and mobile health (mHealth) applications, but challenges in the accuracy and usability of these tools remain present. Computer vision (CV), machine learning (ML), and signal processing has the capability to transform infectious disease diagnosis and has already shown extreme promise in this area. The goal of Chinasa’s research is to build a computer vision-based system that outputs multi-labeled predictions from audiovisual data of respiratory symptoms associated with pneumonia and other respiratory infections. Within this system, she will develop novel ML and CV models that accurately recognize symptoms of pneumonia and work with stakeholders in the field to ensure that the system she builds is usable and appropriate for community health workers (CHWs) who work in these contexts.
As a professor of computer science, Chinasa would like to work in Sub-Saharan Africa and advise governments and NGOs on health and technology policies for the Global South. Eventually, she plans to transition to academic leadership/administration and join the C-suite or board of a large tech company.
Chinasa likes to sing, update her blog, read intriguing articles & novels, travel the world, compose songs, and scroll down her Twitter feed.
Fun fact: She has traveled to almost 2 dozen countries and has lived in 3!
Find her on Twitter at @ChinasaTOkolo and on Instagram at @chinasza
PhD candidate at Cornell University
Introducing a WE Local Buffalo Collegiate Competition Finalist, Chinasa T. Okolo! Chinasa is the Professional Development Co-Chair and Corporate Chair for Cornell GradSWE. Chinasa is also a blackcomputeHER Fellow and a recipient of the Community-Engaged Student Travel Grant from Engaged Cornell, the John Postel Scholarship from the North American Network Operators Group, the UNCF Oracle Academy Fellowship from Oracle Corporation, and the GEM Fellowship from The National GEM Consortium. She is also a Graduate Dean’s Scholar at Cornell University and a FLIP Fellow from the Diversifying Future Leadership in the Professoriate Alliance.
Chinasa’s research is in Smartphone-based Object Detection for Low-Resource Diagnostic Monitoring.
According to the World Health Organization, pneumonia accounts for 15% of deaths of children under the age of five, making it the single largest infectious cause of death for children worldwide. In Sub-Saharan Africa and other low-resource settings, poor infrastructure hampers pneumonia diagnosis and treatment, contributing to misdiagnoses of pneumonia as malaria or tuberculosis. In recent years, several methods have helped to make progress in diagnosing pneumonia in children living in low-resource regions, including fingertip pulse oximeters, acute respiratory infection (ARI) timers, and mobile health (mHealth) applications, but challenges in the accuracy and usability of these tools remain present. Computer vision (CV), machine learning (ML), and signal processing has the capability to transform infectious disease diagnosis and has already shown extreme promise in this area. The goal of Chinasa’s research is to build a computer vision-based system that outputs multi-labeled predictions from audiovisual data of respiratory symptoms associated with pneumonia and other respiratory infections. Within this system, she will develop novel ML and CV models that accurately recognize symptoms of pneumonia and work with stakeholders in the field to ensure that the system she builds is usable and appropriate for community health workers (CHWs) who work in these contexts.
As a professor of computer science, Chinasa would like to work in Sub-Saharan Africa and advise governments and NGOs on health and technology policies for the Global South. Eventually, she plans to transition to academic leadership/administration and join the C-suite or board of a large tech company.
Chinasa likes to sing, update her blog, read intriguing articles & novels, travel the world, compose songs, and scroll down her Twitter feed.
Fun fact: She has traveled to almost 2 dozen countries and has lived in 3!
Find her on Twitter at @ChinasaTOkolo and on Instagram at @chinasza
