Butros (Peter) Dahu

Peter's Research

Peter's research is to integrate population health and geospatial data layers with artificial intelligence (AI)-generated information from remote sensing for advanced predictive and prescriptive analytics of population phenomena such as the COVID-19 outbreak. Use cutting edge and best practice analytical techniques, we will extract information from multi-dimensional heterogeneous complex data collections such as environmental time series; multiscale-images; epidemiological and spaceborne sensor data. Extracted information can include multiscale convolutional features from high-resolution images; time dependent characterizations; and complex multi-model behavior. Such features are critical to the etiology of any phenomena. I thrive on new approaches to complex problems involving complex data.

Open Access Paper:

PhD Candidate, Informatics

Research Focus:

  • Geospatial AI for Health & Disease Management

  • Deep Learning Analysis of Space-borne Sensors for Health Mapping

  • Epidemiological Analytics


Master of Science: Health and Bioinformatics, 2022

  • University of Missouri

Master of Business Administration: Health Sector Management and Finance, 2016

  • DePaul University

Graduate Certificate: Data Science and Analytics, 2021

  • University of Missouri

Bachelor of Science: Biology, minor Chemistry, 2013

  • University of Houston


  1. Dahu BM, Khan S, Li WS, Shu X, Popescu M, Scott GJ. Time Trend Analysis of COVID-19 Positive Individuals in Boone County, Missouri Based on Demographic Features. AMIA 2022 Annual Symposium, Washington, DC, United States (under review)

  2. Khan S, Dahu BM, Scott GJ. A Spatio-temporal Study of Changes in Air Quality from Pre-1 COVID Era to Post-COVID Era in Chicago, USA. Aerosol and Air Quality Research (AAQR). 2022 (accepted)

  3. Cao Y, Dahu BM, Scott GJ. A Geographic Computational Visual Feature Database for Natural and Anthropogenic Phenomena Analysis from Multi-Resolution Remote Sensing Imagery. 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2020) Seattle, Washington, United States. 2020 URL: https://www.ncbi.nlm.nih.gov/myncbi/1xIgokelE A1u8/bibliography/public/