Mohamed (Moe) Sallam Ph.D.

Education

2014-2016: Postdoc-University of Florida, Gainesville, FL
2010-2013: Doctor of Philosophy, King Saud University, Saudi Arabia
2008: Master of Science, Ain Shams University, Cairo, Egypt

Biography

Data scientist, health Geographer, modeler, disease and arthropod-vector ecologist, and a former U.S. Navy entomologist. I develop AI and machine learning tools to predict and forecast distribution models using big data repositories and field-collected data to demonstrate the geographic mosaic of population health sciences, especially emerging disease pathogens. My research focus is the One-Health strategies that integrate biosurveillance data and their associated environmental predictors to better identify overlooked risk factors using machine and deep learning techniques. I develop, with my collaborators, user-friendly platforms for disease-risk mapping and arthropod identification using machine learning and artificial intelligence.
My current funded projects evaluate multiple innovative airborne and AI-driven products, aiming for arthropod surveillance, automated identification, and real-time diagnostic tools for infectious diseases applicable in remote, inaccessible field settings, expediting diagnoses during outbreaks, and reducing reliance on sample transport to support force health protection in deployment settings.

Career Highlights: Positions, Projects, Deployements, Awards and Additional Publications

2019-April 2022: Assistant professor, University of Nevada-Reno, Reno, Nevada

2017-2018: Senior analyst, Environmental Protection Agency, Research Triangle Park-Durham, North Carolina

USU Health Geography Lab Helps Secure First Place in XPRIZE Rainforest Competition: 2024; https://news.usuhs.edu/2024/11/usu-health-geography-lab-helps-secure.html

Stanislav Kolencik, Oldrich Sychra, Kevin P Johnson, Jason D Weckstein, Mohamed F Sallam, Julie M Allen. The parasitic louse genus Myrsidea (Amblycera: Menoponidae): a comprehensive review and world checklist. Insect systematics and diversity 8 (3), 1

Masoud A Rostami, Behnaz Balmaki, Lee A Dyer, Julie M Allen, Mohamed F Sallam, Fabrizio Frontalini. Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study. Journal of Big Data 10 (1), 151

Qualls W.A., Steck M.R., Weaver J.R., Xue R.D., and M.F. Sallam. Shift in the spatial and temporal distribution of Aedes taeniorhynchus following environmental and local developments in St. Johns County, Florida. Wetlands Ecology and Management, https://doi.org/10.1007/s11273-021-09816-6

Mohamed F Sallam, Shelley Whitehead, Narayani Barve, Amely Bauer, Robert Guralnick, Julie Allen, Yasmin Tavares, Seth Gibson, Kenneth J Linthicum, Bryan V Giordano, Lindsay P Campbell. Co-occurrence probabilities between mosquito vectors of West Nile and Eastern equine encephalitis viruses using Markov Random Fields (MRFcov). Parasites & Vectors 16 (1), 10

JE Cilek, JD Fajardo, JR Weston, M Sallam. Evaluation of alternative power sources for operating CDC mosquito surveillance traps. Journal of the American Mosquito Control Association 38 (1), 24-28.

Sallam M.F., Rui De X., Pereira M.R., and P.G. Koehler. Ecological niche modeling of mosquito vectors of West Nile virus in St. John’s County, Florida, USA. Parasites & Vectors, 9:371.

Lindsay P Campbell, Mohamed F Sallam, Amely M Bauer, Yasmin Tavares, Robert P Guralnick. Climate, landscape, and life history jointly predict multidecadal community mosquito phenology. Scientific Reports 13 (1), 3866

Representative Bibliography

XPrize: https://news.usuhs.edu/2024/11/usu-health-geography-lab-helps-secure.html

GoogleScholar: https://scholar.google.com/citations?hl=en&user=6jvXiuYAAAAJ&view_op=list_works&sortby=pubdate

Labweek: https://twitter.com/USUhealthsci/status/1650571114932731904

Uni-port taxis box: https://pubmed.ncbi.nlm.nih.gov/30689913/