Shaw Talebi, PhD
About
Meet Shaw, an educator, entrepreneur, data scientist, researcher, drummer, and bread enthusiast driven by an insatiable curiosity to understand the world and create better systems.
With a Ph.D. in Physics from The University of Texas at Dallas, Shaw’s research focused on novel AI applications to fields spanning neuroscience, environmental science, and human performance.
As an educator, Shaw is driven by a passion for learning and a mission to make AI accessible to all. His YouTube channel and technical blog have taught millions and have gained an audience of over 50,000 learners.
“Data is how we listen to reality, but data science is how we translate what we hear into something we can understand.”
Research
Wijeratne, Lakitha O. H., Kiv, D. R., Aker, A. R., Talebi, S., & Lary, D. J. (2019). Using machine learning for the calibration of airborne particulate sensors. Sensors, 20(1), 99.
Zhang, Y., Wijeratne, L. O. H., Talebi, S., & Lary, D. J. (2021). Machine learning for light sensor calibration. Sensors, 21(18), 6259.
Talebi, S., Waczak, J., Fernando, B. A., Sridhar, A., & Lary, D. J. (2022). Data-driven EEG band discovery with decision trees. Sensors, 22(8), 3048.
Fernando, B. A., Sridhar, A., Talebi, S., Waczak, J., & Lary, D. J. (2022). Unsupervised blink detection using eye aspect ratio values.
Wijeratne, Lakitha Omal Harindha, Zewdie, G. K., Kiv, D., Aker, A., Lary, D. J., Talebi, S., … Levetin, E. (2021). Advancement in Airborne Particulate Estimation Using Machine Learning. In Geospatial Technology for Human Well-Being and Health (pp. 243–263). Springer International Publishing Cham.
Talebi, S., Lary, D. J., Wijeratne, L. O. H., Fernando, B., Lary, T., Lary, M., … Others. (2022). Decoding physical and cognitive impacts of particulate matter concentrations at ultra-fine scales. Sensors, 22(11), 4240.
Waczak, J., Aker, A., Wijeratne, L. O. H., Talebi, S., Fernando, A., Dewage, P. M. H., … Lary, D. J. (2024). Characterizing water composition with an autonomous robotic team employing comprehensive in situ sensing, hyperspectral imaging, machine learning, and conformal prediction. Remote Sensing, 16(6), 996.
Ruwali, S., Talebi, S., Fernando, A., Wijeratne, L. O. H., Waczak, J., Dewage, P. M. H., … Others. (2024). Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning. BioMedInformatics, 4(2), 1019–1046.
Fernando, B. A., Talebi, S., Wijeratne, L., Waczak, J., Sooriyaarachchi, V., Ruwali, S., … Others. (2024). Data-driven environmental health: Unraveling particulate matter trends with biometric signals. Medical Research Archives, 12(1).
Ruwali, S., Fernando, B. A., Talebi, S., Wijeratne, L., Waczak, J., Sooriyaarachchi, V., … Others. (2024). Gauging ambient environmental carbon dioxide concentration solely using biometric observations: A machine learning approach. Medical Research Archives, 12(1).
Ruwali, S., Fernando, B., Talebi, S., Wijeratne, L., Waczak, J., Madusanka, P. M. H., … Others. (2024). Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response. Advances in Environmental and Engineering Research, 5(2), 1–12.
Waczak, J., Aker, A., Wijeratne, L. O. H., Talebi, S., Fernando, A., Dewage, P. M. H., … Others. (2024). Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping. Remote Sensing, 16(13), 2430.
Talebi, S. (2022). Physical Quantification of the Interactions Between Environment, Physiology, and Human Performance.
