Firas Gerges

firas

Firas Gerges

firas
Postdoctoral Research Associate
Email
[email protected]
Office
E401 E-Quad

Firas Gerges

Postdoctoral Research Associate
About

Firas is a Postdoctoral Research Associate at Princeton University's Department of Civil and Environmental Engineering working at the intersection of technology and sustainability, on applying AI and big data analytics to address critical environmental and urban issues. Firas holds a Ph.D. in Computer Science from the New Jersey Institute of Technology. He is focused on developing sophisticated AI models that advance our understanding of climate change impacts, air quality, urban heat islands, and the resilience of communities in the face of disasters. His approach integrates diverse data sources to predict and mitigate environmental challenges effectively.

Curriculum Vitae
CV
Firas CV
Website
https://firasgerges.com/
Education
  1. Ph.D. in Computer Science, 2022, New Jersey Institute of Technology, Newark, NJ, USA
    Dissertation: “Monitoring Climate Change with Machine Learning and Uncertainty Quantification” (Best Computer Science PhD Dissertation Award, 2022, NJIT)
  2. Master of Science in Computer Science, 2018, Lebanese American University, Byblos, Lebanon
    Dissertation: “Prediction of Movie Success before production using Machine Learning”
  3. Bachelor of Science in Computer Science, 2015, Lebanese American University, Byblos, Lebanon
Start Date
2022
Selected Publications
  1. Gerges, F., Llaguno-Munitxa, M., Zondlo, M., Boufadel, M., Bou-Zeid, E. (2024) Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants. Environmental Science and Technology.
  2. Gerges, F., Boufadel, M. C., Bou-Zeid, E., Nassif, H., & Wang, J. T. (2024). Long-term prediction of daily solar irradiance using Bayesian deep learning and climate simulation data. Knowledge and Information Systems, 66(1), 613-633.
  3. Gerges, F., Assaad, R. H., Nassif, H., Bou-Zeid, E., & Boufadel, M. C. (2023). A perspective on quantifying resilience: Combining community and infrastructure capitals. Science of the Total Environment, 859, 160187.
  4. Gerges, F., Boufadel, M. C., Bou-Zeid, E., Darekar, A., Nassif, H., & Wang, J. T. (2023). Bayesian multi-head convolutional neural networks with Bahdanau attention for forecasting daily precipitation in climate change monitoring. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, Grenoble, France. (pp. 565-580). Cham: Springer Nature Switzerland.
  5. Gerges, F., Boufadel, M. C., Bou-Zeid, E., Nassif, H., & Wang, J. T. (2022). Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data. World Scientific Annual Review of Artificial Intelligence, 2250001.