Andre Unger, PEng

André Unger develops and applies numerical methods and algorithms to solve a wide variety of problems involving Earth Sciences and Geological Engineering

Professor Unger studies how municipalities can sustainably manage their water resources and infrastructure using a variety of tools, including groundwater and surface water modeling, and water infrastructure asset management tools.

Professor Unger also studies the financial and health risks associated with re-developing hazardous waste sites known as "brownfields" using alternative clean-up techniques.

Professor Unger also studies aspects of signal analysis and spectrogram inversion with specific application to speech recognition and synthesis.

• Computational hydrogeology
• Water resource supply and infrastructure management, including financial sustainability
• Signal analysis including speech recognition and synthesis

Financially sustainable water resource supply and infrastructure: Professor Unger's research on this topic focuses on assessing the risk associated with securing the future water supply using groundwater/surface water hydrologic models at the watershed scale. The Province of Ontario has recently issued legislation mandating that all municipalities with a permit to issue water to residents must ensure that their water system is self-financing in order to have their permit renewed. In other words, the fee consumers pay per cubic meter of water consumed must be sufficient to cover all expenses needed to operate and maintain the system in a safe and reliable manner. While this seems like an obvious requirement, it is a fundamental paradigm shift in how municipalities operate. Professor Unger is also investigating project finance strategies to rehabilitate and expand the infrastructure needed to deliver and collect water to and from households given the uncertainty in population growth and the residential/commercial demand for water; the uncertainty in commodity prices consumed during all capital works and O&M activities used to maintain the infrastructure; and the uncertainty in the degradation rate and operable lifespan of the infrastructure. Signal Analysis: Professor Unger's research on this topic focuses on developing numerical algorithms for speech synthesis with human-like emotion, intonation and phrasing. These algorithms represent a mechanistic speech synthesis technology that minimizes the need for machine learning to simply collecting/analyzing/generating context specific information. Key features of this technology involve spectrogram construction and inversion approaches.

• 1995, Doctorate Earth Sciences, University of Waterloo, Canada
• 1991, Master of Science Earth Sciences, University of Waterloo, Canada
• 1990, Bachelor of Applied Science (BASc) Geological Engineering, University of Waterloo, Canada

• Professional Engineer, Province of Ontario, Canada

• Waterloo Artificial Intelligence Institute
• Water Institute

• Wu, Q., S. Lin, A. Unger (2023) Modelling multi-material structural patterns in tectonic flow with a discontinuous Galerkin level set method, Journal of Geophysical Research: Solid Earth; 10.1029/2023JB026806
• Enouy, R.W., K.W. Walton, I.I Malton, K.S. Sra, N.N. Sihota, E.J. Daniels, and A.J.A. Unger (2022) A mechanistic derivation of the Monod bioreaction equation for a limiting nutrient, Journal of Mathematical Biology 84 (62).
• Mohammadifardi, H., M. Knight and A. Unger (2022) Sustainability assessment of strategic asset management decisions on municipal water infrastructure systems: Framework and application. AWWA Water Science, 4(4), e1297.
• Ganjidoost, A., MA Knight, AJA Unger and CT Haas (2021) Performance modeling and simulation for water distribution networks, Frontiers in Water 3, 718215.
• Enouy, R., A.J.A. Unger, and R. Rehan (2019) Advective-dispersive transport of residential water consumption. Journal of Hydroinformatics. doi: 10.2166/hydro.201930

• Enouy, R.W. and A.J. Unger (2020), System and method of digital sound synthesis, signal transmission, and subsequent analysis using spectrogram inversion. United States Patent No. US 17/317,296 May 11 2020.
• Enouy, R.W. and A.J. Unger (2019), System and method of pre-processing discrete datasets for use in machine learning. United States Patent Publication No. US 16/272,442 Dec. 12, 2019.