Dr. Amalia Kokkinaki has been an Assistant Professor in the department of Environmental Science since 2016. She is an environmental engineer with expertise in groundwater flow, fate and transport of contaminants and biological treatment of organic pollutants. Her research is in the nexus of mathematical and statistical modeling of the physical, chemical and biological processes governing complex environmental systems. She was a postdoctoral researcher in the Department of Civil and Environmental Engineering at Stanford University from 2014-2016.
Amalia teaches undergraduate courses in the department of Environmental Science, graduate courses in the master’s program (MSEM) and in the Geospatial Analysis Lab (GsAL). Amalia is also in the curriculum committee for the new Engineering program at USF and is the faculty advisor for the Environmental Engineering and Science student club at USF.
In her research, Amalia uses statistical and mathematical modeling to simulate the behavior of pollutants in environmental systems. Her expertise is in understanding and predicting how pollutants get transported in the subsurface and how they change as they make their way through aquifers. She works with and develops computer models that simulate these processes and predict where, when and how much of the pollutant will be present in the environment. Some examples include: Modeling the fate of carcinogenic organic contaminants resulting from inappropriate waste disposal practices; Modeling of injection of supercritical CO2 (a liquid version of CO2) into deep geological formations to reduce CO2 emissions from industry; Modeling of groundwater levels near pumping wells and simulating the possibility of over-pumping and depleting groundwater resources. These models can be used to create engineering designs to clean up contaminated groundwater. Amalia combines these mathematical models with statistical models (a process called filtering) to help better determine the properties of the simulated aquifers. By combining statistics with mathematical models, we can try to quantify uncertainty and the value of information.
Amalia’s former students, ENVS alumni Letizia Tjiupek (ENVS 2018) and Matthew Hanson (ENVM 2017) are working on simulating the particular matter (PM) emissions from vehicles in Sacramento, and modeling the potential reduction of PM by urban forests. Her current research assistant, Abhinav Agrawal (ENGY 2019) is working on modeling and optimizing the electrification of developing urban areas.
Amalia is a big advocate of the value of learning how to program. Learning how to code is a skill that extends far beyond an employable skill: it is useful in developing process-oriented thinking, organizing and processing information and building a systems-level understanding of complex interrelated processes. All of these skills are critically needed in studying environmental phenomena. In Amalia’s research lab, my students learn how to use Matlab, Python or R for their various research projects. All of them have started from knowing little about programming to being able to confidently use, modify and develop code to run environmental models.
Interested in learning about environmental models or analyzing environmental data? Contact me!
- PhD, University of Toronto, Civil & Environmental Engineering, 2013
- MA Sc, University of Toronto, Civil & Environmental Engineering, 2007
- BS & MSc, Technical University of Crete, Environmental Engineering, 2005
- Groundwater Modeling
- Mathematical and Statistical Modeling
- Computer Programming
Lee, J., Kokkinaki, A., Kitanidis, P.K. (2018), Fast Large-Scale Joint Inversion for Deep Aquifer Characterization Using Pressure and Heat Tracer Measurements, Transport in Porous Media, 123(3), pp. 533-543
Ghorbanidehno, H., Kokkinaki, A., Li, Y. J., Darve, E. F. and Kitanidis, P. K. (2017) Optimal estimation and scheduling in aquifer management using a Spectral Linear Quadratic Gaussian Control Method, Advances in Water Resources, 110, pp. 310-318
Li, J. Y., Kokkinaki, A., E. F. Darve, and P. K. Kitanidis (2017), Smoothing-based compressed state Kalman filter (sCSKF) for joint state-parameter estimation: applications in reservoir characterization and CO2 storage monitoring, Water Resources Research, 53 (8) pp. 7190-7207
Ghorbanidehno, H., Kokkinaki, A., Li, Y. J., Darve, E. F. and Kitanidis, P. K. (2015), Real time data assimilation for large-scale systems: The Spectral Kalman Filter, Adv. in Water Resources, 86, pp.260-272
Li, J. Y., Kokkinaki, A., H. Ghorbanidehno, E. F. Darve, and P. K. Kitanidis (2015), The Compressed State Kalman Filter for nonlinear state estimation: Application to large-scale reservoir monitoring, Water Resources Research, 51(12), pp. 9942-9963
Kokkinaki, A., Werth, C.J. and Sleep, B.E. (2014) Comparison of upscaled models for multistage mass discharge from DNAPL source zones, Water Resources Research, 50(4), pp. 3187-3205
Kokkinaki, A., O`Carroll, D.M., Werth, C.J. and Sleep, B.E. (2013) An evaluation of Sherwood-Gilland correlations for NAPL dissolution and their relationship to soil properties, Journal of Contaminant Hydrology, 155, pp. 87-98
Kokkinaki, A., O`Carroll, D.M., Werth, C.J. and Sleep, B.E. (2013) Coupled simulation of DNAPL infiltration and dissolution in three-dimensional heterogeneous domains: process model validation, Water Resources Research, 49(10), pp. 7023-7036