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Data Scientist

San Francisco, California, United States
February 02, 2018

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**** - **** ****** ***** **********, Ph.D.

Hydroinformatics with focus in Machine Learning, Data Assimilation, and Statistics


****- ******* **** *********, ******** McDonald Engineers (MME), San Francisco, CA

Internet of Things (IoT): Optimizing wastewater treatment plants operation

Machine Learning: Develop predictive models using machine learning


2016 – 2017 Postdoctoral Researcher, Oregon State University

Artificial Intelligence: Creating real-time emergency response application

Optimization: Developing Decision Support System for water, food, and energy nexus

2012 - 2016 Research assistant, Oregon State University

Ensemble data assimilation: Incorporating satellite data into numerical models

Machine Learning: Simulating complex water systems

Statistic: Uncertainty and sensitivity analysis

Developing web-based technologies to plan and design conservation practices on their landscape

2016 - 2016 Data Scientist Intern, National Water Center, Tuscaloosa, AL

Ensemble data assimilation: Working with big data and updating continental-scale numerical models

2014 – 2014 Data analyst Intern, Oregon Department of transportation (ODOT)

Machine Learning: Creating classification models to improve safety in construction zones


Machine Learning: Regression, classification, clustering, Associate Rule learning (ARL), Reinforcement Learning (online learning), Natural Language Processing(NLP)

Deep Learning: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Self-Organizing Maps, Deep Boltzmann Machine

Optimization: Gradient Descent, Stochastic Gradient Descent, Genetic Algorithm

Python: Scikit-Learn, Numpy, SciPy, Pandas, matplotlib

Data Assimilation: Kalman Filter, Ensemble Kalman Filter

Statistics: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel PCA

SQL database


Numerical modeling and time series analysis


Javaheri, Amir, “Deep learning in watershed modeling: Using Artificial Intelligence to develop real time flood maps.” Journal of Hydrology, 2017 (in review).

Javaheri, Amir, M. Babbar-Sebens, and R. N. Miller, “An adaptive Ensemble Kalman Filter for assimilation of multi-sensor, multi-model water temperature observations into hydrodynamic model of shallow rivers.” Journal of Hydrology, 2017.

Javaheri, Amir, M. Babbar-Sebens, J. Alexander, J. Bartholomew, and S. Hallet. “Uncertainty analysis and global sensitivity analysis of water age and water temperature for disease management.” Journal of Hydrology, 2017.

Javaheri, Amir, M. Babbar-Sebens, and R. N. Miller, “From skin to bulk: An adjustment technique for assimilation of satellite-derived temperature observations in numerical models of small inland water bodies.” Advances in water resources, 92, 284-298, 2016.


Machine Learning; Hands-On Python & R

Deep Learning; Hands-on Artificial Neural Networks

Engineer-in-Training (EIT)


Outstanding Graduate Student Award, 2017

National Water Center Innovators Program Summer Institute of 2016

CUAHSI Travel Grant Award for the 2016 CUAHSI Biennial Symposium

Highest GPA among students of Civil Engineering, Isfahan University of Technology


“An ensemble Kalman Filter framework to assimilate snow albedo and snow water equivalent data from heterogeneous sources and update streamflow prediction in mountainous regions”, (PI: Amir Javaheri), NASA Jet Propulsion Laboratory (JPL), submitted November 2016.

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