Bahareh Fallah

WORK HISTORY

EDUCATION

ABOUT BAHAREH FALLAH

Environmental Modeling Specialist with Ph.D. in Environmental Systems Engineering and over 7 years of work experience in R&D projects in developing impactful data-driven solutions to real-world environmental challenges, employing advanced techniques such as data analytics, spatial statistics, machine learning methods, and Geographic Information Systems (GIS) approaches.Skills and Achievements: • Applied spatial statistics and machine learning methods to evaluate and rank the natural and people-related indicators of the environmental stress in the oil sand area of Alberta. Performed the assessment of landscape spatial and temporal variability and changes. • Developed a prototypical computational model using Object-Oriented Programming and Agent-Based modeling in Mesa, a Python library, to simulate coupled human-water interactions for analyzing and mitigating water-related threats. • Proposed a new geostatistical tool to better evaluate groundwater quality near an urban landfill that can benefit planners and decision-makers to devise policy guidelines for efficient management of their water resources. • Developed a new landfill gas estimation tool utilizing a two-stage time series neural network that can be used as an effective landfill gas modeling tool, especially for landfills with incomplete datasets.• Experienced in working with first-order decay models such as LandGEM and IPCC for landfill gas evaluation.• Experienced in environmental data analysis, visualization, and predictive modeling, including time series modeling (e.g, ARIMA), machine learning techniques, and geostatistical and network analysis in ArcGIS.• Performed dashboarding and data visualization using Streamlit. • Worked effectively in an agile team (tools: JIRA, Git). • Passionate about finding patterns in data, prediction, and optimization to find answers to complex and impactful questions. Technical Proficiency:• Python (SciPy, GeoPandas, PyTorch, TensorFlow, Scikit-Learn, Matplotlib, Seaborn, Plotly), Object-Oriented Programming and Agent-Based modeling in Mesa, Power BI, MATLAB, SQL, Git, Docker, Jupyter Notebook, Anaconda, Google Cloud Platform, IBM Cloud, ESRI suite (ArcGIS pro).• Deep Neural Networks, Long-Short-Term Memory (LSTM) model, Random Forest, Support Vector Machines, Principal Component Analysis, K-Nearest Neighbors, Regression Modeling, Clustering and Classification.