Resume
Skills
& Expertise
• Programming Languages Python, SQL, Java
• Data Visualization Matplotlib, Geoplotlib, Plotly, Streamlit, Folium
• Data Analysis Libraries Pandas, Numpy, Scipy
• Machine Learning Scikit-learn, NLTK, OpenCV, TensorFlow, Keras, Pytorch, CNN, MLP, RNN
• Geospatial Tools QGIS, Rasterio, osgeo/gdal, shapely, ESRI, Google Earth Engine, Geopandas
• Other Remote Sensing, Bash, Ubuntu, GIT,GitHub, datasets, Labelbox, Image Segmentation, Docker, Linux, AWS, xarray, Google Cloud Computing
Education
2018-2022
California State University, Stanislaus
Bachelor's of Science, Physics
Minor in Mathematics
2016-2018
San Joaquin Delta College
Associate's of Science, Criminal Justice
08/2022-10/2022
Data Science: Visualize Soil Wetness in California
• Created and managed a SQL database
• Created queries on large data sets in SQL.
• Collected, cleaned, and prepared data for analysis using Pandas and Geopandas.
• Synthesized data and used Streamlit to create an interactive visual of the soil wetness in California.
• Using folium through Streamlit, created an interactive choropleth map that clearly features the surface soil wetness values as well as shade the map to visualize how dry or wet the soil is.
• Applied seasonal time series analysis using the statsmodel package in Python.
• Deploy the Streamlit app using Docker
• Used GitHub for version control.
• Experienced strong software engineering practices like debugging and profiling, version control, and optimization
04/2022 - 08/2022
Geospatial Data Science
• Developed automated fuel source classification algorithms for WRF-SFIRE simulations using satellite imagery.
• Used GIS to train machine learning models to evaluate fuel sources and vegetation from aerial images, satellite images and remote sensing data.
• Developed of remote sensing and machine learning pipelines.
• Constructed tools and visualizations that produce high quality results to discuss with scientists, researchers, and engineers.
• Evangelized spatial data science through documentation, presentations, and visualizations.
• Optimized models to run efficiently on large amounts of geospatial and remote sensing data for pattern recognition and predictive modeling.
• Accessed the quality of machine learning models using statistical frameworks and experiments to validate their performance.
• Applied machine learning and statistics fundamentals to remote sensing, forest science, and vegetation index (NDVI).
• Synthesized academic literature and applied those learnings to model and experiment design.
06/2022-08/2022
Machine Learning Engineer(Task Leader)
• Built a system to detect different elements in 2D building architectural diagrams using Machine Learning and image processing techniques.
• Lead a team of engineers to create a model to accurately perform element detection and dimension estimation.
• hipped quality software by working effectively in an agile, interactive, and creative team environment.
• Reviewed and recreated the work presented to annotate images with 150+ objects using semantic segmentation
• Synthesized data into actionable insights.
• Object Detection(YOLO V5), Semantic Segmentation(Mask RCNN), OCR( EasyOCR EAST detection)
• Created full stack pipeline using Github integration in a virtual machine and implemented deployment pipeline.
Green House Gass Emissions Analysis
• Collected data from Kaggle and the United Nations on UNData website.
• Used pandas to clean and prepare data for exploratory data analysis
• Developed an algorithm that plots the green house has emissions for each country and properly labels and formats the data.
• Created an interactive visual of the data using Streamlit
2018-2022
Astrophysics Research Assistant
• Four years of extensive experience writing and debugging a program in Python and using it for statistical modeling and scientific data analysis.
• Experienced software development applied to solving scientific problems
• Developed a program that takes user input of metal abundance and returns the supernova make-up of the observed object.
• Delegated tasks to other researchers to create a collaborative and efficient work environment.
• Communication methods: documentation, written reports, posters, and oral presentations
• Efficiently organized data collection.
• Experienced communicating mathematical concepts, analytical results, and data-driven insights to both technical and non-technical audiences
Present
Geospatial Data Scientist
• Develop geospatial data-driven algorithms to make actionable insights for wildfire risk.
• Apply statistical models and visualization techniques to optimize risk scoring algorithms.
• Collaborate with the team for data-driven decision making, model building, and meeting deadlines.
• Using Google Cloud Console, I used tools such as Vertex Ai and BigQuery SQL to analyze and process large datasets.
• Demonstrate ability to write clean, concise, tested, and maintainable code in Python.