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Resume

(601 Words, 4 Minutes)

Alexander Reese Brown

2020-2022: M.S., Spatial Data Science, University of Southern California - Los Angeles, CA.
2018-2019: Multi-Subject Teaching Credential, Loyola Marymount University - Los Angeles, CA.
2012-2016: B.A., Philosophy, Minor Computer Science, Lewis & Clark College - Portland, OR.

Outway, Boulder, CO., 2021-present

Spatial Data Engineer/Scientist

Developed scalable Python-based modeling, cleaning, and ingestion/integration scripts for varied spatial (vector/raster) data sources, using a number of open-source spatial data libraries such as GeoPandas, GDAL, Fiona, Shapely, etc.
Integrated two large (100k+ features) statewide public lands datasets (COMaP, CORTEX) into an existing database by developing a map matching heuristic algorithm to merge similar lines/polygons.
Deployed a containerized Postgres/PostGIS based dynamic tile server to provide near real-time visualization of a spatial table with over 500K features, and configured a reverse proxy cache to reduce database load by up to 300x.
Modeled and developed a database schema to allow Google Drive style sharing of map features, then implemented a serverless API to provide CRUD endpoints.

University of Southern California, Los Angeles, CA., 2020-2022

Teaching Assistant

Graded assignments, provided feedback for students, and held office hour for graduate and undergraduate level courses in the Viterbi School of Engineering and the Dornsife Spatial Sciences Institute
Courses: Principals of Programming for Data Science, Remote Sensing for GIS

STEM Prep Schools - Teach for America, Los Angeles, CA., 2018-2020

6th Grade Math and Science Teacher

Selected from approximately 45,000 highly competitive nationwide applicants to join a national teacher corps of recent college graduates and professionals who commit two years to teach in under-resourced public schools.
Participated in an intensive training program including formal graduate-level schooling and other professional development activities to develop the skills and knowledge needed to achieve significant gains in student achievement and work on critical issues in the field of education.
Worked as full time teacher, developing and implementing engaging and rigorous math and science curriculum to prepare students for upper education and future careers in STEM-related fields.

University of Southern California

Machine Learning for Data Science - Explored various advanced topics in modern statistical/algorithmic machine learning such as supervised/unsupervised learning, regularization, Bayesian techniques, deep learning (RNNs, LSTMs, SOM) and trained/evaluated models using real world datasets.
Foundations of Data Management - Studied principles of data management, including data modeling, query optimization/execution, and transaction management using prominent SQL/NoSQL databases.
Spatial Data Science - Investigated the theory and practice behind statistical/algorithmic methods that complement and enhance traditional approaches to spatial analysis including spatial summary statistics, spatially explicit regression, regionalization/clustering, and spatial optimization.
Data Science at Scale - Learned modern techniques to aid in scalable data processing and storage solutions, such as distributed computing frameworks like Spark/Hadoop and complementary database systems like Hive, HBase, and Cassandra.
GIS Programming and Customization - Explored the programmatic customization of both proprietary GIS software and open source tools for geospatial analysis, modeling, and visualization.
Remote Sensing Applications and Emerging Technologies - Researched the principles, technical characteristics, and applications of emerging technologies in remote sensing.

Lewis & Clark College

Algorithm Design and Analysis - Implemented and analyzed key algorithms and data structures fundamental to the computational sciences, including trees, graph theory, string searching, sorting, bit vectors, hashing, heaps, and dynamic programming.
Computer Architecture and Assembly Language - Explored the computer-design concepts and assembly language inherent to the x86 architecture, including ALUs, instruction sets, memory addressing modes, parameter passing, macro facilities, the binary representation of information, pointers.
Introduction to Data Science - Learning the fundamental techniques of modern data science including data cleaning/wrangling, visualization, feature engineering, and machine learning.
Computational Mathematics - Studied computational implementations behind classic mathematical problems found in economics, engineering, and statistics, including differentiation, integration, linear/nonlinear systems, ordinary differential equations, approximation, curve fitting, gradient descent, and dimensional compression.

Languages: Python (advanced), SQL(advanced), R (intermediate), Node.JS (intermediate), Java (intermediate), C/C++ (basic)
Libraries/Frameworks: GeoPandas, Pandas, Polars, Numpy, Scikit-Learn, PySal, StatsModels, Tensorflow/Keras, XGBoost, Hadoop, Spark, ArcPy, GDAL, PyGeos, Fiona, Shapely, Selenium, Beautiful Soup, Plotly, Matplotlib, Seaborn, Flask, Node.JS, Sequlize
GIS Applications: ArcGIS Pro, QGIS, IDRISI, Pix4D
Databases: Postgres/PostGIS, MySQL, MongoDB, HBase
AWS Cloud: EC2, ECS, Lambda, RDS Aurora, S3, Cloudfront

Recipient of the 2018-2020 Americorps Segal Education Award for successfully completing a year of service in AmeriCorps
Dean’s list four consecutive years at Lewis & Clark College
Invited and accepted into Gamma Theta Upsilon (Geographic Honor Society)