Data Computer Science

Ritche Long

206-***-**** *********@*****.***

linkedin.com/in/ritcheclong github.com/fsharpasharpinfinity PROJECTS

The Nature Conservancy Fisheries Monitoring

• Developed a deep learning model to classify fish images supplied by fishing boats for endangered fish species.

• Ranked in the top 14% amongst 2,293 data science teams. King County Housing

• Developed an ensemble machine learning model to forecast housing prices for King County properties in 2015.

• Engineered new features to increase model’s r2 performance from .9031 1 to .9127, and halved log-error from 16 to 8.

• Created an interactive web application for further exploratory analysis and visualization. Taiwanese Credit Default

• Performed analysis on default likelihood in Taiwanese credit clients.

• Established classification model with .821 prediction accuracy. EXPERIENCE

Industrial Design Clinic

Professional Worker I

Python/Embedded Sys.

June 2015 - May 2017

Pullman, WA

Dept. of Mathematics

Research Assistant

C++/Python/Linux

July 2016-May 2017

Pullman, WA

HPCBioinformatics Lab,

SHODOR Research Intern

C++/Bash/Linux

Jun 2015-Nov 2016

Urbana-Champaign, IL

EXTRACURRICULARS

Analysis + Data Group Undergraduate Scholar

Coursera Deep Learning Specialization

Management and Information Systems Club

WSU ACM Hackathon Finalist (2015)

• Implemented parallel clustering algorithms for use in topological data analysis methods on NCSA Blue Waters supercomputer.

• Documented results and code, presented findings to head researchers.

• Wrote scripts for parallel computing automation, and general statistical data analysis for plant phenomics.

• Engaged in tests on model’s predictive power with interdisciplinary teams.

• Developed/tested first revision of embedded UAV autopilot using reinforcement learning for orangutan and painted wild dog conservation under Agile/Scrum methodologies.

• Mined and decoded noisy signal data from animal transceivers, performed exploratory data analysis, and reported findings to cross-discipline team members.

• Coordinated with engineering teams to interface mechanical hardware with autopilot software and reduce autopilot costs from $9,000 to $500.

• Converted MATLAB prototypes of breast cancer simulations to parallelized C++ code using MPI.

• Tested/debugged prototyped parallel code for concurrency errors.

• Engaged in statistical modeling and Bayesian inference to create a parameter estimation method for the model.

• Reduced job runtime of simulation model by 30%.

SKILLSET

Languages: Python, Scala, C++, C#, Bash, SQL

Data Visualization: Seaborn, matplotlib, PowerBI, Tableau Machine Learning: Scikit-Learn, mlxtend, xgboost, keras, Azure ML, TensorFlow

EDUCATION

B.S. Computer Science, May 2017

Washington State University,

Pullman, WA

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