CV

Ph.D.

University of New Mexico, School of Engineering

Computer Science

M.B.A.

University of New Mexico, Anderson School of Business

Business Management

B.S.

University of New Mexico, School of Engineering

Computer Science

R&D Graduate Intern

Sandia National Laboratories

Year-round intern in the Scientific Machine Learning department.

• Participated in the Lab Driven Research & Development project CLDERA, which seeks to develop tools to identify source to impact pathways in climate systems.
• Researched the application of causal discovery, specifically the PC and PCMCI algorithms, for identifying causal pathways from the 1991 Mt. Pinatubo eruption.
• Developed the CaStLe algorithm for constructing spatiotemporal causal graphs of local dependencies in climate data.
• Researched random forest machine learning techniques for learning about emergent dynamics in climate models.
• Used feature importance analysis to make comparisons between observed and simulated data to look for the simulations' faults

Graduate Research Assistant

University of New Mexico

A research assistant under Dr. Lydia Tapia and Dr. Marina Kogan.

• Robotics under Dr. Tapia
• Computational sociology under Dr. Kogan

Owner

Swarming Technologies LLC

Ran a business full-time manufacturing, repairing, and developing autonomous ground-based robots.

• Robots, known as 'Swarmies' were designed for swarm robotics and autonomous robotics research and education.
• Swarmies were featured in the NASA Swarmathon, NASA Minds competition, and CS4All NM.

Robotics Engineer and Designer

NASA Swarmathon & Moses Biological Computation Lab

Designed and developed autonomous, swarming robots called Swarmies. The robots were a part of a nation-wide, college-level robotics competition called the NASA Swarmathon. Development included designing with Autodesk Inventor and manufacturing parts with SLS, SLA, and FDM 3D printing, as well as electronics development.

• Analyzed data to track progress and success determinants using MySQL and Python’s numpy, scipy, and pandas.
• Developed automated deployment for code on robots using Ansible and Docker.
• Conducted swarm robotics research using Arduino/iPod Touch-controlled robots, iAnts, using a genetic algorithm to tune behavior.

Software Engineering Intern

Intel Corporation

Product surveying, QA, troubleshooting, and testing.

• Arduino/Galileo programming and debugging.
• Design and construction of a robotic car, controlled by the Intel Galileo Gen 2.
• Developed a Arduino/Intel Galileo controlled robot that included: input given via Bluetooth from Android phone and multiple sensors for line following and obstacle avoidance.

Best Talk Prize

European Seminar on Computing (ESCO)

3rd Place Poster Prize

Department of Energy Conference on Data Analysis (CoDA)

Adaptive Ski Instructor

Adaptive Sports Program New Mexico Inc

Teach skiing to people with various disabilities.

Troop 9 Board of Review Member

Boy Scouts of America

Attend board of review meetings to assist in scout advancement.

Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change

Journal of Computational and Applied Mathematics

In September of 2020, Arctic sea ice extent was the second-lowest on record. State of the art climate prediction uses Earth system models (ESMs), driven by systems of differential equations representing the laws of physics. Previously, these models have tended to underestimate Arctic sea ice loss. The issue is grave because accurate modeling is critical for economic, ecological, and geopolitical planning. We use machine learning techniques, including random forest regression and Gini importance, to show that the Energy Exascale Earth System Model (E3SM) relies too heavily on just one of the ten chosen climatological quantities to predict September sea ice averages. Furthermore, E3SM gives too much importance to six of those quantities when compared to observed data. Identifying the features that climate models incorrectly rely on should allow climatologists to improve prediction accuracy.