CV

Download my CV here (last updated Dec 2023)

Education

Harvey Mudd College, Claremont, CA | Expected 2024
B.S. Mathematics and Computer Science, Emphasis in Environmental Analysis
Major GPA: 4.00, Overall GPA: 3.95

Select Coursework
Mathematics: Dynamical Systems, Mathematical Modeling, Stochastics, Differential Equations, Linear Algebra
Computer Science: Neural Networks, Scientific Computing, Algorithms, Programming Languages, Data Structures
Earth Science: Atmosphere and Ocean Dynamics, Climate Science, Oceanography, Global Climate Change

Publications

2023
Yik, W., Sonnewald, M., Clare, M. C. A., Lguensat, R. (2023). Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning. NeurIPS Workshop: Tackling Climate Change with Machine Learning. https://arxiv.org/abs/2310.13916

Hom, C., Yik, W., Montañez, G. D. (Accepted, 2023). Finite-Sample Bounds for Two-Distribution Hypothesis Tests. IEEE International Conference on Data Science and Advance Analytics (DSAA). https://doi.org/10.1109/DSAA60987.2023.10302643

Yik, W., Silva, S. J., Geiss, A., Watson-Parris, D. (Accepted, 2023). Exploring Randomly Wired Neural Networks for Climate Model Emulation. Artificial Intelligence for the Earth Systems (AIES). https://doi.org/10.1175/AIES-D-22-0088.1

2022
Yik, W., Silva, S. J., Geiss, A., Watson-Parris, D. (2022). Exploring Randomly Wired Neural Networks for Climate Model Emulation. NeurIPS Workshop: Tackling Climate Change with Machine Learning. https://www.climatechange.ai/papers/neurips2022/36/paper.pdf

Yik, W., Serafini, L., Lindsey, T., Montañez, G. D. (2022). Identifying Bias in Data Using Two-Distribution Hypothesis Tests. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES). https://doi.org/10.1145/3514094.3534169

Research experience

University of California, Davis, CA | Aug 2023 - Present
Student Researcher, Computational Climate and Ocean Group

• Advisor: Maike Sonnewald
• Identifying and tracking Southern Ocean dynamics under climate change using neural networks

Lawrence Livermore National Laboratory, Livermore, CA | Aug 2023 - Present
Clinic Project Team Member, Harvey Mudd College Clinic Program

• Liaison: Robert Blake, Advisor: Naim Matasci
• Investigating empirical scaling of scientific machine learning emulators

University of Southern California, Los Angeles, CA | May 2022 - Present
Undergraduate Researcher, Atmospheric Composition and Earth Data Science Group

• Advisor: Sam Silva
• Exploring the utility of randomly wired neural networks for climate model emulation
• Investigating methods for enforcing fairness and equity in neural climate emulators

NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ | May 2023 - Aug 2023
Research Intern, Ocean and Cryosphere Division

• Advisor: Maike Sonnewald, Stephen Griffies
• Applied deep ensemble learning methods for inferring subsurface ocean dynamics
• Improved the interpretability of models using explainable AI techniques such as layer-wise relevance propagation and Shapley additive explanations

Harvey Mudd College, Claremont, CA | May 2021 - May 2023
Undergraduate Researcher, AMISTAD Machine Learning Lab

• Advisor: George Montañez
• Implemented novel hypothesis tests to systematically identify bias in machine learning training data
• Derived mathematical finite-sample bounds for two-distribution hypothesis tests

Idaho National Laboratory, Idaho Falls, ID | Aug 2020 - Sept 2020
Research Intern, Energy Innovation Laboratory

• Advisor: Christopher Zarzana
• Tested separation and content analysis methods for ligands and biomass using gas chromatography and pyrolysis
• Utilized liquid chromatography and mass spectrometry to accelerate ligand sample production

Contributed Talks and Posters

Ocean Sciences Meeting, Talk: Explainable Machine Learning for Inferring Subsurface Ocean Dynamics, Upcoming Feb 2024.

NeurIPS Workshop: Tackling Climate Change with Machine Learning, Poster: Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning, Dec 2023.

American Geophysical Union Fall Meeting, Talk: Enforcing Equity in Neural Climate Emulators, Dec 2023.

IEEE International Conference on Data Science and Advanced Analytics, Talk: Finite-Sample Bounds for Two-Distribution Hypothesis Tests, Oct 2023.

Harvey Mudd College Student Symposium, Poster: Explainable Machine Learning for Inferring Subsurface Ocean Dynamics, Sept 2023

NOAA Science and Education Symposium, Talk: Explainable Machine Learning for Inferring Subsurface Ocean Dynamics, Aug 2023.

NeurIPS Workshop: Tackling Climate Change with Machine Learning, Poster: Exploring Randomly Wired Neural Networks for Climate Model Emulation, Dec 2022.

Harvey Mudd College Student Symposium, Poster: Exploring Randomly Wired Neural Networks for Climate Model Emulation, Sept 2022.

AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, Talk and Poster: Identifying Bias in Data Using Two-Distribution Hypothesis Tests, Aug 2022.

Awards and Honors

Nominee, Outstanding Undergraduate Researcher Award (application pending) | 2023
Computing Research Association

Ernest F. Hollings Undergraduate Scholarship | 2022
National Oceanic and Atmospheric Administration

Dean’s List | Spring 2021 - Fall 2022
Harvey Mudd College

National Merit Scholarship | 2020
National Merit Scholarship Corporation

Teaching Experience

Harvey Mudd College, Claremont, CA | Aug 2022 - Present
Mathematics Academic Excellence Facilitator

• Courses: Differential Equations, Discrete Mathematics, Linear Algebra, Probability and Statistics, Calculus
• Nominated by faculty to hold weekly tutoring sessions for groups of 10-50 student
• Hosting weekly facilitator meetings to improve mentor and tutor sessions across the college

Harvey Mudd College, Claremont, CA | Aug 2021 - May 2022
Teaching Assistant

• Courses: Computability and Logic, Discrete Mathematics, Introduction to Computer Science
• Held weekly tutoring sessions for groups of 5-30 students and graded homework assignments

Skills

Programing Languages: Python, R, C++, Java, MATLAB, Haskell
Machine Learning/Data Science: Tensorflow, PyTorch, Scikit-learn, SciPy, NumPy, Xarray, Pandas
Software/Web Development: Git, Docker, Visual Studio Code, Eclipse, Flask, HTML, CSS