Robin Yancey, Ph.D.

Dr. Robin Yancey is a computer scientist, educator, and applied machine learning researcher specializing in artificial intelligence, machine learning, deep learning, computer vision, and data science. She holds a PhD and two master’s degrees from the University of California, Davis, in Computer Science and Electrical Engineering.  As an Engineering Data Science Adjunct Faculty at Solano Community College, she developed and taught courses in Python, Machine Learning, Deep Learning, R, and Assembly Language while helping to kickstart their Data Science and Artificial Intelligence program. Previously, she served as a Machine Learning instructor at UC Davis and as an Applied Machine Learning Postdoctoral Researcher at Lawrence Livermore National Laboratory, where she developed deep learning and computer vision systems for the National Ignition Facility. She has published many peer-reviewed research papers in machine learning, deep learning, computer vision, and scientific computing and has completed applied machine learning consulting projects with industry partners. Her current work also includes research and professional development in ethics in data science, with a focus on responsible and equitable approaches to data and AI. She is currently a Co-PI of CONFIDE (Community of Networking Faculty Investigating Data Ethics), a faculty learning program focused on integrating data ethics into data science education. Dr. Yancey also mentors students in machine learning, helping them develop technical skills, academic pathways, portfolios, and preparation for careers in computing and AI.

Degree & Academic Institution:

• Ph.D. Computer Science
  University of California, Davis
• MS, Computer Science
  University of California, Davis
• MS, Electrical Engineering
  University of California, Davis
• Post Doc, Applied Machine Learning Researcher
  Lawrence Livermore National Lab

Courses Taught:

• CS 250 - Introduction to Programming
• CS 250L - Introduction  to Programming Lab
• MATH 201 - Calculus I

Publications:

• Yancey, R. Deep Learning Model for Prediction of Laser-Induced Damage Growth on NIF Optics. SPIE Laser Damage, 2023.
• Yancey, R., et al. Temporally and Spatially Resolved Photoluminescence of Laser-Induced Damage Sites of Fused Silica. SPIE Laser Damage, 2023 & 2024.
• Yancey, R., et al. Wide-Field Probing of Silica Laser-Induced Damage Precursors by Photoluminescence Photochemical Quenching. Optics Letters, 2023.
• Yancey, R. Parallel YOLO-Based Model for Real-Time Mitosis Counting. WSCG, 2022.
• Yancey, R. Deep Feature Fusion for Mitosis Counting. PRML, 2022.
• Yancey, R. Deep Learning for Localization of Mixed Image Tampering Techniques. IEEE IWSSIP, 2022.
• Yancey, R. Machine Learning Methods for Current Image Fraud Detection. Statistical Methods in Imaging (UCI), 2019.
• Yancey, R. Modernizing k-Nearest Neighbors. SDSS, 2020.
• Yancey, R., et al. The R Language: A Powerful Tool for Taming Big Data. Springer Encyclopedia of Big Data Technologies, 2018.
• Yancey, R. Fast Computation of Large-Scale Mixed Effects Models. JSM Proceedings, 2018.
• Yancey, R. Fast, General Parallel Computation for Machine Learning. ICPP P2PS, 2018.