Skunkworks Successful Project Highlights
For select research projects that have concluded we have created a brief Project Highlight which summarizes the goals, structure, and outcomes of the project. Generally these projects projects will also include papers, presentations, etc. which are also included in the table of products below.
Skunkworks Product Posts
Skunkworkers have helped contribute to a new work using machine learning to predict how to make better metallic glasses. See paper here. Afflerbach, Benjamin T., Carter Francis, Lane E. Schultz, Janine Spethson, Vanessa Meschke, Elliot …May 4, 2022
Left: CT of Pancreatic cyst. Right: Adam Awe, a medical student who is the lead author, presenting the results at the Shapiro forum at the UW Medical School. He also presented at two subsequent national …March 9, 2022
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Acknowledging support from The Informatics Skunkworks:
For new products supported by the Informatics Skunkworks please include the acknowledgement below.
This material is supported by the National Science Foundation under grant OAC 2017072. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
|Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels||Yu-chen Liu, Henry Wu, Tam Mayeshiba, Benjamin Afflerbach, Ryan Jacobs, Josh Perry, Jerit George, Josh Cordell, Jinyu Xia, Hao Yuan, Aren Lorenson, Haotian Wu, Matthew Parker, Fenil Doshi, Alexander Politowicz, Linda Xiao, Dane Morgan, Peter Wells, Nathan Almirall, Takuya Yamamoto & G. Robert Odette||Journal Paper||2022-04-27|
|Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features||Benjamin T. Afflerbach, Carter Francis, Lane E. Schultz, Janine Spethson, Vanessa Meschke, Elliot Strand, Logan Ward, John H. Perepezko, Dan Thoma, Paul M. Voyles, Izabela Szlufarska, and Dane Morgan||Journal Paper||2022-03-30|
|Multi defect detection and analysis of electron microscopy images with deep learning||MingrenShen, Guanzhao Li, Dongxia Wu, Yuhan Liu, Jacob R. C. Greaves, Wei Hao, Nathaniel J. Krakauer, Leah Krudy, Jacob Perez, Varun Sreenivasan, Bryan Sanchez, Oigimer Torres-Velázquez, Wei Lia, Kevin G.Field, DaneMorgan||Journal Paper||2021-11-01|
|Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts||Adam M. Awe, Michael M. Vanden Heuvel, Tianyuan Yuan, Victoria R. Rendell, Mingren Shen, Agrima Kampani, Shanchao Liang, Dane D. Morgan, Emily R. Winslow, Meghan G. Lubner||Journal Paper||2021-10-12|
|A deep learning based automatic defect analysis framework for In-situ TEM ion irradiations||Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C. Haley, Kevin G.Field, Dane Morgan||Journal Paper||2021-09-01|
|Crash course in machine learning to engage undergraduates in research experiences||Anne Lynn Gillian-Daniel, Matthew Stilwell, Benjamin Afflerbach, Wendy Crone, Dane Morgan||Conference Presentation||2021-04-05|
|Assessing Graph-based Deep Learning Models for Predicting Flash Point||Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei-Ting Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James Wang, Dane Morgan||Journal Paper||2020-02-20|
|Prediction of concrete coefficient of thermal expansion and other properties using machine learning||Vanessa Nilsen, Le T. Pham, Michael Hibbard, Adam Klager, Steven M. Cramer, Dane Morgan||Journal Paper||2019-09-30|
|Fast approximate STEM image simulations from a machine learning model||Aidan H. Combs, Jason J. Maldonis, Jie Feng, Zhongnan Xu, Paul M. Voyles, and Dane Morgan||Journal Paper||2019-03-12|
|Robust FCC solute diffusion predictions from ab-initio machine learning methods||Henry Wu, Aren Lorenson, Ben Anderson, Liam Witteman, Haotian Wu, Bryce Meredig, Dane Morgan||Journal Paper||2017-06-15|