Ryan Socha Email & Phone Number

Greater Fayetteville, AR Area

• Funded by the Air Force Research Lab to develop generative AI image-to-image models improving the quality of radar images of target military vehicles.
• First authored “Assessing the value of phase to deep SAR ATR via non-inferiority testing” whichproposes a novel feature importance method to test the importance of noisy features via controlled trials on modified.
• Coauthored “Graph pretraining approach to utilize synthetic data for SAR ATR”, presented atSPIE Defense and Commercial Sensing’s April 2024 Conference.

Fayetteville, Arkansas, United States

• Learned the mathematics of traditional methods for computational fluid dynamics (CFD) modeling such as Direct Numerical Simulation, Large-eddy Simulation, and Reynolds-averaged Navier-Stokes models.
• Conducted an extensive literature review on the strengths and weaknesses of machine learning models vis-à-vis traditional CFD models, with a particular focus on the advantages of Li et al. 2020's Fourier Neural.
• Authored a grant proposal to replicate and extend Li's results that was accepted and funded by the University of Arkansas' Honors College.
• Also investigated theoretical extensions to the Generalized Negative Correlation Learning strategy of Buschjäger, Pfahler, and Morik 2020.

Fayetteville, Arkansas, United States

• Designed an overhaul to SupplyPike’s consumer demand modeling framework to improveaccuracy and reduce the costs of their retail demand forecasting models by 95%+.
• Additionally worked on applications of AI/ML to automated data entry, collaborative filtering, and root cause analysis for inventory management.
• Mitigated data quality problems associated with right-censored inventory data (stockouts).

Fayetteville, Arkansas, United States

• Collaborated with Walmart’s “2D to 3D” modeling team to construct tools decreasing Walmart’s reliance on costly physical samples from overseas suppliers in their design approval process.
• Created a procedure for the nonparametric estimation of products’ raw material composition from manufacturing blueprints with incomplete information. This work leveraged Monte Carlo numerical integration methods from.
• Presented code and its results to an audience of business leaders in McMillon Innovation Studio’s Demo Day 2020.
• Additionally created, and presented, supplementary visualizations made using R and gnuplot.

Dayton, Ohio, United States

• Worked as a civilian contractor for the Air Force Research Lab-affiliated ATRC to engineer a novel technique for AI/ML ensemble learning with applications to sensors. This work was based on an original insight.
• Led a team of three to create a prototype in PyTorch implementing the project’s design and experimentally test its performance on benchmark datasets.
• First author of the associated research paper “Extending Negative Correlation Learning”, presented at a restricted conference and published to the ATRC’s internal database.

• Delivered a guest lecture on artificial neural networks and the unique challenges of Few Shot Learning to the Computer Science Department’s graduate-level Big Data elective.
• Organized regular brainstorming and paper reading events within the lab.
• Coauthored “Siamese Networks with Episodic Structure Loss for Few-Shot Learning”.