Design goals for a system for enhancing AAC with personalized video. - Katie O'Leary; Charles B. Delahunt; Patricia Dowden; Ivan Darmansya; Jiaqi Heng; Eve A. Riskin; Richard E. Ladner; Jacob O. Wobbrock (ASSETS, 2012)

Automated microscopy and machine learning for expert-level malaria field diagnosis. - Charles B. Delahunt; Courosh Mehanian; Liming Hu; Shawn K. McGuire; Cary R. Champlin; Matthew P. Horning; Benjamin K. Wilson; Clay M. Thompson (GHTC, 2015)

Computer-Automated Malaria Diagnosis and Quantitation Using Convolutional Neural Networks. - Courosh Mehanian; Mayoore S. Jaiswal; Charles B. Delahunt; Clay Thompson; Matthew P. Horning; Liming Hu; Shawn K. McGuire; Travis Ostbye; Martha Mehanian; Benjamin K. Wilson; Cary R. Champlin; Earl Long; et al. (ICCV Workshops, 2017)

A moth brain learns to read MNIST. - Charles B. Delahunt; J. Nathan Kutz (ICLR (Workshop), 2018)

Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data. - Charles B. Delahunt; J. Nathan Kutz (CoRR, 2018)

Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, With Applications to Neural Nets. - Charles B. Delahunt; Jeffrey A. Riffell; J. Nathan Kutz (Frontiers Comput. Neurosci., 2018)

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images. - Charles B. Delahunt; Roman Gebrehiwot; Benjamin K. Wilson; Earl Long; Stephane Proux; Dionicia Gamboa; Peter Chiodini; Jane Carter; Mehul Dhorda; David Isaboke; Bernhards Ogutu; Mayoore S. Jaiswal; et al. (GHTC, 2019)

Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy. - Charles B. Delahunt; Courosh Mehanian; J. Nathan Kutz (CoRR, 2019)

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information. - Charles B. Delahunt; Mayoore S. Jaiswal; Matthew P. Horning; Samantha Janko; Clay M. Thompson; Sourabh Kulhare; Liming Hu; Travis Ostbye; Grace Yun; Roman Gebrehiwot; Benjamin K. Wilson; Earl Long; et al. (CoRR, 2019)

Putting a bug in ML: The moth olfactory network learns to read MNIST. - Charles B. Delahunt; J. Nathan Kutz (Neural Networks, 2019)

Algorithms to predict moisture content of grain using relative humidity time-series. - Charles B. Delahunt; Wenbo Wang; Simon Ghionea; Andrew Miller; Austin Chan; Anjali Sehrawat; Courosh Mehanian; Michael Friend (GHTC, 2020)

Predicting United States policy outcomes with Random Forests. - Shawn K. McGuire; Charles B. Delahunt (CoRR, 2020)

PySINDy: A comprehensive Python package for robust sparse system identification. - Alan A. Kaptanoglu; Brian M. de Silva; Urban Fasel; Kadierdan Kaheman; Jared L. Callaham; Charles B. Delahunt; Kathleen P. Champion; Jean-Christophe Loiseau; J. Nathan Kutz; Steven L. Brunton (CoRR, 2021)

A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes. - Charles B. Delahunt; J. Nathan Kutz (IEEE Access, 2022)

Use case-focused metrics to evaluate machine learning for diseases involving parasite loads. - Charles B. Delahunt; Noni Gachuhi; Matthew P. Horning (CoRR, 2022)

PySINDy: A comprehensive Python package for robust sparse system identification. - Alan A. Kaptanoglu; Brian de Silva; Urban Fasel; Kadierdan Kaheman; Andy Goldschmidt; Jared Callaham; Charles B. Delahunt; Zachary Nicolaou; Kathleen P. Champion; Jean-Christophe Loiseau; J. Nathan Kutz; Steven L. Brunton (J. Open Source Softw., 2022)

Deep Learning Video Classification of Lung Ultrasound Features Associated with Pneumonia. - Daniel E. Shea; Sourabh Kulhare; Rachel Millin; Zohreh Laverriere; Courosh Mehanian; Charles B. Delahunt; Dipayan Banik; Xinliang Zheng; Meihua Zhu; Ye Ji; Travis Ostbye; Martha-Marie S. Mehanian; et al. (CVPR Workshops, 2023)

How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound. - Menghan Yu; Sourabh Kulhare; Courosh Mehanian; Charles B. Delahunt; Daniel E. Shea; Zohreh Laverriere; Ishan Shah; Matthew P. Horning (SASHIMI@MICCAI, 2023)

Reducing Poisson error can offset classification error: a technique to meet clinical performance requirements. - Charles B. Delahunt; Courosh Mehanian; Matthew P. Horning (ML4H@NeurIPS, 2024)

Driving down Poisson error can offset classification error in clinical tasks. - Charles B. Delahunt; Courosh Mehanian; Matthew P. Horning (CoRR, 2024)