Publications

Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart (2024). Continuum Attention for Neural Operators. arxiv preprint.

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Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari, Emily Fox (2024). Hybrid Square Neural ODE Causal Modeling. In Review.

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Jin-Long Wu, Matthew E. Levine, Tapio Schneider, Andrew Stuart (2023). Learning About Structural Errors in Models of Complex Dynamical Systems. In Review.

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Jacob Parres-Gold, Matthew E. Levine, Benjamin Emert, Andrew Stuart, Michael B. Elowitz (2023). Principles of Computation by Competitive Protein Dimerization Networks. In Review.

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Ke Alexander Wang, Matthew E. Levine, Jiaxin Shi, Emily Fox (2022). Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates. NeurIPS Timeseries for Health Workshop 2022.

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Matthew E. Levine, Andrew M. Stuart (2022). A Framework for Machine Learning of Model Error in Dynamical Systems. Communications of the American Mathematics Society.

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Peter D. Sottile, David J. Albers, Peter E. DeWitt, Seth Russell, J N Stroh, David P. Kao, Bonnie Adrian, Matthew E. Levine, Ryan Mooney, Lenny Larchick, Jean S. Kutner, Matthew K. Wynia, Jeffrey J. Glasheen, Tellen D. Bennett (2021). Real-Time Electronic Health Record Mortality Prediction during the COVID-19 Pandemic: A Prospective Cohort Study. JAMIA 2021.

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Jami J. Mulgrave, Matthew E. Levine, David J. Albers, Joon Ha, Arthur Sherman, George Hripcsak (2020). Using Data Assimilation of Mechanistic Models to Estimate Glucose and Insulin Metabolism. arXiv:2003.06541 [physics, stat].

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Elliot G. Mitchell, Esteban G. Tabak, Matthew E. Levine, Lena Mamykina, David J. Albers (2019). Enabling Personalized Decision Support with Patient-Generated Data and Attributable Components. arXiv:1911.09856 [stat].

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D. J. Albers, M. E. Levine, M. Sirlanci, A. M. Stuart (2019). A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System. arXiv:1910.14193 [q-bio].

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David J. Albers, Paul-Adrien Blancquart, Matthew E. Levine, Elnaz Esmaeilzadeh Seylabi, Andrew Stuart (2019). Ensemble Kalman methods with constraints. Inverse Problems.

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Pooja M. Desai, Elliot G. Mitchell, Maria L. Hwang, Matthew E. Levine, David J. Albers, Lena Mamykina (2019). Personal Health Oracle: Explorations of Personalized Predictions in Diabetes Self-Management. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.

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D. J. Albers, M. Levine, L. Mamykina, G. Hripcsak (2019). The Parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems. arXiv:1902.01978 [q-bio, stat].

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Pooja M. Desai, Matthew E. Levine, David J. Albers, Lena Mamykina (2018). Pictures Worth a Thousand Words: Reflections on Visualizing Personal Blood Glucose Forecasts for Individuals with Type 2 Diabetes. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.

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Matthew E. Levine, David J. Albers, Marissa Burgermaster, Patricia G. Davidson, Arlene M. Smaldone, Lena Mamykina (2018). Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data. arXiv:1802.08761 [stat].

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Matthew E. Levine, David J. Albers, George Hripcsak (2018). Methodological variations in lagged regression for detecting physiologic drug effects in EHR data. Journal of Biomedical Informatics.

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David J. Albers, Matthew E. Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, George Hripcsak (2018). Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. Journal of the American Medical Informatics Association: JAMIA.

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George Hripcsak, Matthew E. Levine, Ning Shang, Patrick B. Ryan (2018). Effect of vocabulary mapping for conditions on phenotype cohorts. Journal of the American Medical Informatics Association: JAMIA.

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Daniel J. Feller, Marissa Burgermaster, Matthew E. Levine, Arlene Smaldone, Patricia G. Davidson, David J. Albers, Lena Mamykina (2018). A visual analytics approach for pattern-recognition in patient-generated data. Journal of the American Medical Informatics Association: JAMIA.

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Matthew E. Levine, George Hripcsak, Lena Mamykina, Andrew Stuart, David J. Albers (2017). Offline and online data assimilation for real-time blood glucose forecasting in type 2 diabetes. arXiv:1709.00163 [math, q-bio].

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David J. Albers, Matthew Levine, Bruce Gluckman, Henry Ginsberg, George Hripcsak, Lena Mamykina (2017). Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS computational biology.

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Lena Mamykina, Matthew E. Levine, Patricia G. Davidson, Arlene M. Smaldone, Noemie Elhadad, David J. Albers (2017). From Personal Informatics to Personal Analytics: Investigating How Clinicians and Patients Reason About Personal Data Generated with Self-Monitoring in Diabetes. Cognitive Informatics in Health and Biomedicine: Understanding and Modeling Health Behaviors.

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Lena Mamykina, Matthew E. Levine, Patricia G. Davidson, Arlene M. Smaldone, Noemie Elhadad, David J. Albers (2016). Data-driven health management: reasoning about personally generated data in diabetes with information technologies. Journal of the American Medical Informatics Association: JAMIA.

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Matthew E. Levine, David J. Albers, George Hripcsak (2016). Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data. AMIA … Annual Symposium proceedings. AMIA Symposium.

Matthew E. Levine, Lena Mamykina (2016). Bridging a Gap Between Data Science Research and Health DIY Movement. ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2016.

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