Nicasia Beebe-Wang


I am a data scientist at Bayesian Health.

I completed my PhD at the Paul G. Allen School for Computer Science & Engineering at the University of Washington, where I was advised by Su-In Lee. My PhD research focused on developing and applying machine learning techniques for biological and medical problems, particularly in cases of real-world data limitations. Before that, I received my BA in Computer Science (with a minor in Statistics) from Harvard University. Outside of my PhD research, I’ve been fortunate to learn from internship experiences at Google (Cloud AI Research Team, 2022-2023), Recursion (2021), and Facebook (2020).

Publications & Projects

Nicasia Beebe-Wang, Sayna Ebrahimi, Jinsung Yoon, Sercan O. Arik, Tomas Pfister. ''PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series.'' Preprint, 2023.

Nicasia Beebe-Wang, Ayse B Dincer, and Su-In Lee. ''An automatic integrative method for learning interpretable communities of biological pathways.'' NAR Genomics and Bioinformatics, 2022.

Ethan Weinberger, Nicasia Beebe-Wang, and Su-In Lee. ''Moment matching deep contrastive latent variable models.'' 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

Nicasia Beebe-Wang, Safiye Celik, Ethan Weinberger, Pascal Sturmfels, Philip De Jager, Sara Mostafavi S*, and Su-In Lee*. ''Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies." Nature Communications, 2021.

Nicasia Beebe-Wang*, Alex Okeson*, Tim Althoff **, and Su-In Lee**. ''Efficient and Explainable Risk Assessments for Imminent Dementia in an Aging Cohort Study." IEEE Journal of Biomedical and Health Informatics (Special Issue on Explainable AI for Clinical and Population Health Informatics), 2021.

Nicasia Beebe-Wang, Safiye Celik, Pascal Sturmfels, Sara Mostafavi*, and Su-In Lee*. ''MD-AD: Multi-task deep learning for Alzheimer’s disease neuropathology.'' ICML Workshop on Computational Biology, 2019 (Spotlight Talk; Travel Award)

Nicasia Beebe-Wang, Safiye Celik, and Su-In Lee. ''MD-AD: Multi-task deep learning for Alzheimer’s disease neuropathology.'' ICML & IJCAI Workshop on Computational Biology, 2018 (Poster)

Nicasia Beebe-Wang. "Towards Learning Regulatory Elements of Promoter Sequences with Deep Learning." Harvard University, Undergraduate honors thesis, 2017.

Scott Moeller, Nicasia Beebe-Wang, Kristin Schneider, Anna Konova, Muhammad Parvaz, Nelly Alia-Klein, Yasmin Hurd, and Rita Z. Goldstein. ''Effects of an opioid (proenkephalin) polymorphism on neural response to errors in health and cocaine use disorder.'' Behavioural Brain Research, 2015

Scott Moeller, Muhammad Parvaz, Elena Shumay, Salina Wu, Nicasia Beebe-Wang, Anna Konova, Michail Misyrlis, Nelly Alia-Klein, and Rita Z. Goldstein. ''Monoamine polygenic liability in health and cocaine dependence: Imaging genetics study of aversive processing and associations with depression symptomology.'' Drug and Alcohol Dependence, 2014

Scott Moeller, Nicasia Beebe-Wang, Patricia Woicik, Anna Konova, Thomas Maloney, and Rita Z. Goldstein. ''Choice to view cocaine images predicts concurrent and prospective drug use in cocaine addiction.'' Drug and Alcohol Dependence, 2013

Scott Moeller, Muhammad Parvaz, Elena Shumay, Nicasia Beebe-Wang, Anna Konova, Nelly Alia-Klein, Nora D. Volkow, and Rita Z. Goldstein. ''Gene × abstinence effects on drug cue reactivity in addiction: multimodal evidence.'' Journal of Neuroscience, 2013