W. Boag. Evidence-based AI Ethics. MIT. PhD Dissertation. May 2022. [watch]

W. Boag. Quantifying Racial Disparities in End-of-Life Care. MIT. Master’s Thesis. June 2018. [repo] [slides] [blog]

W. Boag. Sense-Aware Word Embeddings Using Stream Clustering. UMass Lowell. Bachelor’s Honors Thesis. May 2016.

In Proceedings Papers

W. Boag*, M. Oladipo, P. Szolovits. ​EHR Safari: Data is Contextual Machine Learning for Healthcare 2022 (MLHC 2022).[watch]

W. Boag, H. Suresh, B. Lepe, and C. D’Ignazio. 2022. Tech Worker Organizing for Power and Accountability. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22), June 21– 24, 2022, Seoul, Republic of Korea. ACM, New York, NY, USA,

W. Boag,H. Kané, S. Rawat, J. Wei, A. Goehler. Pilot Study in Surveying Clinical Judgments to Evaluate Radiology Report Generation. In 2021 ACM Conference on Fairness, Accountability and Transparency (FAccT ’21). [watch] [bib]

W. Boag , O. Kovaleva , T. McCoy Jr, A. Rumshisky, P. Szolovits, R. Perlis. Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes. Translational Psychiatry (Nature Publishing Group). Jan 11, 2021.

S. Saleh*, W. Boag*, L. Erdman*, T. Naumann. ​Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration. Machine Learning for Healthcare 2020 (MLHC 2020).[watch] [bib]​

W. Boag, T.M. H. Hsu, M. McDermott, G. Berner, E. Alsentzer, P. Szolovits. Baselines for Chest X-Ray Report Generation. Proceedings of Machine Learning Research XX:1–15, 2019 Machine Learning for Health (ML4H) at NeurIPS 2019. [repo] [poster]

G. Liu, T.M. H. Hsu, M. McDermott, W. Boag, W.H. Weng, P. Szolovits, M. Ghassemi. Clinically Accurate Chest X-Ray Report Generation. Machine Learning for Healthcare (MLHC 2019). August, 2019. Ann Arbor, MI. [poster]

B. Nestor, M. McDermott, W. Boag, G. Berner, T. Naumann, M. C. Hughes, A. Goldenberg, M. Ghassemi. Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. Machine Learning for Healthcare (MLHC 2019). August, 2019. Ann Arbor, MI. [repo] [poster] [slides]

W. Boag, H. Suresh, L.A. Celi, P. Szolovits, M. Ghassemi. Racial Disparities and Mistrust in End-of-Life Care. Machine Learning for Healthcare (MLHC 2018). August, 2018. Palo Alto, CA. [repo] [slides] [blog]

W. Boag​, D. Doss, T. Naumann, P. Szolovits. What’s in a Note? Unpacking Predictive Value in Clinical Note Representations. AMIA 2018 Informatics Summit. March, 2018. San Francisco, California. [repo] [slides]

W. Boag, R. Campos, K. Saenko, A. Rumshisky. MUTT: Metric Unit TesTing for Language Generation Tasks. ACL 2016. August, 2016. Berlin, Germany. [repo][poster]


M. Abulnaga, W. Boag, D. Palmer, D. Shanmugam, C. Testart, A. Zeng. Best Practices for UROP Mentorship in EECS. Student-driven community report. June 8, 2021.

D. Jackson, H. Abelson, W. Boag, J. Dorfman, N. Figueroa, G. Jones, F. Keniston, D. Olson, D. Weitzner, R. White, C. Yuan. CSAIL Ahead: Improving CSAIL Culture and Community. CSAIL Working Group committee report. June 1, 2021.

G. Kozemcak, W. Monge, N. Good, G. Gupta, J. Reardon, D. Kinney, W. Boag. Issue Brief: Shadow Profiling and User Control. International Digital Accountability Council report. May 2021.

Q. Palfrey, N. Good, L. Ghamrawi, W. Monge, W. Boag. Privacy Considerations as Schools and Parents Expand Utilization of Ed Tech Apps During the COVID-19 Pandemic. International Digital Accountability Council report. September 1, 2020. [blog] [video]

(unsigned). Premom’s Deceptive Privacy Practices Places Vulnerable Users’ Data at Risk. Letter to the FTC urging investigation. August 6, 2020. [Washington Post summary] [Concurrence from Senators Warren and Klobuchar (The Hill)]

W. Boag, P. Satterthwaite, A. Zeng, H. Karimi, A. Brahmakshatriya, S. Muschinske, L. Makatura. MIT EECS Department DEI Petition. Graduate Student petition. June 26, 2020 [Progress Scorecard]

(unsigned). Privacy in the Age of COVID: An IDAC Investigation of COVID-19 Apps. International Digital Accountability Council report. June 5, 2020. [Policy Changes]

T. Colvin, K. Crane, R. Lindbergh, B. Lal. Demand Drivers of the Lunar and Cislunar Economy. IDA Science and Technology Policy report. April 2020. (uncredited).

Workshop Papers

I. Chen, H. Berlin, W. Boag, D. Sontag, P. Szolovits, P. Kamble, S. Wang, K. Elomaa, M. Luo. Applying Machine Learning to Large Databases to Predict Nonresponse to Conventional Treatment in Patients with Ulcerative Colitis. International Society for Pharmacoeconomics and Outcomes Research, May 2021.

E. Alsentzer,  J. Murphy, W. Boag, W.H. Weng, D. Jin, T. Naumann, M. McDermott. Publicly Available Clinical BERT Embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop. June 2019. Minneapolis, Minnesota. [repo]

T.M. H. Hsu, W.H.Weng, W. Boag, M. McDermott, P. Szolovits. Unsupervised Multimodal Representation Learning across Medical Images and Reports. NeurIPS 2018 Workshop on Machine Learning for Health. December, 2018. Montreal, Canada.

W. Boag, H. Suresh, L.A. Celi, P. Szolovits, M. Ghassemi. Modeling Mistrust in End-of-Life Care. Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2018). July, 2018. Stockholm, Sweden. [repo][poster]

W. Boag, M. Kane, P. Szolovits. AWE-CM Vectors: Augmenting Word Embeddings with a Clinical MetathesaurusNeurIPS 2017 Workshop on Machine Learning for Health. December, 2017. Long Beach, California. [repo] [poster]

W. Boag, T. Naumann, E. Sergeeva, S. Kulshreshtha, P. Szolovits, A. Rumshisky. CliNER 2.0: Accessible and Accurate Clinical Concept ExtractionNeurIPS 2017 Workshop on Machine Learning for Health. December, 2017. Long Beach, California. [repo] [poster]

Y. Ling, S. Hasan, M. Filannino, K. Buchan, K. Lee, J. Liu, W. Boag, D. Jin, O. Uzuner, K. Lee, V. Datla, A. Qadir, D. Farri. A Hybrid Approach to Precision Medicine-related Biomedical Article Retrieval and Clinical Trial Matching. TREC 2017 Precision Medicine / Clinical Decision Support Track. November, 2017. Gaithersburg, Maryland.

W. Boag, T. Naumann, P. Szolovits. Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes. NeurIPS 2016 Workshop on Machine Learning for Health. December, 2016. Barcelona, Spain. [repo][poster]

P. Potash, W. Boag, A. Romanov, V. Ramanishka, A. Rumshisky. Simihawk at Semeval 2016 Task 1: A Deep Ensemble System for Semantic Textual SimilairtyThe 10th International Workshop on Semantic Evaluation (SemEval-2016). NAACL HLT 2016. June, 2016. San Diego, California. [poster]

W. Boag, P. Potash, A. Rumshisky. TwitterHawk: A Feature Bucket Approach to Sentiment Analysis, In Proceedings of the 9th international workshop on Semantic Evaluation Exercises (SemEval 2015). June 2015, Denver, Colorado, USA. [repo] [poster]

W. Boag, K. Wacome, T. Naumann, A. Rumshisky. CliNER: A Lightweight Tool for Clinical Concept Extraction (abstract). AMIA Joint Summits on Clinical Research Informatics  (AMIA CRI 2015). San Francisco, CA. [repo] [poster]


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