William-Speier,-PhD--
Speier@ucla.edu
310-267-1421
Office Info

924 Westwood Blvd, Suite 420, Room K

William Speier, PhD

Assistant Professor, Department of Radiological Sciences

BIOGRAPHY

Robotics, natural language processing, and machine learning have made amazing advances over the past few decades, with significant time and funding dedicated to development of countless applications of these fields. Nevertheless, no machine-based system can match the versatility or robustness of the human brain; human-created language and image processing systems are vastly inferior to their biological counterparts; and human decisions and mechanical actions remain the gold standard in the medical field. The goal of my research is to bridge the gap between the brain and machine applications through:

Learning the underlying processes in the function of the human brain
Creating interfacing software to facilitate brain-machine interaction
Developing closed-loop systems to modulate patient treatment based on their physiological state
An example of a brain-computer interface project from our lab can be seen below:

brain-computer interface project

Publications

2020
Meng Y, Speier W, Ong M, Arnold CW. Multi-level embedding with topic modeling on electronic health records for predicting depression. Proc AAAI Health Intelligence Workshop. New York City, NY. February 7-12, 2020.
2019
Li W, Wang Z, Yue Y, Li J, Speier W, Zhou M, Arnold CW. Semi-supervised Learning using Adversarial Training with Good and Bad Samples. arXiv preprint arXiv:1910.08540. 2019 Oct 18. Petousis P, Winter A, Speier W, Aberle DR, Hsu W, Bui AAT. Using Sequential Decision Making to Improve Lung Cancer Screening Performance. IEEE Access. 2019;7:119403-119419. doi: 10.1109/ACCESS.2019.2935763. Epub 2019 Aug 16. PMID: 32754420; PMCID: PMC7402617. Meng Y, Speier W, Shufelt C, Joung S, E Van Eyk J, Bairey Merz CN, Lopez M, Spiegel B, Arnold CW. A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data. IEEE J Biomed Health Inform. 2020 Mar;24(3):878-884. DOI: 10.1109/JBHI.2019.2922178. Epub 2019 Jun 11. PMID: 31199276; PMCID: PMC6904535. Li J, Li W, Gertych A, Knudsen BS, Speier W, Arnold CW. An attention-based multi-resolution model for prostate whole slide image classification and localization. arXiv preprint arXiv:1905.13208. 2019 May 30. Li W, Wang Z, Li J, Polson J, Speier W, Arnold CW. Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach. arXiv preprint arXiv:1905.06484. 2019 May 16. Sohn A, Speier W, Lan E, Aoki K, Fonarow G, Ong M, Arnold CW. Assessment of Heart Failure Patients' Interest in Mobile Health Apps for Self-Care: Survey Study. JMIR Cardio. 2019 Oct 29;3(2):e14332. DOI: 10.2196/14332. PMID: 31758788; PMCID: PMC6851712.
2018
Meng Y, Speier W, Dzubur E, Spiegel B, Arnold CW. Predicting Patient Health Status using Activity Tracker Data. Proceedings of the 2018 American Medical Informatics Association Annual Symposium. San Francisco; 2018. Li J, Speier W, Ho KC, Sarma KV, Gertych A, Knudsen BS, Arnold CW. An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies. Comput Med Imaging Graph. 2018 Nov;69:125-133. DOI: 10.1016/j.compmedimag.2018.08.003. Epub 2018 Sep 3. PMID: 30243216; PMCID: PMC6173982. Speier W, Dzubur E, Zide M, Shufelt C, Joung S, Van Eyk JE, Bairey Merz CN, Lopez M, Spiegel B, Arnold CW. Evaluating utility and compliance in a patient-based eHealth study using continuous-time heart rate and activity trackers. J Am Med Inform Assoc. 2018 Oct 1;25(10):1386-1391. DOI: 10.1093/jamia/ocy067. PMID: 29850807; PMCID: PMC6188512.

Research Projects