Bing Xue

I am currently a research scientist at Meta. I completed my PhD at Washington University, advised by Prof. Chenyang Lu, where I work on deep learning applications in healthcare. Before that, I was a research engineer at SMART centre in Massachusetts Institute of Technology, where I was advised by Prof. Moshe E. Ben-Akiva. I earned my Bachelor of Engineering from Nanyang Technological University, and Master of Science from National University of Singapore.

Email / Google Scholar / LinkedIn

News

09/2024, I have been invited by ICASSP'25 to serve as an Area Chair!

08/2024, I have been invited by AAAI'25 to serve as an Area Chair!

11/2023, I have graduated from Washington University! I will accept Meta's return offer and join as a Research Scientist.

09/2023, I'm glad that I received a return offer from Amazon as an Applied Scientist! Thanks for the recognition!

08/2023, I will be the session chair for Search and Information Retrieval on Aug 8 at KDD'23. Excited to meet new people there!

07/2023, I received the Student Travel Award for KDD'23!

05/2023, I will join Amazon Search Science for my summer internship as applied scientist. I will work on multi-task learning with large language models.

05/2023, I will be a co-principal investigator (Co-PI) on “Cancer FAST Stability Project”, funded by Big Ideas.

04/2023, our work on individualized treatment effect estimation is accepted by KDD'23.

03/2023, 2 papers about machine learning in COVID treatment are accepted by JAMIA. and Artificial Organs.

01/2023, our paper about perioperative risk management will appear on Journal of Biomedical Informatics.

Internship Experience

06/2023, I will do my summer internship at Amazon Search.

06/2022, I will do my first internship at Meta!

Recent Projects

Individualized Treatment Effect Estimation

JAMIA | Artificial Organs | KDD

We could have saved more people! In collaboration with National COVID Cohort Collaborative (N3C) from NIH and International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) from Oxford University, we investigated COVID-19 patients from 63 countries across 5 continents who were admitted to Intensive Care Units (ICU) during the pandemic. We evaluated the impact of their treatment assignment and responses, and developed several clinical assisting tool that could predict the best treatment options by considering both the their factual and counterfactual responses.

AI4Science

We are exploring a brand-new domain - can AI help physicians decode the material properties from their spectral imaging and vice versa? We have developed an innovative optical architecture that nearly perfectly recover the spectral imaging from crystal responses! I will work on publishing our results after my internship.

Publications

Conference Papers:

Xue, B., Said, A., Xu, Z., Liu, H., Shah, N., Yang, H., Payne, P. and Lu, C., Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
KDD 2023 | paper | news

Li, D., Xue, B., C., King, Fritz, B., Avidan, M.S., Abraham, J, and Lu C. (2022, Nov). Self-explaining Hierarchical Model for Intraoperative Time Series
KDD 2022 | paper

Zhang, Z., Xiang, Y., Wu, L., Xue, B. and Nehorai, A., 2019. Kergm: Kernelized graph matching
NeurIPS 2019 | paper

Shi, W., Xue, B. Guo, S., Goh, D. Y., and Ser, W. (2018). Obstructive Sleep Apnea Detection Using Difference in Feature and Modified Minimum Distance Classifier. EMBC 2018 | paper

Journal Papers:

Xue, B., Shah, N., Xu, Z., Yang, H., Marwali, E., Dalton, H., ... and ISARIC Clin- ical Characterisation Group. (2023). Validation of extracorporeal membrane oxygenation mortality prediction & severity of illness scores in an international COVID‐19 cohort.
Artificial Organs 2023 | paper

Xue, B., Shah, N., Yang, H., Kannampallil, T., Payne, P. R. O., Lu, C., and Said, A. S. (2022). Multi-horizon predictive models for guiding extracorporeal resource allocation in critically ill COVID-19 patients.
JAMIA 2023 | paper

Abraham, J., Bartek, B., Meng, A., King, C. R., Xue, B., Lu, C., and Avidan, M. (2022). Integrating Machine Learning Predictions for Perioperative Risk Management: Towards an Empirical Design of a Flexible-Standardized Risk Assessment Tool.
Journal of Biomedical Informatics 2023 | paper

Xue, B., Shi, W., Chotirmall, S., Koh, V., Ang, Y., Tan, X., and Ser, W. (2022). Distance-based detection of cough, wheeze and breath sounds on wearable devices.
Sensors 2023 | paper

Xue, B., Licis, A., Boyd, J., Hoyt, C. R., and Ju, Y. E. S. (2022). Validation of actigraphy for sleep measurement in children with cerebral palsy.
Sleep Medicine 2023 | paper | news by NeurologyLive

Jiao, Y., Xue, B., Lu, C., Avidan, M.S., and Kannampallil, T. (2021). Continu- ous Real-Time Prediction of Surgical Case Duration Using a Modular Artificial Neural Network.
British Journal of Anaesthesia 2022 | paper | Editorial Comment

Xue, B., Li, D., Lu, C., King, C.R., Wildes, T., Avidan, M.S., Kannampallil, T. and Abraham, J., (2021). Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications.
JAMA network open 2022 | paper | Editorial Comment

Services

Session Chair of KDD 2023, EMBC 2023

Program Committee Member of NeurIPS 2023, KDD 2023, EMSOFT 2023, AMIA 2023, AAAI 2022, etc.

Reviewer of Scientific Reports, Journal of Biomedical Informatics, BMC Medical Informatics and Decision Making, etc.

Welcome to use this website's source code.