Wang Lab

Principal Investigator: Fei Wang. PhD. FACMI, FAMIA, FIAHSI, ACM Distinguished Member.
Professor of Health Informatics. Department of Population Health Sciences. Weill Cornell Medicine. Cornell University.
Professor of Emergency Medicine (Secondary). Department of Emergency Medicine. Weill Cornell Medicine. Cornell University.
Founding Director. WCM Institute of AI for Digital Health. Weill Cornell Medicine. Cornell University.
Adjunct Scientist. Hospital for Special Surgery.

Graduate Field Member. Computer Science.
Graduate Field Member. Computational Biology.
Graduate Field Member. Information Science.
Graduate Field Member. Physiology, Biophysics and Systems Biology.
Graduate Field Member. Population Health Sciences.
Graduate Field Member. Statistics and Data Science.
Graduate Field Member. Tri-Institutional PhD Program on Computational Biology and Medicine.

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Research Interests

Our lab is working on developing data mining and machine learning approaches for health data science.

Health Data Science
Predictive modeling of clinical risks
Disease subtyping
Computational drug discovery and design
Knowledge graph
Machine Learning and Data Mining
Multi-modal learning
Meta-learning and transfer learning
Model interpretability and explainability
Federated learning
Model fairness

Research Highlights

July 2022. We are the winner of the AACC PTHrP results prediction challenge.
July 2022. Our project TREM2 Genotype-Informed Drug Repurposing and Combination Therapy Design for Alzheimer's Disease has been funded officially by NIA as an R01 project.
June 2022. Our PASC subphenotyping paper was highlighted in Fortune.
June 2022. Our OA vs. RA work was featured on HSS news.
June 2022. Our study on the importance of a multidisciplinary medical team for pregnant women with lupus was highlighted on PR Newswire.
May 2022. Our PASC analysis paper was highlighted in News Medical.
November 2021. Our KDD paper on fairness explanation was highlighted in AMIA 2021 Year-in-Review session.
July 2021. Paper on Socioeconomic variation in characteristics, outcomes, and healthcare utilization of COVID-19 patients in New York City is published on Plos ONE. See the story from department highlight.
July 2021. Paper on subphenotyping of COVID-19 is published on npj Digital Medicine. See the story from department highlight.
May 2021. Dr. Wang's commentary on Machine Learning on Rare Clinical Outcome Prediction is published on JAMA Network Open, which has also been mentioned in this news story.
April 2021. Our department reported our recent research on federated learning.
April 2021. We released the Cornell Biomedical Knowledge Hub.
April 2021. Our project " Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers" has been funded officially by NIA as an R01 project.
March 2021. Our paper "Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets" won Distinguished Paper Award of Implementation from the AMIA 2021 Virtual Informatics Summits on Clinical Research Informatics.
January 2021. Our lab was awarded the Sanofi iDEA award on the research of deep learning for real world data analysis.
January 2021. Our collaboration with Mount Sinai on federated learning for mortality prediction of COVID-19 patients was reported by News Medical.
December 2020. Our NSF project on knowledge engineering for COVID-19 was highlighted in our department news. Dr. Wang was also invited to co-organize the kick-off workshop for the Pandemic Research for Preparedness and Resiliance (PREPARE) effort funded by NSF.
December 2020. Our AI for COVID-19 Drug Repurposing paper was selected as the cover story of the Lancet Digital Health December issue.
November 2020. Our paper on Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent was highlighted in the AMIA 2020 Medical Informatics Year-in-Review session.
September 2020. Our projects on Data Fusion for Suicide Risk Modeling and Suicide Risk Modeling for Patients Prescribing Opioids are both funded by NIMH. We have two recent papers on Scientific Reports and Translational Psychiatry on related topics. This UConn Todays article introduced our efforts in recent years on the efforts of suicide risk modeling. We are also part of the project on Suicide Risk Modeling for Veterans with Chronic Pain.
August 2020. Collaborating with Department of Pathology and Laboratory Medicine, we published a paper on Clinical Chemistry on building a machine learning model to predict the SARS-Cov-2 infection status based on routine blood lab test results. See media coverage here and here.
August 2020. We presented our paper on MoFlow in KDD 2020 and the INNF+ 2020 workshop. MoFlow is a novel molecule generation algorithm based on graph normalizing flow models. The source code for MoFlow is available here. We are also in the process of building a user interface for MoFlow. Stay tuned.

Group News

[July 14, 2022] Dr. Wang is invited to give a keynote presentation at the NAACL 2022 workshop on TrustNLP .
[July 12, 2022] Dr. Wang is invited to give a research seminar in Sema4 .
[May 23, 2022] Dr. Wang is invited to present a short course at the 35th New England Statistics Symposium (NESS).
[May 3, 2022] Dr. Wang is invited to participate in a AI in Healthcare Panel in the 10th Joint Conference of the Upstate Chapters of the American Statistical Association (UP-STAT).
[May 1, 2022] Dr. Chengxi Zang is promoted to Instructor.
[April 26, 2022] Dr. Wang is invited to deliver a keynote at the International Workshop On AI In Health: Explainable AI For Better Health at WWW 2022.
[January 3, 2022] Dr. Zhenxing Xu is promoted to Research Associate.
[November 11, 2021] Dr. Wang is invited to talk at The 2021 DAISY workshop: Tackling Bias in Data Science: from Prediction to Intervention.
[September 22, 2021] Dr. Wang is invited to talk at The 1st Annual NJ ACTS Symposium: Translational Medicine and Science.
[August 21, 2021] Dr. Wang is invited to talk at IJCAI 2021 International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality.
[August 16, 2021] Dr. Wang is co-organizing Joint KDD 2021 Health Day and 2021 KDD Workshop on Applied Data Science for Healthcare.
[July 23, 2021] Dr. Wang is invited to talk at ICML 2021 Interpretable Machine Learning in Healthcare.
[April 20, 2021] Dr. Bojian Hou joins our lab as a postdoctoral researcher. Welcome Dr. Hou!
[April 12, 2021] Dr. Wang gives a talk at on the promising of machine learning in clinical laboratory medicine at the grand rounds of Pathology and Labortory Medicine, together with Dr. He Sarina Yang.
[April 1, 2021] Dr. Zilong Bai joins our lab as a postdoctoral researcher. Welcome Dr. Bai!
[February 3, 2021] Dr. Wang is going to present a tutorial on Artificial Intelligence for Drug Discovery at AAAI 2021 together with Prof. Jian Tang and Prof. Feixiong Cheng.
[January 8, 2021] Dr. Wang is invited to give a keynote talk at IJCAI-PRICAI workshop on Computational Disease Modeling.
[December 21, 2020] Dr. Wang is invited to serve as the Chair of Health Day for KDD 2021.
[December 15-16, 2020] Dr. Wang is co-organizing the PREPARE Kickoff Workshop.
[December 15, 2020] Dr. Wang is invited to give a talk at the ICSA Applied Statistics Symposium.
[December 15, 2020] Dr. Wang is invited to give a talk at NIEHS.
[December 13, 2020] Dr. Wang is invited as the judge of the Singapore-MIT Healthcare Datathon 2020.
[December 11, 2020] Dr. Wang is invited to give a talk to Mount Sinai Clinical Intelligence Center.
[December 10, 2020] Dr. Wang is invited to give a talk at the Singapore AI Healthcare Expo 2020.
[December 2, 2020] Dr. Wang is invited to give a talk at the biomedical informatics grand rounds of Stony Brook University.
[November 30, 2020] Dr. Wang is invited to give a talk to UMass Boston.
[November 12, 2020] Dr. Wang serves as the executive editor of the new SPJ on Health Data Science. Consider submitting your cutting edge research on health data science!
[October 14, 2020] Dr. Wang is inducted to the Fellow of AMIA.
[August, 2020] The homepage is establised.