Co Director of Duke Forge, and Assistant Dean for Biomedical Informatics, Dr. Huang’s research interests span applied machine learning, research provenance and data infrastructure. Projects include building data provenance tools funded by the NIH’s Big Data to Knowledge program, regulatory science funded by the Burroughs Wellcome Foundation. Applied machine learning applications include “Deep Care Management” a highly interdisciplinary project with Duke Connected Care, Duke’s Accountable Care Organization, that integrates claims and EHR data for predicting unplanned admissions and risk stratifying patients for case management; CALYPSO, a collaboration with the Department of Surgery for utilizing machine learning to predict surgical complications. My team is also building the data platform for the Department of Surgery's "1000 Patients Project" an intensive biospecimen and biomarker study based around patients undergoing the controlled injury of surgery.
As Co Director of Duke Forge, Dr. Huang is working with Robert Califf, former Commissioner of the FDA to build a data science culture and infrastructure across Duke University that focuses on actionable health data science. The Forge emphasizes scientific rigor, awareness that technology does not supersede clinicians’ responsibilities and human relationship with their patients, and the role of data science in society.
Education and Training
- Duke University, Ph.D. 2002
- Duke University, M.D. 2003
Selected Grants and Awards
- Medical Scientist Training Program
- Postdoctoral Training in Genomic Medicine Research
- Bridging the Gap to Enhance Clinical Research Program (BIGGER)
- Bioinformatics and Computational Biology Training Program
- Infrastructure for Research Provenance & Reproducibility to Support Auditable Regulatory Scientic Workflows
- Increasing the Quality and Efficiency of Clinical Trials (R18)
- Flexible & Executable Provenance in Data-Intensive Biomedical Research: A Flexible Research Data Service
- Machine Learning Platform for Surgical Risk Prediction and Modification
- Duke/NIDDK Functional Genomics Center