Chang Su, PhD

Bio

I am an Assistant Professor in the Department of Population Health Sciences, Institute of Artificial Intelligence for Digital Health (AIDH) at Weill Cornell Medical College, Cornell University. I am broadly interested in machine learning, healthcare data mining, health informatics. 

Research Interests

• Health data mining

  • Clinical data (Electornic Health Records)

  • Multi-omics data

  • Neuroimaging data

  • Social determinants of health (SDoH)

  • etc.

• Biomedical knowledge graph curation and inference.

• Multi-modal biomedical data fusion.

News

• Oct 2023:  Joined the Department of Population Health Sciences, Institute of Artificial Intelligence for Digital Health (AIDH) at Weill Cornell Medical College, Cornell University, as a Tenure-track Assistant Professor.  

• Oct 2023: Our work regarding investigating heterogeneous SDoH data to identify latent SDoH patterns and exploring their associations with child mental health, cognition performance, and physical health was published in JAMA Pediatrics. See story. 

• Our BKG, termed iBKH, is available at iBKH.ai and GitHub. 

• Mar 2023: Our work regarding construction of a comprehensive biomedical knowledge graph (BKG) and BKG-based knowledge discovery was accpted by Cell Press family journal – iScience. Our BKG, termed iBKH, is available at iBKH.ai and GitHub. 

• Dec 2022: Our review paper "Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review" was accepted by Genomics, Proteomics & Bioinformatics.

• Nov 2022: Presenting our work "Comprehensively modeling heterogeneous symptom progression for Parkinson’s disease subtyping" at AMIA 2022 Annual Symposium.

• Nov 2022: Our COVID-19 subphenotyping work and sepsis subphenotyping work were highlighted in AMIA 2022 Year-in-Review Session.

• Sep 2022: Our sepsis subphenotyping work "Sepsis subphenotyping based on organ dysfunction trajectory" was accepted by Critical Care (Impact Factor 19.334).

• Jan 2022: Contribute to book chapter GNN-based Biomedical Knowledge Graph Mining in Drug Development in the book Graph Neural Networks: Foundations, Frontiers, and Applications.

• Oct 2021: Our fusion learning framework for suicide prediction in EHR, tiltled: "Improving suicide risk prediction via targeted data fusion: proof of concept using medical claims data" was accepted to appear in JAMIA.  

• Jul 2021: Joined Temple University as Assistant Professor in Health Informatics.

• Jun 2021:Our COVID-19 subphenotyping work: "Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health" was accepted by npj Digital Medicine (Nature family jounal, Impact Factor 11.653). See story. 

• Mar 2021: Released our Cornell Biomedical Knowledge Hub (CBKH). Preprinted at medrxiv.

• Feb 2021: Finished the COVID-19 Insight project: Novel clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health. Preprinted at medrxiv.

• Jan 2021: AI in Medicine course.

• Nov 2020: Paper published at Nature Translational Psychiatry.

• Nov 2020: PC member of SIAM International Conference on Data Mining 2021 (SDM 2021).

• Seq 2020: Paper published at NPJ Parkinson's Disease.

• Jul 2020: Finished the first COVID-19 project: organ dysfunction-based subphenotyping of critically ill patiets with COVID-19. Preprinted at medrxiv. 

• Jun 2020: Paper accepted to Nature Translational Psychiatry.

• Apr 2020: Join the COVID-19 progression-based subphenotyping project.

• Apr 2020: Paper accepted to Briefings in Bioinformatics.

• Feb 2020: Presentation “Longitudinal medical records visualization dash board”, WSDM Workshop Healthcare Day 2020, Houston, Texas

• Jan 2020: AI in Medicine course.

• May 2019: Health Data Mining course.

• Dec 2018: Paper accepted to Briefings in Bioinformatics.

• Oct 2017: Won NeurIPS 2017 Competition on Classifying Clinically Actionable Genetic Mutations. Held at NeurIPS 2017. (Dec 4, Long Beach, CA)