Scholar
Ping Zhang
Google Scholar ID: E3WyjZUAAAAJ
The Ohio State University
Citations & Impact
Citations
5,325
H-index
35
i10-index
72
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- OSU President’s Research Excellence Accelerator Award (2024)
- Listed in World's Top 2% Scientists (2023–present)
- NSF CAREER Award (2022)
- OSU Award for Excellence in Mentoring (2021)
- ICML Top Reviewer (2020)
- DII National Data Science Challenge Honorable Mention Award (2019)
- IBM Master Inventor Award (2018)
- IBM Outstanding Technical Achievement Award for Cognitive-driven Chronic Disease Management Solution Suite (2018)
- IBM Outstanding Technical Achievement Award for Patient Similarity Analytics (2016)
- ESWC Best In-Use/Industrial Paper Award (2016)
- AMIA Summits Nomination for Marco Ramoni Distinguished Paper Award (2014)
- ACM Future of Computing Gold Award (First Place) for Graduate Projects (2012)
- NSF Travel Grant Award for BIBM (2012)
- Associate Editor for journals including BMC Digital Health, BMC Medical Informatics and Decision Making, Journal of Healthcare Informatics Research
- IAHSI Fellow (2025–present), AMIA Fellow (2020–present), IEEE Senior Member (2018–present)
- ACM Distinguished Speaker (2018–2021)
- Principal investigator on multiple grants from NSF (e.g., CAREER IIS-2145625) and NIH (multiple R01, R21, R25 awards)
Background
- Tenured Associate Professor at The Ohio State University (OSU), with joint appointments in the Department of Computer Science and Engineering (CSE) and the Department of Biomedical Informatics (BMI)
- Leads the Artificial Intelligence in Medicine (AIMed) Lab at OSU
- Leads the Artificial Intelligence in Digital Health Collaboration Core at OSU Wexner Medical Center (OSUWMC)
- Serves as Vice Chair of Education, developing and directing AI in Digital Health education programs at OSU
- Research focuses on machine learning, data mining, and their applications in foundation models (e.g., LLMs, large vision/multimodal models, AI agents), trustworthy AI (e.g., explainability, robustness, domain generalization, causal inference, uncertainty quantification, human-AI collaboration), and computational medicine (e.g., predictive modeling, medical imaging, clinical NLP, real-world evidence, drug discovery & development)
Co-authors