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Fusheng Wang 		 

Fusheng Wang, Ph.D.
Stony Brook University
2313D Computer Science, Stony Brook, NY 11794-8330
Email: fusheng . wang @ stonybrook . edu
Phone: (631)632-2594   
Areas of Research Interest:
  • Scalable Big Data Management and GIS
  • Population Health and Opioid Epidemic Research
  • Medical Imaging Informatics
  • AI in Healthcare
  • Assistive Technologies
Lab: Data Management and Biomedical Data Analytics
I am a Professor at Department of Biomedical Informatics and Department of Computer Science at Stony Brook University. I received my Ph.D. in Computer Science from University of California, Los Angeles, and M.S. and B.S. in Engineering Physics from Tsinghua University. Prior to joining Stony Brook University, I was an assistant professor at Emory University. I was a research scientist at Siemens Corporate Research (Princeton, NJ) before joining Emory University.
My research covers Spatial Big Data Management, GIS, Medical Imaging Informatics, AI in Healthcare, Population Health and Opioid Epidemic Research, and Assistive Technologies








Oct 3, 2024: PCORI Award to Advance Machine Learning Models for Patient Outcomes Prediction.
I will lead a PCORI funded project to to work on human centered AI for early prediction of patients' risks.
More information can be found at News.















August 29, 2024: Proud to Win the VLDB Test of Time Award!
We have been awarded the 2024 Test of Time award from the Very Large Database (VLDB) Endowment, for our paper "Hadoop GIS: a high performance spatial data warehousing system over mapreduce" published in 2013. The paper has made significant contributions to the creation of an ecosystem for big spatial analytics that is currently widely adopted for its merits of large-scale capacity, scalability, compatibility with low-cost commodity processors, and open-source accessibility, making it indispensable in the society for various applications.
More information can be found at News.















October 13, 2023: Congratulations to Jianyuan for her paper at Nature Communications!
The paper "A systematic study of key elements underlying molecular property prediction" unveils insights into molecular property prediction using AI. The rigorous evaluation of 62,820 models uncovers challenges and opportunities.