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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 Analytics
  • Spatial and Temporal Data Management and Analytics
  • Medical Imaging Informatics
  • Healthcare and Public Health Data Analytics
I am an assistant 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, China. 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 goal on big data management and analytics is to address the research challenges for delivering effective, scalable and high performance software systems for managing, querying and mining complex big data at multiple dimensions, including 2D and 3D spatial and imaging data, temporal data, spatial-temporal data, and sequencing data. My research goal on biomedical informatics is to develop novel methods and software systems to optimize the acquisition, extraction, management, and mining of biomedical data with much improved efficiency, interoperability, accuracy, and usability to support biomedical research and the healthcare enterprise.
I received an NSF CAREER award in 2014.
CV (last updated July 23, 2017).
May 31, 2017: CSIRE Program for K12 Students at Stony Brook University.


We are excited to announce that we started a new program "Computer Science and Informatics Research Experience Program for K12 Students" (CSIRE) at Stony Brook University. The CSIRE program at Stony Brook University is an opportunity for qualified, academically talented and motivated K12 students interested in pursuing a career in Computer Science or Informatics. The program provides the students a unique research experience working with leading researchers in the field.

January 31, 2017: NSF REU Opening: Exploring Scalable Data Analytics for Big Data at Stony Brook University.

I am looking for a highly motivated undergraduate student with CS or informatics major to work on an NSF sponsored project on Research Experiences for Undergraduates (REU).
If you are interested, please submit your application at SPIDAL REU at Stony Brook University .

January 25, 2017: We are organizing the Third International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'2017), in conjunction with VLDB 2017.

The workshop will bring people cross-cutting the fields of information management and medical informatics, to discuss innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare, public health, and everyday wellness, with clinical, physiological, imaging, behavioral, environmental, and omic- data, and data from social media. The workshop will be held at Munich, Germany on September 1, 2017. The workshop is in conjunction with the 43rd Very Large Databases Conference (VLDB 2017).



January 1, 2017: ACM Technews on industrial adoption of our GPU accelrated spatial querying methods.

ACM TechNews featured our research on GPU accelerated spatial querying methods, which are adopted byFixstars Solutions Inc for their geometry compuation engine. The work (published in VLDB 2012) is in collaboration with the Ohio State University.

November 2, 2016: Yanhui Liang won the best poster award at SIGSPATIAL 2016.

Yanhui's paper "Scalable 3D Spatial Queries for Analytical Pathology Imaging with MapReduce" won the best poster award at SIGSPATIAL 2016.


June 10, 2016: I will teach CSE532: Theory of Database Systems this fall.

This course covers recent advances in data management systems. Topics include complex queries and optimizations, XML data management, spatial data management, distributed and parallel databases, NoSQL databases, and MapReduce based data processing systems. We will discuss the foundations of data models, transaction models, storage, indexing and querying methods for these data management systems. We will demonstrate real world databases with biomedical data, geospatial data and/or social media data.

Course Website