I am currently a PhD candidate (started in 2015) in Computer Science at the University of Minnesota, Twin-Cities.
I am expected to graduate in Spring/Summer 2020 and seeking a faculty position in the broad area of spatial data science (e.g., data mining, machine learning, optimization and database techniques for spatial data).
Broad research area:
- 2019: Our paper has been accepted by ACM Transactions on Data Science (ACM TDS)!
- 2019: Received the Best Vision Paper Award at the ACM SIGSPATIAL 2019 (recognized by CCC's Blue Sky Ideas)!
- 2019: Our paper has been accepted by the ACM SIGSPATIAL 2019!
- 2019: Received the Best Paper Award at SSTD 2019!
- 2019: Our paper has been accepted by ACM Transactions on Intelligent Systems and Technology (ACM TIST)!
- 2019: Our paper has been accepted by International Symposium on Spatial and Temporal Databases (SSTD'19)!
- 2019: Our paper has been accepted by International Journal of Geographic Information Science (IJGIS)!
- 2018: Our paper has been accepted by SIAM International Conference on Data Mining (SDM' 19)!
- 2018: Our paper has been accepted by IEEE International Conference on Data Mining (ICDM'18)!
- 2018: Our paper has been accepted by ACM SIGSPATIAL 2018!
Spatial Data Science (spatial data mining, machine learning, optimization, database) with a transdisciplinary view (computer science, statistics, mathematics, domain science)
Sample research projects
- Spatial data mining (ACM TDS'20; ACM TIST'19; SSTD'19 Best Paper Award; SDM'19; ICDM'14)
- Importance and applications: public health (e.g., disease outbreak), public safety, transportation, climate change...
- Supported by NSF
- Statistically robust clustering
- Hotspot detection
- Significant DBSCAN clustering
- Significant change interval detection
- Deep learning for remote sensing data (ICDM'18; ACM SIGSPATIAL'18, 19; IJGIS'18)
- Importance and applications: Unawareness of the locations of geospatial objects (e.g., individual trees) has caused serious societal problems. For example, a series of deadly fires caused by trees near electricity power lines have killed many people and cost billions of economic loss.
- Supported by NSF
- Domain-knowledge guided deep learning for problems with
- Limited training data
- Non-distinctive signatures/features
- Spatial optimization (SSTD'17, AAAI'17 Workshop, ISPRS IJGI'17)
- Importance and applications: Huge demands on food production (estimated 9 billion population by 2050) vs. severe water pollution (e.g., Dead Zone in the Gulf of Mexico)
- Optimization with spatially dependent decision variables
Courses on machine learning, data science and database.