Dejun Teng

Ph.D. Candidate

Department of Computer Science

Stony Brook University

Email: dteng [at] cs.stonybrook.edu



I am a Ph.D. candidate in Computer Science Department at Stony Brook University, SUNY. I work in the Data Management and Biomedical Data Analytics Lab(BMIDB) under the supervision of Professor Fusheng Wang. I received my M.S. in Computer Science from Emory University and B.E. in Software Engineering from Xi'an Jiaotong University, China.

My research interests lie in Big Data, Computer Systems, Spatial Data Management, and Geographic Information Systems (GIS).

Google Scholar DBLP CV 中文简历 Github

Research Projects

  • 3DPro: Querying Complex Three-dimensional Data with Progressive Compression and Refinement

  • GLINT: GPU-based Real-time Contact Tracing at Scale

  • IDEAL: a Vector-Raster Hybrid Model for Efficient Spatial Queries over Complex Polygons

  • LSbM-tree: Re-enabling Buffer Caching in Data Management for Mixed Reads and Writes

  • Scalable Data Management for Big Medical Imaging Data

  • PAIS: Large Scale Spatial Data Management for Pathology Imaging

Publications

  • TENG, D., Liang, Y., Vo, H., Kong, J., and Wang, F. Efficient 3D Spatial Queries for Complex Objects. ACM Transactions on Spatial Algorithms and Systems(TSAS), (2022), pp. 1–26 (accepted)

  • TENG, D., Baig, F., Nehe, A., Emanuel, P., Kong, J., and Wang, F. GPU-based Real-time Contact Tracing at Scale. 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL)(2021), ACM, pp. 1-10, (24% acceptance rate)

  • TENG, D., Baig, F., Qiheng, S., Kong, J., and Wang, F. Ideal: A vector-raster hybrid model for efficient spatial queries over complex polygons. in the 22nd IEEE International Conference on Mobile Data Management (MDM)(2021), IEEE, pp. 1–10, (CCF C, 26% acceptance rate)

  • TENG, D., Kong, J., and Wang, F. Scalable and flexible management of medical image big data. Distributed and parallel databases (DAPD) 37, 2 (2019), pp. 235–250, (CCF C)

  • TENG, D., Guo, L., Lee, R., Chen, F., Zhang, Y., Ma, S., and Zhang, X. A low-cost disk solution enabling lsm-tree to achieve high performance for mixed read/write workloads. ACM Transactions on Storage (TOS) 14, 2 (2018),1–26, (CCF A)

  • TENG, D., Guo, L., Lee, R., Chen, F., Ma, S., Zhang, Y., and Zhang, X. Lsbm-tree: Re-enabling buffer caching in data management for mixed reads and writes. In2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)(2017), IEEE, pp. 68–79, (CCF B, 18.5% acceptance rate)

  • Abell-Hart, K., Rashidian, S., TENG, D., Rosenthal, R., and Wang, F. Where Opioid Overdose Patients Live Far From Treatment: Geospatial Analysis of Underserved Populations in New York State. In JMIR Public Health and Surveillance (JPHS), JMIR, 2022 (IF 3.5, accepted)

  • Baig, F.,TENG, D., Kong, J., and Wang, F. Spear: Dynamic spatio-temporal query processing over high velocity data streams. In IEEE 37th International Conference on Data Engineering (ICDE)(2021), IEEE, (CCF A)

  • Baig, F., Gao, C.,TENG, D., Kong, J., and Wang, F. Accelerating spatial cross-matching on cpu-gpu hybrid platform with cuda and openacc. Frontiers in big Data 3(2020), 14

  • Roy, M., Wang, F., Vo, H.,TENG, D., Teodoro, G., Farris, A. B., Castillo-Leon, E., Vos, M. B., and Kong, J. Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies. Laboratory Investigation 100, 10 (2020), 1367–1383, (IF 5.8)

  • Vo, H., Kong, J.,TENG, D., Liang, Y., Aji, A., Teodoro, G., and Wang, F. Mareia: a cloud mapreduce based high performance whole slide image analysis framework. Distributed and parallel databases (DAPD) 37, 2 (2019), 251–272, (CCF C)

  • Vo, H., Kong, J.,TENG, D., Liang, Y., Aji, A., Teodoro, G., and Wang, F. Cloud-based whole slide image analysis using mapreduce. In VLDB Workshop on Data Management and Analytics for Medicine and Healthcare(2016),Springer, Cham, pp. 62–77

Teaching Experiences

  • CSE 532: Theory of Database Systems (Stony Brook University), Teaching Assistant

    • Spring 2020

  • CSE 537: Artificial Intelligence (Stony Brook University), Teaching Assistant

    • Fall 2019

  • CSE 2111: Modeling and Problem Solving with Spreadsheets and Databases (the Ohio State University), Teaching Assistant

    • Fall 2015, Fall 2017

  • CSE 5339: Intermediate Studies in Parallel Computing (the Ohio State University), Teaching Assistant

    • Spring 2015

Academic Services

  • PC member

    • VLDB DMAH 2021

  • Sub-reviewer

    • ACM SIGSPATIAL 2019, 2020, 2021

    • ACM SIGMOD 2021, 2022

    • IEEE eSCIENCE 2021

    • IEEE Big Data 2019

  • Manuscript reviewer

    • ACM Transactions on Database Systems (TODS)

    • ACM Transactions on Spatial Algorithms and Systems (TSAS)

    • Springer Distributed and Parallel Databases (DAPD)

    • Concurrency and Computation: Practice and Experience