Faculty & Staff Directory
- Assistant Professor
PhD in Computer Science, Summa Cum Laude, Ludwig Maximilian University of Munich, 2013
Andreas Züfle is an assistant professor at the Department of Geography and Geoinformation Science at George Mason University (GMU). In 2013, Dr. Züfle received his Ph.D. in Computer Science under supervision of Dr. Hans-Peter Kriegel at LMU. For his Ph.D., Dr. Züfle received the highest academic honor "Summa Cum Laude", which is used to distinguish the 1% best Ph.D. students at LMU. Among more than 50 Ph.D. students supervised by Dr. Kriegel, only one other student received the same honor. According to the 2018 Times Higher Education World University Ranking, LMU is the best university in Germany, ranking #34 world-wide.
Dr. Züfle’s research expertise includes big spatial data, spatial data mining, social network mining, and uncertain database management. His research quest is to work interdisciplinary and bridge the gap between data-science and geo-science, two fields working independently on often identical research problems. To bring these communities together, Dr. Züfle has joined the Department of Geography and Geoinformation Science at GMU in 2016. Since 2016, Dr. Züfle research has received more than $500,000 in research grants by the National Science Foundation (NSF) and he is the PI on a $1,500,000 cooperate agreement by Defense Advanced Research Projects Agency (DARPA). Since 2011, Dr. Züfle has published more than 80 papers in refereed conferences and journals having an h-index of 18. For his research, Dr. Züfle has received five best research paper awards, three best vision paper awards, and one best demonstration paper awards.
Supporting the DARPA Defense Sciences Office as a Principal Investigator in developing capabilities to "help test and evaluate different methods and tools for analyzing complex social phenomena". This project, which is running from 01/01/2018-06/30/2020 is supported by a $1.5 million cooperative agreement.
Data-driven research to improve traffic conditions, improve well-being and to reduce greenhouse gas emissions by using publicly available mobility data in urban areas. This research is funded by National Science Foundation Grant CCF-1637541 " AitF: Collaborative Research: Modeling movement on transportation networks using uncertain data".
Uncertain Spatial Data: Leveraging uncertainty information in spatial data for informed decision making by using inferential statistics to assess the reliability of data mining results.
Big Spatial Data Mining: Solutions for spatial data mining (clustering, classification, co-location mining) that scale to very large sets (billions of points, lines and polygons) of spatial data.
My goal as an educator is to prepare students for today's data-intensive jobs in academia and industry. This is paramount, as the Unites States alone "faces a shortage of 440,000-490,000 people with deep analytical skills in 2018" as assessed by McKinsey & Company, while "Data Scientist" ranks #1 best job in America according to Glassdoor.com (as of 09/16/2019).
Towards this goal, I teach courses in Data Mining, Quantitative Methods, Algorithms, and Programming, including:
GGS 787: Scientific Data Mining for Geo‐informatics
GGS 650: Introduction to GIS Programming
GGS 399 / GGS 590: GIS Algorithms
GGS 300: Quantitative Methods for Geographical Analysis
GGS 210: Introduction to Spatial Computing
- Schmid, Klaus Arthur, and Andreas Züfle. "Representative Query Answers on Uncertain Data." In Proceedings of the 16th International Symposium on Spatial and Temporal Databases, pp. 140-149. ACM, 2019. (Best Paper Award Runner-Up)
- Kavak, Hamdi, Joon-Seok Kim, Andrew Crooks, Dieter Pfoser, Carola Wenk, and Andreas Züfle. "Location-Based Social Simulation." In Proceedings of the 16th International Symposium on Spatial and Temporal Databases, pp. 218-221. ACM, 2019. (Best Vision Paper Award Runner Up)
- Schubert, Erich, Alexander Koos, Tobias Emrich, Andreas Züfle, Klaus Arthur Schmid, and Arthur Zimek. "A framework for clustering uncertain data." Proceedings of the VLDB Endowment 8, no. 12 (2015): 1976-1979.
- Züfle, Andreas, Tobias Emrich, Klaus Arthur Schmid, Nikos Mamoulis, Arthur Zimek, and Matthias Renz. "Representative clustering of uncertain data." In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 243-252. ACM, 2014.
- Bernecker, Thomas, Hans-Peter Kriegel, Matthias Renz, Florian Verhein, and Andreas Zuefle. "Probabilistic frequent itemset mining in uncertain databases." In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 119-128. ACM, 2009.
- Defense Advanced Research Projects Agency (DARPA) under cooperative agreement No.HR00111820005 to develop capabilities to help test and evaluate different methods and tools for analyzing complex social phenomena ($1,500,000, Role: PI, 01/01/2018-06/30/2020)
- National Science Foundation Grant CCF-1637541 "AitF: Collaborative Research: Modeling movement on transportation networks using uncertain data" (Funding amount $507,852.00, Role: Co-PI, 09/01/2016-08/01/2020).
- SSTD 2019 Best Paper Award Runner-Up (2nd Place) on our paper titled "Representative Query Answers on Uncertain Data".
- SSTD 2019 Best Vision Paper Award Runner-Up (2nd Place) on our paper titled "Location-Based Social Simulation".
- SSTD 2017 Best Vision Paper Award on our paper titled: "A Unified Framework to Predict Movement".