Faculty & Staff Directory
- Associate 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). He received his Ph.D. in Computer Science, summa cum laude, under supervision of Dr. Hans-Peter Kriegel at Ludwig Maximilan University of Munich, Germany (LMU). According to the 2020 Times Higher Education World University Ranking, LMU is the best university in Germany and ranks #32 world-wide.
Dr. Züfle’s research focuses on mining spatial-temporal data with applications in transportation, epidemiology, social science, and urban computing. He is a computer scientist who is passionate to work interdisciplinary and has a special interest in Geosimulation as a new paradigm to combine large-scale data generation with data mining to improve decision-making.
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.
Improving predictions of COVID-19 spread by building an ensemble of existing prediction models. This research is funded by NSF under Award Abstract #2030685 "RAPID: An Ensemble Approach to Combine Predictions from COVID-19 Simulations"
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".
- National Science Foundation Grant DEB-2030685 "RAPID: An Ensemble Approach to Combine Predictions from COVID-19 Simulations" (Funding amount $199,998.00, Role: Co-PI, 05/15/2020-04/30/2021).
- ACM KDD Workshop on Prescriptive Analytics for the Physical World (PAPW 2020) Challenge on Mobility Intervention for Epidemics 4th Place Award.
- MDM 2020 Best Paper Award Runner-Up (2nd Place) on our paper titled "Semantically Diverse Path Search"
- PAKDD 2020 Outstanding Reviewer Award.
- ACM SIGSPATIAL GIS Cup 2019 1st Place Award Winner.