I am broadly interested in characterizing spatial patterns and processes through the use of quantitative geographic methods, which typically falls under the banners of spatial analysis and spatial statistics, geographic information science, and the emerging discipline of spatial data science. Overall, my research has targeted the development of multivariate spatial statistics and how they can be used to capture how relationships change by spatial and temporal contexts. This includes issues of theory, interpretation, scalability, and integration of traditional geographic models with novel "big" datasets, as well as applications in public health, crime, urban mobility, and transportation systems.

Zheng Liu is a Ph.D. candidate in the Center for Geographical Information Science, University of Maryland, College Park. His research interest lies in urban analytics and micro-mobility modeling using big data and machine learning techniques. He is working on multi-temporal mobility modeling of micro-mobility as his dissertation topic. He is also interested in the application of GIScience in the self-driving industry. Before joining UMD, he obtained his Bachelor of Science in Geographical Information Science at Peking University.

My interests include spatiotemporal theories and applications, big data, spatial data science, as well as geospatial modeling with machine learning. As a Ph.D student, I am eager to use those above to explore how humans perceive, interact with, and change space/environments. Particularly, I have a strong interest in bridging geospatial modeling with criminology, human mobility and health.

Matthew Tralka (he/him) is a faculty specialist in GIScience and Remote Sensing at the University of Maryland. At UMD, Matthew specializes in geospatial software engineering with an overall focus in advancing morphological image processing techniques for performance at scale and creating sustainable and accessible cyberinfrastructure for distributed and decentralized computing initiatives. He has a proven background on projects with NASA, USGS, DOD, IARPA, and the NGA. Explicitly, his latest work on IARPA’s WATCH project involves accelerating novel at-scale cloud detection workflows for heterogeneous datasets. In his free time, Matthew enjoys promoting data-equity initiatives and contributing to the open source community.

Coming from a background of math and computer science, Tyler is interested in methods development and algorithm design for spatial data science. He's also an advocate for the use of spatial methods in broader research. In his spare time, Tyler loves watching movies, playing video games, and biking. He's currently a Ph.D student at Arizona State University in the Spatial Analysis Research Center (SPARC).

Dave Cook is a data science, GEOINT and AI/ML practitioner in Washington, DC. He is passionate about the transformational power of AI/ML in GEOINT and MULTI-INT, particularly in building operational standards and best practices for data pipelining and readiness. Over his 25+ year career in the public and private sector, Dave has focused on solving complex challenges in law enforcement, intelligence, defense, health and across leading corporations worldwide. Believing that bad data is worse than no data, and good data is more a marathon than a sprint, Dave holds to the power of lifelong learning, disciplined training, and endless reinvention.