Welcome to our research lab at the Computer Science & Engineering Department, University of Minnesota, Twin Cities.

Our research focuses on applied machine learning and data science, thriving at the intersection of computer science and spatial sciences. We create advanced machine learning algorithms and intelligent systems to tackle real-world challenges by:

• Dicovering, gathering, integrating, and interpreting data from diverse sources
• Leveraging multimodal data characterized by diverse spatial and temporal scales, completeness, and coverage

We are currently concentrating on building machine learning frameworks that exploit spatial data properties and structural knowledge to address challenges in natural language processing, computer vision, and spatiotemporal data prediction & forecasting. We collaborate with domain experts in fields such as:

• Transportation
• Health and public health
• Human geography and history
• Digital humanities, library science, and museums
• Agriculture

Our work involves analyzing a wide range of datasets, including:

• Scenic photos (e.g., Google Street View)
• Complex scanned documents (e.g., scanned maps)
• In-situ sensor observations (e.g., air quality monitors, ECG data)
• Remote sensor data (e.g., satellite imagery, elevation data)
• Time series data (e.g., weather and traffic data)
• Natural language data
• Graph data (e.g., network logs, linked data)
• Spatial data (e.g., moving trajectories)

Learn more about our research and find our Github (In 2021, we transitioned from the Spatial Sciences Institute at the University of Southern California (USC) to Minnesota. Explore our lab page at USC.)