FireCast: Leveraging Deep Learning to Predict Wildfire Spread. - David Radke; Anna Hessler; Dan Ellsworth (IJCAI, 2019)

Can Future Wireless Networks Detect Fires? - David Radke; Omid Abari; Tim Brecht; Kate Larson (BuildSys, 2020)

Beyond Measurement: Extracting Vegetation Height from High Resolution Imagery with Deep Learning. - David Radke; Daniel Radke; John Radke (Remote. Sens., 2020)

Exploring the Benefits of Teams in Multiagent Learning. - David Radke; Kate Larson; Tim Brecht (IJCAI, 2022)

Identifying Completed Pass Types and Improving Passing Lane Models. - David Radke; Tim Brecht; Daniel Radke (LINHAC, 2022)

An Analysis of Engineering Students' Responses to an AI Ethics Scenario. - Alexi Orchard; David Radke (AAAI, 2023)

The Importance of Credo in Multiagent Learning. - David Radke; Kate Larson; Tim Brecht (AAMAS, 2023)

Presenting Multiagent Challenges in Team Sports Analytics. - David Radke; Alexi Orchard (AAMAS, 2023)

Towards a Better Understanding of Learning with Multiagent Teams. - David Radke; Kate Larson; Tim Brecht; Kyle Tilbury (IJCAI, 2023)

Analyzing Passing Metrics in Ice Hockey using Puck and Player Tracking Data. - David Radke; Jaxin Lu; Jackson Woloschuk; Tin Le; Daniel Radke; Charlie Liu; Tim Brecht (LINHAC, 2023)

Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams. - David Radke; Kyle Tilbury (CoRR, 2023)

Simulating Tracking Data to Advance Sports Analytics Research. - David Radke; Kyle Tilbury (AAMAS, 2025)

Dynamic Reward Sharing to Enhance Learning in the Context of Multiagent Teams. - Kyle Tilbury; David Radke (AAMAS, 2025)