Workshop on Machine Learning and AI Applications in Astrophysics and Cosmology Associated with the IJCAI-09 conference, Pasadena, CA, July 16-17, 2009 Possibly even the final agenda, but it could change: Thursday, July 16 8:30 - 9:00 Coffee Morning session, chair: A. Gray 9:00 - 9:20 Setting the stage: the goals of the workshop [Djorgovski, Gray] 9:20 - 9:45 An overview of ML/AI applications in astrophysics, and some outstanding and upcoming challenges Ball: Data Mining and Machine Learning in Astronomy Borne: The VO and Large Surveys: What More Do We Need? 9:45 - 10:00 Discussion 10:00 - 10:30 Coffee break / informal discussion / posters 10:30 - 11:15 Some AI/ML applications and challenges in astronomy & cosmology Longo: Astronomical data mining: Renaissance or dark age? Connolly: Scaling Up and Scaling Out Myers: The Death of Spectroscopy 11:15 - 12:00 Discussion 12:00 - 1:30 Lunch Afternoon session, chair: R. White 1:30 - 2:15 Time-domain astronomy: classification of transients and variables, robotic telescope networks Mahabal: Combining diverse classifiers Khardon: From Raw Measurements to Clean Catalogs: Automatic Filtering and Classification of Variable Stars in the MACHO Survey Wozniak: Mining the sky in real time 2:15 - 3:00 Discussion 3:00 - 3:30 Coffee break / informal discussion / posters 3:30 - 4:15 Vision from AI/ML and statistics, part I Babu: Understanding 21st Century Astronomical Data Cubes van Dyk: Astrostatistics: Complex Models and Complex Questions Schneider: Active Learning for Fitting Simulations to Observational Data 4:15 - 5:00 Discussion 5:00 Adjourn 6:00 - ? Workshop dinner: Celestino restaurant Friday, July 17 8:30 - 9:00 Coffee Morning session, chair: TBD 9:00 - 9:40 Computer vision, tracking, and astrometry Jojic: Could existing computer vision tools assist/replace humans in the Galaxy Zoo? Kubica: Scaling up Data Streams for Asteroid Tracking Roweis: Making The Sky Searchable: Large Scale Astronomical Pattern Recognition 9:40 - 10:00 Discussion 10:00 - 10:30 Coffee break / informal discussion / posters 10:30 - 11:20 Vision from AI/ML and statistics, Part II: diffusion maps, virtualization and intelligent agents Freeman: Measurement Error and Estimator Bias Lee: High-dimensional Inference for Large Data Sets Sen: Separating Signal from Background Graham: Philosophical Languages in the 21st Century 11:20 - 12:00 Discussion 12:00 - 1:30 Lunch Afternoon session, chair: J. Mazzarella 1:30 - 1:45 Vision from AI/ML: knowledge capture, workflows Gil: Semantic Workflow Reasoning for Scientific Data Analysis 1:45 - 2:15 Discussion 2:15 - 3:00 Open discussion: scalability of data mining algorithms [moderators: Gray, Longo, Connolly] 3:00 - 3:30 Coffee break / informal discussion / posters 3:30 - 4:30 Open discussion: Emerging themes and new ideas [moderators TBD] 4:30 - 4:45 Concluding comments and next steps [Djorgovski, Gray] 4:45 - 5:00 Final discussion 5:00 Adjourn