Paper submission deadline: September 30, 2018 11:59 pm UTC
Acceptance notification: October 21, 2018
Camera ready deadline: November 30, 2018 11:59 pm UTC
Workshop: December 07, 2018 8.00-6:30
3. Workshop Overview
Understanding the evolution of a process over space and time is fundamental to a variety of disciplines. To name a few, such phenomena that exhibit dynamics in both space and time include propagation of diseases, variations in air pollution, dynamics in fluid flows, and patterns in neural activity. In addition to these fields in which modeling the nonlinear evolution of a process is the focus, there is also an emerging interest in decision-making and controlling of autonomous agents in the spatiotemporal domain. That is, in addition to learning what actions to take, when and where to take actions is crucial for an agent to successfully interact with dynamic environments. Although various modeling techniques and conventions are used in different application domains, the fundamental principles remain unchanged. Automatically capturing the dependencies between spatial and temporal components, making accurate predictions into the future, quantifying the uncertainty associated with predictions, real-time performance, and working in both big data and data scarce regimes are some of the key aspects that deserve our attention. Establishing connections between Machine Learning and Statistics, this workshop aims at:
(1) raising open questions on challenges of spatiotemporal modeling and decision-making,
(2) establishing connections among diverse application domains of spatiotemporal modeling, and
(3) encouraging conversation between theoreticians and practitioners to develop robust predictive models.
Theory: stochastic processes, deep learning/convolutional LSTM, kernel methods, chaos theory, time-frequency analysis, reinforcement learning for dynamic environments, dynamic policy learning, biostatistics, epidemiology, geostatistcs, climatology, neuroscience, etc.
Natural phenomena: disease propagation and outbreaks, environmental monitoring, climate modeling, etc.
Social sciences and economics: predictive policing, population mapping, poverty mapping, food resources, agriculture, etc.
Engineering/robotics: active data collection, traffic modeling, motion prediction, fluid dynamics, etc.
Ransalu Senanayake, The University of Sydney (rsen4557<at>uni.sydney.edu.au)
Neal Jean, Stanford University
Fabio Ramos, The University of Sydney
Girish Chowdhary, University of Illinois Urbana-Champaign