Friday, December 07, 2018 at Room 513ABC
Abstract: 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 efficiently and safely operate in 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: deep learning/convolutional LSTM, kernel methods, chaos theory, 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 and economics: predictive policing, population mapping, poverty mapping, food resources, agriculture, etc.
Engineering/robotics: active data collection, traffic modeling, motion prediction, fluid dynamics, spatiotemporal prediction for safe autonomous driving, etc.
|08:30 AM||Christopher Wikle (Uni. of Missouri): Introduction to spatiotemporal modeling (Invited talk)|
|09:45 AM||Spotlight talks (session 1) (Spotlight talks)|
|Denisa Roberts, David Kozak, Kehinde Owoeye, astrid dahl, Abdi Dirie, Wei-Cheng Chang, Vladimir Ivashkin|
|10:00 AM||Modeling Rape Reporting Delays Using Spatial, Temporal and Social Features (Contributed talk)|
|10:15 AM||Spotlight talks (session 2) (Spotlight talks)|
|Sophie Giffard-Roisin, Marc Rußwurm, Esra Suel, Binh Tang, Harshal Maske, Daniel Neill, Doyup Lee|
|10:30 AM||Coffee break + poster session 1 (Coffee break + poster session)|
|11:00 AM||Stefano Ermon (Stanford University): Weakly Supervised Spatio-temporal Regression (Invited talk)|
|11:30 AM||Long Range Sequence Generation via Multiresolution Adversarial Training (Contributed talk)|
|11:45 AM||Modeling Spatiotemporal Multimodal Language with Recurrent Multistage Fusion (Contributed talk)|
|Paul Pu Liang|
|12:00 PM||Spotlight talks (session 3) (Spotlight talks)|
|Farzaneh Mahdisoltani, Frederik Kratzert, SUBBAREDDY OOTA, Mehul Motani, Tryambak Gangopadhyay, Sathwik Tejaswi Madhusudhan, Marc Rußwurm, Mahta Mousavi, Mihir Jain|
|12:20 PM||Lunch break|
|01:45 PM||A Nonparametric Spatio-temporal SDE Model (Contributed talk)|
|02:00 PM||Spotlight talks (session 4) (Spotlight talks)|
|Anwar Walid, Yitong Li, Ehsan Pajouheshgar, Oliver Hennigh, Seongchan Kim, Vaibhav Kulkarni, Koh Takeuchi|
|02:15 PM||Ani Hsieh (UPenn): Modeling, Tracking, and Learning Coherent Spatiotemporal Features in Geophysical Flows (Invited talk)|
|M. Ani Hsieh|
|02:45 PM||Spotlight talks (session 5) (Spotlight talks)|
|Alexis Asseman, Roman Marchant, RAKSHIT TRIVEDI, Murali Narayanaswamy, Massi AMROUCHE, Henry Martin, Nelson FERNANDEZ PINTO|
|03:00 PM||Coffee break + poster session 2 (Coffee break + poster session)|
|03:30 PM||Chelsea Finn (UCBerkeley / Google Brain): Learning Generalizable Behavior through Unsupervised Interaction (Invited talk)|
|04:00 PM||Girish Chowdhary (UIUC): Spatiotemporal Learning for Enabling Agricultural Robotics (Invited talk)|
|04:30 PM||Quantile Regression Reinforcement Learning with State Aligned Vector Rewards (Contributed talk)|
|04:45 PM||Path Planning for Mobile Inference of Spatiotemporally Evolving Systems (Contributed talk)|
|05:00 PM||Fabio Ramos (Uni. of Sydney): Learning and Planning in Spatial-Temporal Data (Invited talk)|
|05:30 PM||Tomaso Poggio (MIT): Dynamical System Theory for Deep Learning (Invited talk)|
|06:00 PM||Panel Discussion|