A novel hybrid probabilistic framework for model validation 

Structure learning for manifolds and multivariate time series 

Risk assessment, intrinsic interpolation and computationally efficient models for systems under uncertainty 

Risk-aware path planning for autonomous underwater vehicles 

Active state learning from surprises in stochastic and partially-observable environments 

Modeling and predicting with spatial‐temporal social networks 

Utilizing real-world traffic data to forecast the impact of traffic incidents 

Decentralized real-time trajectory planning for multi-robot navigation in cluttered environments 

Brain-based prediction of chronic pain progression: a longitudinal study of urologic chronic pelvic pain syndrome using baseline resting state connectivity from the periaqueductal gray 

Predicting and modeling human behavioral changes using digital traces 

Information theoretic and probabilistic measures for power analysis of digital circuits 

Shrinkage methods for big and complex data analysis 

Forecasting directivity in large earthquakes in terms of the conditional hypocenter distribution 

Sense and sensibility: statistical techniques for human energy expenditure estimation using kinematic sensors 

Invariant representation learning for robust and fair predictions 

Gaze following in natural scenes: quantifying its role in eye movements, towards a more complete model of free-viewing behavior 

Learning personal thermal comfort and integrating personal comfort requirements into HVAC system control loop 

Design and analysis of large scale antenna systems 

Probabilistic analysis of power dissipation in VLSI systems 

Validating the HAZUS coastal surge model for Superstorm Sandy 

Learning affordances through interactive perception and manipulation 

Pandemic prediction and control with integrated dynamic modeling of disease transmission and healthcare resource optimization 

Contingency severity and individual performance in a probabilistic game setting 

The value of task information on bankruptcy predictions 

Model-based approaches to objective inference during steady-state and adaptive locomotor control