Recent & Upcoming Talks

Computer graphics focuses on rendering high-quality 2D images from 3D scenes, with most research focusing on simulating elements of the …

Our streets shape us in more ways than we can imagine. In this talk, I offer a unique view into the promising applications of machine …

Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP …

As robots move from labs and factories into human-centric spaces, it becomes progressively harder to predetermine the environments, …

In their seminal paper in 2002, Joe Cartwright and Mads Huuse referred to 3D seismic reflection data as the ‘Geological Hubble’, …

Robots are envisioned as capable machines who easily navigate and interact in a world built for humans. However, looking around us we …

The Earth’s system dynamics has an intrinsically multi-scale and nonlinear nature, which fundamentally affects the ability to …

To achieve long-term climate change goals, such as limiting global warming to 1.5 or 2°C, there must be a global effort to decide and …

Way too often, observations from weather stations fall outside of the ensemble generated by initial uncertainty in weather models. This …

Robots are envisioned as capable machines who easily navigate and interact in a world built for humans. However, looking around us we …

Recent advancement of AI technologies like deep learning or deep reinforcement learning greatly enhance robot’s …

Variational inference (VI) is a versatile framework to perform approximate inference in probabilistic models. Recent progress made it …

In the real world, a robot might face any unanticipated situation, such as damaged motors, unseen terrain conditions, faults in the …

This talk will describe a framework for constructing simulation-based kernels for ultra data-efficient Bayesian optimization (BO). We …

In order to leave the factory floors and research labs, future robots must abandon their stiff and pre-programmed movements and be …

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their …

I will discuss multiple-data-source prediction and modelling problems arising in a number of fields, for instance in omics-based …

We derive a family of Monte Carlo estimators for gradients of expectations, which is related to the log-derivative trick, but involves …

In machine learning, new learning algorithms are designed by borrowing ideas from optimization and statistics followed by an extensive …