Lecture

Join us for Dr. Ricard Solé's WICI Talk: "Synthetic evolutionary transitions: from cells to brains and ecosystems"

Dr. Ricard Solé is a Professor, at the Complex Systems Lab, in Barcelona Biomedical Research Park, Universitat Pompeu Fabra.   He will present on April 24th, 2018, in DC 1302 at 2:00 p.m.

Abstract:  Evolution is marked by well-defined events involving profound innovations

Dr. Carla Restrepo is a Professor at the College of Natural Sciences, Biology, UPR, Puerto Rico.

She will be presenting her WICI Talk : "From sandpiles to real mountains - Complex dynamics of tropical mountainscapes mediated by landslides" on February 27, 2018 at 2 p.m. in the Davis Centre Rm.1302.  

Monday, October 2, 2017 2:00 pm - 3:00 pm EDT (GMT -04:00)

WICI Talk: Dr. Johan Koskinen -Analysing covert networks from unstructured sources

Understanding covert and criminal behaviour from a social network perspective is gathering increasing currency. While the standard social network paradigm assumes that network data has been collected though eliciting ties from respondents in a predefined set of individuals, covert networks pose obviously challenges in several respects. Firstly, the individuals in the network might not be known a prior. Secondly, what constitutes a relevant set of individuals and ties might be ambiguous.

There have been broad advances in the fields of Artificial Intelligence and Machine Learning (AI/ML) in the past decade, especially in the areas of Deep Learning and Reinforcement Learning (RL) which allow us to more easily learn predictive models and control policies for large, complex systems than ever before. One subset of problems that remains very challenging are domains that contain some form of spatially spreading process (SSP) where some local features change over time across based on proximity in space.

Deep learning has given rise to a major revolution in the field of artificial intelligence (AI). A major challenge with the democratization and proliferation of deep learning as commodity AI for all is the sheer complexity of current deep neural networks, making them ill-suited for operational use in a large number of scenarios.