This workshop is run by MSc of Logic students as part of the Seminar Mathematical Logic. The programof the workshop consists of student presentations and an invited lecture by Dr. Jan-Willem van de Meent. 

Workshop location: C1.112, Building 904 (Amsterdam Science Park)

Workshop Schedule:

13:00 (opening)

13:00 Joel Maxson: 

A logic for reasoning about probabilities (Fagin et al., 1990)

13:30 Simeon du Toit: 

Full and Partial Belief (Genin, 2019)

14:00 Jay Warnock: 

Making Solomonoff Induction Effective (Zimmermann & Cremers, 2012)

14:30 Simone Testino: 

The Random Graph (Cameron, 2013)

15:00 break 

15:10 Invited lecture (joined with FOAM seminar): 

Jan-Willem van de Meent (Amsterdam Machine Learning Lab, UvA)

"Differentiable and Probabilistic Programming for Scientific Computation"

16:10 break

16:30 Dennis Lindberg: 

On the Independence Assumption in Neurosymbolic Learning (van Krieken et al., 2024)

17:00 Vighnesh Iyer: 

Reasoning with Limited Resources and Assigning Probabilities to Arithmetical Statements (Gaifman, 2004)

17:30 (closing)

Invited Speaker (joined with FOAM seminar) :

In this talk, I will discuss where I see opportunities to apply AI in scientific computation. Whilst the mainstream of work in AI has predominantly focused on images and text, there lies a tremendous potential for AI in domains where data modalities and prediction tasks are much more diverse. I will give examples of applications of deep learning, differential programming, and probabilistic programming to these domains, and conclude with a perspective on how these methods can be combined to overcome bottlenecks in scientific computation.  

Bio: Dr. Jan-Willem van de Meent is an Associate Professor (Universitair Hoofddocent) at the University of Amsterdam. He directs the Amsterdam Machine Learning Lab (AMLab), co-directs the UvA Bosch Delta Lab, and directs the Amsterdam ELLIS unit. A major theme in his work is to understand what methods in AI have the potential to generalize across diverse application domains, and how we can think compositionally about such methods. To this end, his group combines methods development in generative AI and probabilistic programming with science-focused collaborations. In the past he has worked on problems in biophysics, neuroscience, healthcare, and robotics. His current collaborations focus on physical chemistry, fluid mechanics, and materials science. Jan-Willem is a co-author of a book on probabilistic programming. He has served as a founding co-chair of the international conference on probabilistic programming (PROBPROG) and as a program chair for the international conference on artificial intelligence and statistics (AISTATS). He was the recipient of an NWO Rubicon Fellowship and of the NSF CAREER award.