Prominent European Researcher, Dr. Luc de Raedt Delivers Opening CS Distinguished Lecture Series Talk for 2016
The Computer Science Distinguished Lecture Series started the 2016 year with Dr. Luc de Raedt, professor of research at the University of Leuven (KU Leuven) in Leuven, Belgium, prominent researcher from Europe, and an expert in intelligent systems, machine learning, and probabilistic programming.
On Friday, February 12th, Dr. Luc De Raedt delivered his talk, “Probabilistic (Logic) Programming: Concepts & Applications.” During his talk, Dr. Luc De Raedt focused on probabilistic logic programming languages, which naturally belongs to both of these paradigms as they combine the power of a programming language with possible world semantics. Dr. Luc De Raedt, introduced concepts underlying probabilistic logic programming, their semantics, different inference and learning mechanisms, as well as presenting recent extensions towards dealing with continuous distributions and dynamics.
In his talk, Dr. Luc De Raedt sketched a few emerging applications in bioinformatics, where it is used to analyze molecular profiling data in networks, and in robotics where it is used for tracking relational worlds where objects or their properties are occluded in real time and planning. Dr. Luc De Raedt discussed several open challenges and opportunities for research.
Dr. Luc De Raedt is a full professor (of research) in the Department of Computer Science at the University of Leuven (KU Leuven) in Belgium, and a former chair of Machine Learning at the Albert-Ludwigs-University in Freiburg, Germany. He served as program (co)-chair of European Conference on Artificial Intelligence in 2012, ICML 2005, and the key organizer and co-chair of the program committees of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) and the 12th European Conference on Machine Learning (ECML) in 2001.
Dr. Luc De Raedt’s research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is currently working on probabilistic logic learning (sometimes called statistical relational learning), which combines probabilistic reasoning methods with logical representations and machine learning, the integration of constraint programming with data mining and machine learning principles, the development of programming languages for machine learning, and analyzing graph and network data. He is also interested in applications of these methods to chemo- and bio-informatics, to natural language processing, vision, robotics and action- and activity learning.
He is an area editor of the Theory and Practice of Logic Programming, an action editor for the Journal of Machine Learning Research and the Machine Learning Journal, a former member of the advisory board and a former associate editor of the Journal of Artificial Intelligence Research. He is also a member of the editorial boards of New Generation Computing, AI Communications, Intelligent Data Analysis, Informatica, Data Mining and Knowledge Discovery, and the Journal of Applied Logic.
Dr. Luc De Raedt is well known for his work on inductive logic programming (combining logic with learning). Since 2000, he has been working towards a further integration of logical and relational learning with probabilistic reasoning (statistical relational learning and probabilistic programming), on inductive querying in databases, and on using declarative languages for data mining and machine learning.
Dr. Luc De Raedt will be delivering his talk this Friday, February 12th, at 11am in the TI Auditorium.
About the UT Dallas Computer Science Department
The UT Dallas Computer Science program is one of the largest Computer Science departments in the United States with over 1,600 bachelor’s-degree students, more than 1,100 master’s students, 160 PhD students, and 80 faculty members, as of Fall 2015. With The University of Texas at Dallas’ unique history of starting as a graduate institution first, the CS Department is built on a legacy of valuing innovative research and providing advanced training for software engineers and computer scientists.
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