https://ugentt2k.github.io/), and is developing new fundamental research tracks on energy-based neural networks, as well as on health analytics, including drug design.
"> https://ugentt2k.github.io/), and is developing new fundamental research tracks on energy-based neural networks, as well as on health analytics, including drug design. " /> https://ugentt2k.github.io/), and is developing new fundamental research tracks on energy-based neural networks, as well as on health analytics, including drug design. "> https://ugentt2k.github.io/), and is developing new fundamental research tracks on energy-based neural networks, as well as on health analytics, including drug design. ">Thomas Demeester is assistant professor at IDLab, Ghent University - imec in Belgium.
After his master’s degree in electrical engineering (2005), he obtained his Ph.D. in Computational
Electromagnetics (FWO fellow) in 2009. His research interests then shifted to information retrieval (with a research stay at the University of Twente in The Netherlands, 2011), natural language processing (NLP) and machine learning (with a stay at University College London in 2016), and
more recently to Neuro-Symbolic AI. He co-leads the text-to-knowledge research cluster at IDLab
(https://ugentt2k.github.io/), and is developing new fundamental research tracks on energy-based neural networks, as well as on health analytics, including drug design.