Sepp Hochreiter is heading the Institute for Machine Learning, the LIT AI Lab and the AUDI.JKU deep learning center at the Johannes Kepler University of Linz and is director of the Institute of Advanced Research in Artificial Intelligence (IARAI). He is regarded as a pioneer of Deep Learning as he discovered the fundamental deep learning problem: deep neural networks are hard to train, because they suffer from the now famous problem of vanishing or exploding gradients. He is best known for inventing the long short-term memory (LSTM) in his diploma thesis 1991 which was later published in 1997. LSTMs have emerged into the best-performing techniques in speech and language processing and are used in Google’s Android, in Apple’s iOS, Google’s translate, Amazon’s Alexa, and Facebook’s translation. Currently, Sepp Hochreiter is advancing the theoretical foundation of Deep Learning, investigates new algorithms for deep learning, and reinforcement learning. His current research projects include Deep Learning for climate change, smart cities, drug design, for text and language analysis, for vision, and in particular for autonomous driving.
Günter Klambauer is assistant professor for "Artificial Intelligence in Drug Design" at the LIT AI Lab and the Institute for Machine Learning of the Johannes Kepler University Linz. After studying mathematics and biology at the University of Vienna, Günter Klambauer started his research in the field of machine learning and bioinformatics in 2010 at the Johannes Kepler University Linz, where he received his doctorate in 2014. For the application of machine learning techniques in genetics and molecular biology he was awarded the Austrian Life Science Award 2012 and the Award of Excellence of the Austrian Ministry of Science in 2014. Over the past years, he has led several machine learning groups in large projects with several pharmaceutical companies. In an international scientific competition, the Tox21 Data Challenge, Günter Klambauer and his group developed the best artificial intelligence method for predicting the toxicity of chemicals. He is known for the development of "self-normalizing neural networks". His current research focuses on the development of Deep Learning and AI methods for use in Life Sciences. In 2020, he became member of the ELLIS society, which is focused on European excellence in machine learning.