Sepp Hochreiter’s seminal works about the vanishing gradient and Long Short-Term Memory (LSTM) networks laid the foundation of what has now become known as Deep Learning. LSTM has been widely adopted in deep learning applications including research and commercial tools such as speech and language interfaces in smartphones.

Gerhard Widmer is one of the most highly cited researchers in the fields of Sound and Music Computing (SMC) and Music Information Retrieval (MIR). His teams in Linz and Vienna have effectively been defining state-of-the-art in tasks such as rhythm and tempo perception, real-time beat and music tracking as well as predictive modeling of expressive music performance, and are among the leading labs in general acoustic scene classification and event detection. Widmer's pioneering work has won him numerous top scientific awards and accolades both in Austria (Wittgenstein Award) and abroad (ERC Advanced Grant).

Günter Klambauer is a leading researcher for machine learning methods in Life Sciences. For the introduction of machine learning methods in molecular biology, he has received the Austrian Life Science Award. He is known for the invention of self-normalizing neural networks.

Martina Seidl is the head of the Institute for Symbolic Artificial Intelligence since October 2020. After her doctorate and 2 years as a research assistant at the TU Vienna, she researched and taught for 10 years at the Institute for Formal Models and Verification at the JKU. In her research, Martina Seidl mainly deals with the development of automatic reasoning techniques and their application in software verification and symbolic artificial intelligence.

Markus Schedl is well known for his work on recommender systems, user modeling, multimedia information retrieval, and web mining. He has led or was involved in several scientific projects funded by the Austrian Science Fund (FWF), the Austrian Research Promotion Agency (FFG), and the European Commission (EC).

Johannes Fürnkranz works in machine learning and data mining, in particular inductive rule learning, multi-label classification and preference learning, and their applications in game playing, web mining, and scientific data mining. He helped to shape the field of preference learning by coediting a book on this subject, and published a monograph on inductive rule learning. He is the editor-in-chief of the journal Data Mining and Knowledge Discovery.