Djork-Arné Clevert joined Bayer in 2015 and is since May 2019 the Director of the Machine Learning Research department at Bayer AG. He has a background in computer science and received his doctorate on machine learning for computational biology. He was a senior scientist in the prestigious Hochreiter Lab at the Institute of Bioinformatics at Johannes Kepler University from 2007 to 2015. He has been Co-PI in
several large projects with pharma industry, in particular, transcriptome analysis and statistical genetics with Johnson and Johnson and Merck Serono Geneva, respectively. His research has mainly been concerned with microarray data in earlier years. Later he shifted this research focus to the prediction of biological effects of compounds with
methods, such as, deep neural networks. A highlight of his Marie Curie fellowship was the introduction of the Exponential Linear Units (ELUs), which has become a de-facto standard in the field of deep learning. Dr.Clevert is co-organizer of the international conference “Critical Assessment of Massive Data Analysis (CAMDA)” and of the international workshop on “Deep Learning for Precision Medicine”. He is promoter of multiple master and doctoral projects as well as of 5 completed postdoctoral projects. He is author of more than 40 publications in international peer-reviewed journals (h-index 19 with 1210 citations in 2019 and mores than 5000 citations in total) and books.
Floriane Montanari is a research scientist in the Machine Learning Research department at Bayer AG. She has a background in bio-, chemo- and pharmacoinformatics. Her doctoral degree dealt with the in-silico prediction of inhibition of liver ABC-transporters. She has been an active member of the European IMI project “eTOX” within the University of Vienna where she built predictive models related to liver toxicity. Her research now focuses on deep learning methods for predicting properties of small molecules including ADMET properties and biological activity. She recently obtained internal funding for a project on explainable AI.