The scientists behind UL's Cheminformatics Tool Kit
CRAIG ROWLANDS, PhD, D.A.B.T., Senior Toxicologist, UL
Dr. Craig Rowlands is Senior Toxicologist with UL where he provides leadership in the development of new business models and capabilities for safety assessments of consumer products. With over two decades of experience, Craig is an expert in navigating regulatory compliance for new substances and products through delivery of the appropriate safety data and risk assessments. His research and policy advocacy efforts are in the areas of predictive toxicology with a focus on sustainability and the development of non-animal alternative testing strategies towards reducing environmental, health and safety risks of chemicals.
THOMAS HARTUNG, MD, PhD, The Johns Hopkins University Bloomberg School of Public Health
Thomas Hartung is a recognized authority on toxicology with a broad background in clinical and experimental pharmacology. The goal of his work is to create a paradigm shift in toxicity testing to improve public health. With more than 350 publications to his name, he previously served as the head of the European Center for the Validation of Alternative Methods of the European Commission (2002-2008), and is involved in the implementation of the 2007 NRC vision document “Toxicity Testing in the 21st Century – a vision and a strategy.” Hartung relocated to the US early 2009 to establish the directorship for the Center for Alternatives to Animal Testing (CAAT) a laboratory for developmental neurotoxicity research based on genomics and metabolomics the respective technologies were made available by a Thought-Leader Award from Agilent.
TOM LUECHTEFELD, PhD, CANDIDATE, Toxtrack
Tom Luechtefeld co-founded Toxtrack and brings together an academic and entrepreneurial background in computer science and toxicology. He is responsible for development of computational toxicology algorithms at Toxtrack. Tom integrates machine learning approaches with traditional chemical informatics to make predictive models for chemical hazards. With an academic training in molecular toxicology, machine learning and applied mathematics, Tom builds applications leveraging big data to identify chemical risks. Tom is a PhD candidate in molecular toxicology at Johns Hopkins and has formal training in machine learning and computer science at Johns Hopkins.