NISS-Merck Meet-Up on Open Source Software in Pharma

About the Speakers

Andy Nicholls,  (R Validation Hub, GSK)  

Andy Nicholls is Head of Statistical Data Sciences at GSK.  He has been a user and strong advocate for the use of R for over 15 years.  As part of a Data Science capability development objective, Andy is responsible for driving the R adoption initiative within GSK Biostatistics.  His team helped create Biostatistics’ first dedicated analytics platform for R and developed a world-wide R training programming.  The team has also led the development of several R-based tools and applications to assist the rollout and adoption of R as a clinical and non-clinical reporting capability.  Within the wider pharmaceutical industry, Andy is the lead for the R Validation Hub, a collaboration to support the adoption of R within a biopharmaceutical regulation setting.

Andy trained as a mathematician and statistician.  He has an MMath Mathematics from University of Bath and MSc Statistics with Applications in Medicine from University of Southampton.  Prior to re-joining GSK in 2017 Andy was the Head of Data Science Consultancy at Mango Solutions.

Paul Schuette,  (FDA-CDER) 

Paul Schuette is the Scientific Computing Coordinator for the Office of Biostatistics in the Center for Drug Evaluation and Research, and is a member of the OB Analytics and Informatics Staff.    Paul joined the FDA in 2008 after previously working in academia and as a government contractor.  He is the PI for a Cooperative Research and Development Agreement (CRADA) for detecting data anomalies in clinical trial data, and is a member of FDA's Scientific Computing Board, the High Performance Computing Governing Advisory Board, FDA's Modeling and Simulation working group, and the Interagency R Users group.

Ted Lystig, (Medtronic)

Dr. Theodore Lystig is the Global Head of Biostatistics and a Technical Fellow at Medtronic, where he provides leadership and guidance in the use of robust statistical and research design methods throughout the company. He also holds the position of Adjunct Assistant Professor within the Division of Biostatistics at the University of Minnesota. Dr. Lystig is an elected Fellow of the American Statistical Association (ASA). He is a founding officer and past Chair for the Section on Medical Devices and Diagnostics within the ASA, an Executive Committee member of the Clinical Trials Transformation Initiative (CTTI), and a member of the Scientific Oversight Committee of the Medical Device Epidemiology Network (MDEpiNet). He received his B.A. degree in Mathematics from St. Olaf College in Northfield, Minnesota, his M.S. and Ph.D. degrees in Biostatistics from the University of Washington in Seattle, and was a Postdoctoral Fellow at Chalmers Technical University in Gothenburg, Sweden, in statistical genetics. Prior to joining Medtronic in 2011, he held positions of increasing responsibility as a biostatistician in the pharmaceutical industry for AstraZeneca in Molndal, Sweden, and Boehringer Ingelheim Pharmaceuticals in Ridgefield, Connecticut. Dr. Lystig is an internationally recognized industry leader in statistical methodology, especially in the area of active surveillance for medical devices. He is a frequent speaker at international statistics meetings and has given invited seminars at venues such as Harvard School of Public Health, ANVISA, and US FDA.

Tarek Haddad, (Medtronic)

Tarek Haddad is a Technical Fellow and Director of the Machine Learning and Statistics Group within R&D at Medtronic. His team’s area of focus is the development of machine learning/AI algorithms and stochastic models for improving patient outcomes, efficiencies in Patient Management, and user experience for both patients and health care providers, as well as improving the efficiency of clinical trial through innovative designs and methods. At Medtronic Tarek is helping to set the vision for AI, and developed the knowledge, talent, and Hardware/software infrastructure needed to develop and deploy advanced AI algorithms into Medtronic’s business.  He specializes in Machine learning with a focus in Deep learning, Reinforcement learning, and Bayesian modeling and has numerous publications and patents in these areas. He chairs the Medical Device Innovation Consortium working group on “clinical trials informed by simulation and bench”. He holds a MS in Biostatistics and a BA in Mathematics both from the University of Minnesota and has a Master Black Belt in Design for Six Sigma & Design for Reliability and Manufacturing.