(Overview Coming Soon!)
Speakers
Lauren Wengerd, Ohio State University, Depart of Rehabilitation Science
Dave Friedenberg, Battelle
Moderator
Nancy McMillan, Battelle
Abstract
Coming Soon!
About the Speakers
Dr. Lauren Wengerd's educational and career experiences have led her to the intersection of healthcare, research, and business. She recently completed a PhD in Health and Rehabilitation Sciences with a graduate minor in neuroscience at The Ohio State University. Her research primarily focuses on identifying new and effective interventions to maximize function and independence for adults with neurological conditions. She is passionate about identifying not only effective but also cost-effective approaches to healthcare. She regularly incorporates cost-effectiveness analyses into clinical study design to bridge the gap between research and clinical care. Dr. Wengerd holds a master’s degree in occupational therapy and a bachelor’s degree in business administration/marketing, both of which continue to serve her well in research and clinical consulting roles. She is actively pursuing a career that will cultivate her knowledge and passion for healthcare, research, and business to ultimately enhance the quality of life and functional independence of individuals with neurological injuries.
Dr. David Friedenberg is a Principal - Data Science and Neurotechnology and the Team Lead for Machine Learning/AI in the Advanced Analytics group at Battelle. He's the PI on several neurotechnology efforts developing new AI-powered technologies to help improve the lives of people living with motor impairments due to neurological injuries like spinal cord injuries and stroke. An experienced data scientist with consulting experience across several disciplines he is passionate about developing AI/ML-driven solutions to challenging problems for the betterment of humanity.
About the Moderator
Nancy McMillan currently serves as Data Science Research Leader within Battelle’s Health Research & Analytics Business Line. For a diverse set of federal government clients, she currently leads development of a large language model (LLM) based biocuration acceleration pipeline and user tool, development of pipelines, analytics, and visualizations of electronic initial case reporting data, and development of analytical methods for achieving abbreviated new drug application (ANDA) approval for an agile drug manufacturing technology. Nancy has a long history of collaborative work across Battelle bringing statistics and machine learning to Battelle’s deep capability in biology, chemistry, and material science. As a researcher and Project Management Professional, Nancy has worked and published on environmental exposure and risk assessment; transportation safety benefits; quantitative risk assessment related to chemical, biological, radiological and nuclear (CBRN) terrorism; bio surveillance; and bioinformatics. She managed the Health Analytics Division from 2017-2023, a team of approximately 100 data scientists that supports Battelle’s contract research business. Nancy is a member of the Board of Trustees for the National Institute of Statistical Sciences (NISS), the Chair of NISS’s Affiliates Committee, and a member of the Organ Procurement and Transplantation Network’s Data Advisory Committee.
About the NISS-CANSSI
Collaborative Data Science Web Series:
The NISS-CANSSI Collaborative Data Science initiative that the National Institute of Statistical Sciences (NISS) in collaboration with the Canadian Statistical Sciences Institute (CANSSI) brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.
Goals of the Initiative
The goal is to foster progress in:
- Developing new ideas for experimental and observational data-driven learning and discovery that address key questions at the cutting edge of science and scientific deduction;
- Quantifying and summarizing uncertainty in data-driven theories, as well as complex Data Science models, algorithms, and workflows; and
- Establishing new practices for scientific reproducibility and replicability through Data Science.
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