NISS-CANSSI Collaborative Data Science Webinar: Data Science Techniques for Control of Assistive Devices After Neurological Injury

• Advanced Data Science Methods: Explore the latest techniques in data analysis and machine learning that are being used to enhance the functionality and responsiveness of assistive devices.
• Neurological Injury and Rehabilitation: Understand the challenges faced by individuals with neurological injuries and how data-driven solutions can aid in their rehabilitation.
• Case Studies and Applications: Learn from real-world examples where data science has been successfully implemented to control assistive devices, improving patient outcomes.
• Future Directions: Discuss the future of assistive technology and the role of data science in developing more sophisticated and personalized solutions.

Speakers

Lauren Wengerd, Assistant Professor, Department of Neurological Surgery, The Ohio State University; Joint appointment at the NeuroTech Institute.

Dave Friedenberg, Principal Data Science and Neurotechnology and Team Lead for Machine Learning/AI, Advanced Analytics Group, Battelle 

Moderator

Nancy McMillan, Battelle

About the Speakers

Dr. Lauren Wengerd is an Assistant Professor in the Department of Neurological Surgery at The Ohio State University with a joint appointment at the NeuroTech Institute. With a clinical background in occupational therapy and a PhD in Health and Rehabilitation Sciences, she leads interdisciplinary research at the intersection of neurorehabilitation, neurotechnology, and patient-centered care. Her work focuses on advancing recovery and independence for individuals with neurological conditions through the development and implementation of innovative neurotechnologies. See Profile

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.

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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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Data Science Techniques for Control of Assistive Devices After Neurological Injury

Date: Thursday, June 12, 2025 at 1-2pm ET

 

Speakers: Lauren Wengerd, Ohio State University, Depart of Rehabilitation Science and Dave Friedenberg, Battelle; Moderator: Nancy McMillan, Battelle