NISS AI, Statistics and Data Science in Practice Webinar: Jared Schuetter - Machine Learning for Airborne Biological Hazard Detection

Tuesday, March 11, 2025 at 12-1:30pm ET

Overview

This session will explore the use of machine learning for detecting and identifying airborne biological hazards. Attendees will learn how supervised and unsupervised learning techniques can analyze spectral data to differentiate between harmful and benign substances. The speaker will discuss challenges related to data preprocessing and model accuracy in dynamic environments. Case studies will illustrate real-world applications in public health and national security. The importance of rapid detection and classification in mitigating risks will be emphasized. Practical strategies for deploying machine learning models in field settings will also be shared.

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Speaker

Jared Schuetter, Principal Data Scientist at Battelle

Moderator

Nancy McMillan, Data Science Research Leader, Health Research & Analytics Business Line at Battelle

Abstract

Abstract Coming Soon!

Event Disclaimer

The views and opinions expressed by the speakers during this event are their own and do not necessarily reflect the views, positions, or policies of their employers, affiliated organizations, or any other entity. The speakers are participating in a personal capacity, and their statements should not be attributed to their respective companies or institutions.

About the Speaker

Jared Schuetter is a Principal Data Scientist at Battelle with over 15 years of experience tackling complex and innovative challenges that demand the integration of statistical methods and machine learning/AI techniques. Jared’s expertise spans the entire technical pipeline, from conceptualizing solutions to implementation. His skillset includes exploratory data analysis (e.g., dimension reduction and clustering), predictive modeling using both statistical and machine learning approaches, prototype algorithm and GUI development, visualization and reporting, and embedding algorithms into hardware systems. Throughout his career, Jared has contributed to diverse and impactful projects, such as threat prediction and signal processing for DNA sequences, genomic forensic analysis, classification of chemical and biological spectral signatures, and image processing in various domains. His work also extends to surrogate modeling for CO2 sequestration studies, machine learning applications for the oil and gas industry, forensic geolocation, and automating analytical workflows. Jared is proficient in MATLAB, R, C#, Java, and Python, with his coding skills ranked by proficiency. His ability to translate complex data challenges into actionable solutions has made him a leader in his field, and his innovative approaches continue to drive advancements in applied data science and machine learning.

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 AI, StAtIstics and Data Science in Practice 

The NISS AI, Statistics and Data Science in Practice is a monthly event series will bring together leading experts from industry and academia to discuss the latest advances and practical applications in AI, data science, and statistics. Each session will feature a keynote presentation on cutting-edge topics, where attendees can engage with speakers on the challenges and opportunities in applying these technologies in real-world scenarios. This series is intended for professionals, researchers, and students interested in the intersection of AI, data science, and statistics, offering insights into how these fields are shaping various industries. The series is designed to provide participants with exposure to and understanding of how modern data analytic methods are being applied in real-world scenarios across various industries, offering both theoretical insights, practical examples, and discussion of issues.

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  • NISS Hosted

Website

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Location

United States

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