Machine Learning-guided Biosensing and High-Throughput Characterization of Soft Materials

While biosensors have been widely utilized in controlled measurement formats, such as laboratory settings, it remains a challenge to create robust and reliable biosensors for clinical and industrial applications, such as medical diagnostics and bioprocess monitoring. The limited ability to predict the false positive and false negative responses of biosensors remains a challenge in the biosensing field, particularly in practical high-throughput measurement formats.  This talk will discuss the use of supervised machine learning for improving various aspects of biosensor performance, including measurement confidence, speed, sensitivity, and throughput.  Application to the detection of multiple target analytes using cantilever sensors will be discussed, including biosensing in continuous-flow formats and chemical sensing in high-throughput measurement formats.

Blake Johnson

Virginia Tech on November 4, 2022 at 10:15 AM in EB2 1025

Blake Johnson received a B.S. degree in chemical engineering and certificate in chemistry from the University of Wisconsin-Madison (2008), Ph.D. in chemical engineering from Drexel University (2013), and postdoctoral training in the Department of Mechanical and Aerospace Engineering at Princeton University (2013-2015). His research as an associate professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech focuses on biosensing, biomanufacturing, and additive manufacturing. He is also an affiliated faculty member with the Department of Chemical Engineering, Department of Materials Science and Engineering, and School of Neuroscience. He is an active member of the Institute of Industrial and Systems Engineers (IISE), Society of Manufacturing Engineers (SME), the American Chemical Society (ACS), and American Institute of Chemical Engineers (AIChE). His research contributions to the fields of sensing and manufacturing have resulted in several awards, including a Best Dissertation Award from Drexel University (2013), a Young Investigator Award from the Institute of Industrial and Systems Engineering (2016), a Young Investigator Award from the Society of Manufacturing Engineers (2020), and a NSF Career Award (2022).

Electrical and Computer Engineering Colloquia

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