Machine Learning and Artificial Intelligence in Chemical and Biological Sensing

Jeong-Yeol Yoon received his Ph.D. in biomedical engineering from the University of California, Los Angeles (UCLA), USA in 2004. He joined the faculty at the University of Arizona, USA in the same year. Dr Yoon has worked on smartphone-based biosensors. He was the President of the Institute of Biological Engineering in 2015 and is currently the Editor-in-Chief of the Journal of Biological Engineering (Springer Nature), Associate Editor of Biosensors and Bioelectronics (Elsevier), and Editorial Board Member for several other journals. Chenxu Yu received his Ph.D. in biological systems engineering from University of Wisconsin-Madison, USA in 2003. He then did post-doctoral training at Pennsylvania State University and Purdue University, USA before joined the faculty at Iowa State University, USA in 2007. He has been serving as an editorial board member of seven reputed journals. His research interests are in the areas of bionanotechnology and biosensors, food and bioprocess engineering, spectroscopic imaging and biodiagnostics, environmental monitoring and engineering.

1. Fundamentals of Chemical Sensors and Biosensors
2. Fundamentals of Machine Learning (ML) and Artificial Intelligence (AI)
3. Use of ML/AI in Chemical Sensors and Biosensors
4. ML-Assisted E-nose and Gas Sensors
5. ML-Assisted FTIR and Raman Spectroscopic Sensing in Agricultural and Food Systems
6. ML-Assisted Surface-Enhanced Raman Spectroscopic Characterization of Biological Systems
7. AI-Assisted Microscopic Imaging Analysis for High Throughput Phenotyping
8. ML-Assisted Lens-Free Imaging
9. ML-Assisted Multispectral and Hyperspectral Imaging
10. AI/ML-Assisted NIR/Optical Biosensing for Plant Phenotyping
11. ML-Assisted Receptor-Free Biosensing
12. ML-Assisted Flow Velocity Analysis in Paper Microfluidics
13. AI/ML Tools for Single Molecule Data Analysis in Biomedicine
14. ML-Assisted Characterization of In Situ Protein Dynamics at Solid-Liquid Interfaces
15. AI-Assisted Microbial Population Dynamics Modeling
16. AI-Assisted All-in-One Sensor System