About the event
Dr. Prashanta Dutta, Professor, School of Mechanical and Materials Engineering, WSU
Prof. Prashanta Dutta received his MS (1997) and Ph.D. (2001) degrees from the University of South Carolina and Texas A&M University, respectively. He joined the School of Mechanical and Materials Engineering of Washington State University (WSU) in 2001, where he has been a tenured Full Professor since 2013. During his sabbatical years, he worked as a Visiting Professor at Konkuk University, Seoul, South Korea and the Technical University of Darmstadt, Germany. His primary research area is Micro, Nano, and Biofluidics with a specific focus on the development of new algorithms for multiscale and multiphysics problems. Lately, he developed a suite of physics-based machine learning models for heat and mass transfer problems. He has published more than 200 peer-reviewed journal and conference articles. Prof. Dutta organized and chaired numerous sessions, fora, symposia, and tracks for several ASME (American Society of Mechanical Engineers) and APS (American Physical Society) conferences and served as the Chair of the ASME Micro/Nano Fluid Dynamics Technical Committee. Moreover, he served as an Associate Editor for the ASME Journal of Fluids Engineering; currently, he is an Editor for Electrophoresis. Prof. Dutta is an elected Fellow of ASME, and he is a recipient of the prestigious Fulbright Professorship sponsored by the US Department of State. He is currently leading an NSF-sponsored research traineeship (NRT) program on robotics and autonomous systems.
Drug delivery to the brain is a major challenge for the treatment of neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease due to the presence of the blood-brain barrier (BBB), a highly selective barricade formed by microvascular endothelial cells. In recent years, nanoparticles have received a significant amount of interest for targeted drug delivery across the BBB. Experimental studies have revealed that selective nanoparticles can transport drug molecules from microvascular blood vessels to brain parenchyma in an efficient and non-invasive way. However, current methods for finding optimum nanoparticle-based drug carriers are through experimental trial and error, which are not only time-consuming but also expensive. In this talk, the role of machine learning will be demonstrated in drug design and delivery. In particular, data-based machine learning algorithms will be discussed to identify appropriate kinetic rate parameters for nanoparticle transport through BBB endothelial cells. Furthermore, I will present our latest findings on transfer learning-based deep convolutional neural networks to classify the empty, single-stranded DNA, and double-stranded DNA based adeno-associated viruses (AAVs) with very high accuracy (over 95%), with experimental data obtained from solid-state nanopore experiments, demonstrating the capability of machine learning algorithms in robust detection of target virus from a complex mixture.