Deep Learning and Parallel Computing Environment for Bioengineering Systems

Prof. Arun Kumar Sangaiah received his PhD from the School of Computer Science and Engineering, VIT University, Vellore, India. He is currently a Full Professor with National Yunlin University of Science and Technology, Taiwan. He is also a Professor at the School of Computing Science and Engineering, VIT University, Vellore, India. His areas of research interest include machine learning, Internet of Things, Sustainable Computing. He has published more than 300 research articles in refereed journals, 11 edited books, one patent (held and filed), as well as four projects funded by MOST-TAIWAN, one funded by Ministry of IT of India, and several international projects (CAS, Guangdong Research fund, Australian Research Council). Dr. Sangaiah has received many awards, Yushan Young Scholar, Clarivate Top 1% Highly Cited Researcher (2021,2022, 2023), Top 2% Scientist (Standord Report-2020,2021,2022, 2023), PIFI-CAS fellowship, Top-10 outstanding researcher, CSI significant Contributor etc. He is also serving as Editor-in-Chief and/or Associate Editor of various reputed ISI journals. Dr. Sangaiah is a visiting scientist (2018-2019) with Chinese Academy of Sciences (CAS), China and visiting researcher of Université Paris-Est (UPEC), France (2019-2020) and etc.

• 1. Introductory2. Theoretical results on representation of deep learning and parallel architectures for bioengineering3. Parallel Machine Learning and Deep Learning approaches for Bio-informatics4. Parallel programming, architectures and machine intelligence for bioengineering5. Deep Randomized Neural Networks for Bioengineering applications6. Artificial Intelligence enhance parallel computing environments7. Parallel computing, graphics processing units (GPU) and new hardware for deep learning in Computational Intelligence research8. Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities9. Novel distance metric learning algorithms for biometrics modalities10. Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems11. Deep learning and semi-supervised and transfer learning algorithms for medical imaging12. Biological plausibility/inspiration of Randomized Neural Networks13. Genomic data visualisation and representation for medical information14. Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks15. Inference and optimization with bioengineering problems