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Overview Dr. Sebastian Raschka has published extensively in the fields of machine learning, artificial intelligence, and their applications in various domains. His recent work spans from 2020 to 2023, with publications in reputable journals focusing on advanced AI techniques and their practical implementations. Key Research Areas Machine Learning and Neural Networks Developed deep neural networks for rank-consistent ordinal regression Explored few-shot learning techniques Investigated machine learning trends in Python for data science and AI Computer Vision and Privacy Created semi-adversarial networks for multi-attribute face privacy (PrivacyNet) Studied visual framing in science conspiracy videos using machine learning Bioinformatics and Computational Biology Applied machine learning to bioactive ligand discovery and GPCR-ligand recognition Developed techniques for identifying flexibility signatures in GPCR inhibition Provided guidelines for deep learning applications in biology AI Applications Applied neural networks to age estimation problems Explored machine learning applications in various scientific domains Publication Highlights "Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities" (2023) in Pattern Analysis and Applications "Ten Quick Tips for Deep Learning in Biology" (2022) in PLOS Computational Biology "PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy" (2020) in IEEE Transactions on Image Processing "Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence" (2020) in Information Impact and Interdisciplinary Approach Dr. Raschka's work demonstrates a strong interdisciplinary approach, bridging computer science with biology, medicine, and social sciences. His research has been published in high-impact journals, showing its significance in advancing the field of AI and its applications.