Active Learning to Minimize the Possible Risk of Future Epidemics

Prof. KC Santosh, a highly accomplished AI expert, is the chair of the Department of Computer Science, University of South Dakota. He served the National Institutes of Health as a research fellow. Before that, he worked as a postdoctoral research scientist at the LORIA research centre, Universitè de Lorraine in direct collaboration with industrial partner, ITESOFT, France. He earned his PhD in Computer Science - Artificial Intelligence from INRIA Nancy Grand East Research Centre (France). With funding of over $1.3 million, including a $1 million grant from DEPSCOR (2023) for AI/ML capacity building at USD, he has authored 10 books and published over 240 peer-reviewed research articles. He is an associate editor of multiple prestigious journals such as IEEE Transactions on AI, Int. J of Machine Learning & Cybernetics, and Int. J of Pattern Recognition & Artificial Intelligence. To name a few, Prof. Santosh is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD, 2021), the President's Research Excellence Award (USD, 2019) and the Ignite Award from the U.S. Department of Health & Human Services (HHS, 2014). As the founder of AI programs at USD, he has taken significant strides to increase enrolment in the graduate program, resulting in over 3,000% growth in just three years. His leadership has helped build multiple inter-disciplinary AI/Data Science related academic programs, including collaborations with Biology, Physics, Biomedical Engineering, Sustainability and Business Analytics departments. Prof. Santosh is highly motivated in academic leadership, and his contributions have established USD as a pioneer in AI programs within the state of SD. More info. https://kc-santosh.org/.   Mr. Suprim Nakarmi is a research fellow for the Applied AI research lab, Department of Computer Science at the University of South Dakota. His research focuses on active learning, specifically addressing the pressing global issue of future epidemics.

Introduction.- Active learning – what, when, and where to deploy?.- Active learning – review (cases).- Active learning – methodology.- Active learning – validation.- Case study: Is my cough sound Covid-19?.