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Anindya Sundar Das is a Doctoral student at Umeå University, specializing in the field of Cyber Analytics and focusing on anomaly detection in various contexts. He has contributed to several significant publications, particularly addressing adaptive deviation learning and its applications in visual anomaly detection and few-shot anomaly detection within textual data. His research encapsulates the intersection of computer vision and machine learning, with a particular emphasis on how data contamination can influence detection processes. Das is a member of the Cyber Analytics Learning Group, which collaborates on innovative solutions and strategies for modern computing challenges. His work is recognized within the academic community for its potential implications in real-world applications of cloud computing and data analysis. He is actively involved in presenting his research findings at international conferences, showcasing his commitment to advancing knowledge in his area of expertise.
Umeå University • Umeå, Sweden
Conducting research in anomaly detection and machine learning, contributing to the Cyber Analytics Learning Group.
Requirements are standard for Master's programs across Social Sciences and Humanities at Umeå. English 6 proficiency is the general rule.