Dr. Kristina Pestaria Sinaga is an independent researcher recognized for influential contributions to machine learning and computational intelligence, particularly in clustering and multi-view learning. Her research interests span multi-view learning, federated learning, unsupervised and fuzzy clustering, pattern recognition, and Edge AI, with strong emphasis on scalable, privacy-preserving, and feature-efficient algorithms. Her research skills include advanced algorithm design, K-means and fuzzy C-means variants, feature reduction, entropy-based learning, federated analytics, and real-world data modeling across marketing, telecommunications, and socio-economic domains. She has received notable academic recognition through high-impact publications in leading journals such as IEEE Access, Pattern Recognition, and IEEE TPAMI, reflecting sustained scholarly excellence. Dr. Sinaga’s work demonstrates global collaboration and practical relevance, shaping modern unsupervised learning paradigms. According to Scopus-linked metrics, she has over 3,174 citations, 20 research documents, an h-index of 7, and an i10-index of 6, underscoring her growing international research impact and consistency in high-quality scientific output.
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