Xia Ning selected for 10-Years-Highest-Impact Award

The International Conference on Data Mining has recently awarded their annually selected 10-Years-Highest-Impact Award to a paper titled SLIM: Sparse Linear Methods for Top-N Recommender Systems. The paper, written by TDAI Core Faculty Xia Ning, as well as George Karypis, was published in 2011, and was selected for the impact award via awards committee discussion in 2020. The prestigious honor, granted by the conference award committee,  is given to papers and works at the conference that have the most substantial impact on the data mining community.

Xia Ning is an associate professor at Ohio State in both Biomedical Informatics and Computer Science and Engineering. Her work focusses on data mining, machine learning and big data analytics with applications for emerging critical problems in drug discovery, medical informatics, health informatics and e-commerce. Ning develops efficient data mining and machine learning methodologies to facilitate rapid and targeted exploration over chemical and biological spaces, and effective computational algorithms (e.g., recommendation, information retrieval) to analyze medical and healthcare data (e.g., electronic medical records, pharmacovigilance data).

ICDM acts as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. ICDM draws researchers, application developers, and practitioners from a wide range of data mining related areas such as big data, deep learning, pattern recognition, statistical and machine learning, databases, data warehousing, data visualization, knowledge-based systems, and high-performance computing to submit and display their work. By promoting novel, high-quality research findings and solutions to challenging data mining problems, the conference advances the state-of-the-art in a variety of data mining fields.

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