Skip to main content

# Ning, Xia

# Xia Ning

Professor, Biomedical Informatics
Professor, Computer Science and Engineering
310C Lincoln Tower 1800 Cannon Dr Columbus, OH 43210-1231
(614) 366-2298
Xia.Ning@osumc.edu
ning.104@osu.edu
personal website

## Biography

Ning received her Ph.D degree from the Computer Science & Engineering Department, University of Minnesota, Twin Cities, in 2012. From University of Minnesota, Twin Cities, she also received her M.S. degree in Computer Science, and M.S. degree in Statistics from the School of Statistics. She received her B.S. degree in Computer Science from the Chu Kechen Honors College (an elite college), Zhejiang University, China. She was a researcher at the Autonomic Management Department, NEC Labs America in 2012-2014, and a tenure-track Assistant Professor at the Computer and Information Science Department (a Purdue Ph.D. program), Indiana University – Purdue University Indianapolis (IUPUI) in 2014-2018. At IUPUI, Ning was also a member of the Center of Computational Biology and Bioinformatics (CCBB) and an affiliated research scientist with Regenstrief Institute. Ning joined The OSU as a tenure-track Assistant Professor in July, 2018.

## Expertise

The Ning Lab is conducting research on Artificial Intelligence (AI), Data Mining, Machine Learning and Big Data Analytics with applications in drug discovery, Medical Informatics and Health Informatics. The research in the Lab is highly data-driven, computation-oriented and primarily focused on methodology development. The Lab develops new and better methods/algorithms/models to solve emerging and critical problems in healthcare and medicine domains. The new methodologies being developed at the Lab are also sufficiently generalizable to be applied to the problems from other domains (e.g., e-Commerce, social networks, system monitoring and diagnosis) that share similar characteristics as the targeted problems in healthcare and medicine domains. The typical techniques that the Lab develops include deep learning, information retrieval, learning to rank, recommendation, classification, among others, and are applied to problems such as drug property prediction, compound prioritization, drug selection, pharmacovigilance data mining, electronic medical record analysis, etc.