B.S. Tsinghua University, Beijing, China, 2000M.S. Carnegie Mellon University, Pittsburgh, USA, 2003Ph.D. Carnegie Mellon University, Pittsburgh, USA, 2008
"We aim to develop novel machine-learning techniques on important challenges in biomedicine, especially those dealing with enormous data sets."
Yanjun Qi, Associate Professor
Yanjun Qi is an Associate professor (tenured) of University of Virginia, Department of Computer Science since 2013. She was a senior researcher in the Machine Learning Department at NEC Labs American, Princeton, NJ from July 2008 to August 2013. Her research interests are within machine learning, data mining, and bioinformatics. She obtained her Ph.D. degree from School of Computer Science at Carnegie Mellon University in May 2008 and received her Bachelor degree with high honors from Computer Science Department at Tsinghua University, Beijing. She has served as PCs and reviewers for multiple reknowned international conferences/ journals, and has co-chaired the NIPS “Machine Learning for Computational Biology” workshop. Dr. Qi has received CAREER award from NSF and a Best Paper Award at International Conference of BodyNet.
CAREER award from NSF2015
Best Paper Award at International Conference of BodyNet2014
"A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models", Proceedings of The 20th International Conference on Artificial Intelligence and Statistics (AISTATS) Beilun Wang, Ji Gao, Yanjun Qi, (2017)
"DeepChrome: Deep-learning for predicting gene expression from histone modifications", 15th European Conference on Computational Biology , (ECCB-16 ) (Bioinformatics(2016) 32 (17): i639-i648.) ABSRitambhara Singh, Jack Lanchantin, Yanjun Qi, (2016)
"Deep Motif: Visualizing Genomic Sequence Classifications", the International Conference on Learning Representations Jack Lanchantin, Ritambhara Singh, Zeming Lin, Yanjun Qi, (2016)
"A Theoretical Framework for Robustness of (Deep) Classifiers Against Adversarial Samples", ABSBeilun Wang, Ji Gao, Yanjun Qi, (2017)