Bio

B.S. ​Tsinghua University, Beijing, China, 2000​M.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, Assistant Professor

Yanjun Qi is an assistant professor 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.

Awards

  • CAREER award from NSF 2015
  • Best Paper Award at International Conference of BodyNet 2014

Research Interests

  • Machine Learning

Selected Publications

  • "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.) ABS Ritambhara 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", ABS Beilun Wang, Ji Gao, Yanjun Qi, (2017)

Courses Taught

  • Machine Learning (Graduate+ AdvancedSenior level) 2016 Fall
  • Machine Learning (Graduate level) 2015 Fall
  • Special Topic: Large-Scale Machine Learning (PhD Student level) 2015 Spring

Featured Grants & Projects

  • NSF IIS-1453580:

    2015-2020


    CAREER: A Data-Driven Network Inference Framework for Context-Conditioned Protein Interaction Graphs

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  • NSF CNS-1619098:

    2016-2019


    TWC:Small: Automatic Techniques for Evaluating and Hardening Machine Learning Classifiers in the Presence of Adversaries

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