Welcome!

I am Dr. Yanjun Qi. Please feel free to call me "Jane", "Yanjun", "Professor Qi" or "Dr. Qi".
My research focuses on two major research fronts:

  • 1. Trustworthy deep learning, especially trustworthy NLP (Summary);
  • 2. Deep learning generalization (Summary);

    • In the past, I have also worked on
  • Stochastic relational graph learning (aka link prediction);
  • Deep learning tools for natural language processing and bio-medicine.


My Short Bio

Year (until) Information
Present:

I am a Senior Manager (starting as Principal Applied Scientist first) at Bedrock Science, Amazon Web Services (AWS).

Present:

I am an associate professor of computer science without term (tenured) at the University of Virginia (on leave since 2022).

2021:

I was part of the Data and Technology Advancement (DATA) National Service Scholar Program: Data Scientists Advancing Biomedical Research in National Institute of Health, NIA.

2020 Spring:

I took a Sabbatical in University of California, Berkeley, Visiting Prof. Bin Yu's group.

2019 Aug:

I was an assistant professor at the Department of Computer Science , in University of Virginia. I have received the Young Investigator CAREER award from NSF (National Science Foundation, 2015-2020)!

2013 Aug:

I was a senior researcher @ Machine Learning Department, NEC Labs America.
2013 May:

I was a research staff member @ Machine Learning Department, NEC Labs America.
( I was very fortunate to have the chance to work closely with Jason Weston , Ronan Collobert , Leon Bottou, Vladimir Vapnik, Koray Kavukcuoglu and many great colleagues.)

2008:

I completed my PhD in May of 2008 at the School of Computer Science in Carnegie Mellon University working with Ziv Bar-Joseph and Judith Klein-Seetharaman.
(Dissertation Committee: Ziv Bar-Joseph, Judith Klein-Seetharaman, Christos Faloutsos, Jaime Carbonell, Baldo Oliva)

2003:

I received my M.S. in May of 2003 from the School of Computer Science in Carnegie Mellon University.

2001:

I received my B.S. with first-class high honors (3 out of 156 graduates) and M.E. (in the accelerated program) in June of 2001 from Computer Science Department, Tsinghua university, Beijing.

More:

I was a member of Tsinghua Univ. Chorus team and a member of Tsinghua Univ. Skating Team between 1999-2001.


My UVa Research Team Information

Year (until) Information
2013-2021:

[Group Research Blog] + [Group Research Talk Videos] + [Group GitHub] + [ Group Capstone GitHub] + [ Group Members] + [ Group News ] + [Group Journal Club: Algorithm] + [Group Journal Club: Undergraduate]


Past Teaching

Year Title and Information
2024 Spring

Generative AI - Risks and Benefits (Graduate Advanced level - Seminar)
(Instructor, Department of Computer Science, University of Virginia)

2022 Spring

Machine Learning Foundation, Deep Learning, and Good Uses (Undergraduate Advanced level)
(Instructor, Department of Computer Science, University of Virginia)

2020 Fall

Machine Learning Foundation, Deep Learning, and Good Use on COVID19 (Undergraduate Advanced level)
(Instructor, Department of Computer Science, University of Virginia)

2019 Fall

Machine Learning (Master level)
(Instructor, Department of Computer Science, University of Virginia)

2019 Spring

Deep Learning Advances Graphs (PhD Student level)
(Instructor, Department of Computer Science, University of Virginia)
(Course materials will be added into my Notes2LearnDeepLearning to help students learn state-of-the-art topics in deep learning.)

2018 Fall

Machine Learning (Undergraduate level)
(Instructor, Department of Computer Science, University of Virginia)

2018 Spring

Machine Learning (Undergraduate level)
(Instructor, Department of Computer Science, University of Virginia)

2017 Fall

Advanced Deep Learning (PhD Student level)
(Instructor, Department of Computer Science, University of Virginia)
(Now we expand the course materials into my Notes2LearnDeepLearning to help students learn state-of-the-art topics in deep learning.)
(This list was started from the ~90 papers I chose from NIPS16+ICLR17+ICML17)

2016 Fall

Machine Learning (Master+ AdvancedSenior level)
(Instructor, Department of Computer Science, University of Virginia)

2015 Fall

Machine Learning (Master level)
(Instructor, Department of Computer Science, University of Virginia)

2015 Spring

Special Topic: Large-Scale Machine Learning (PhD Student level)
(Instructor, Department of Computer Science, University of Virginia)
(Later I converted the materials into my Notes2LearnLearning): a list of tutorials to help students learn advanced topics in machine learning.)

2014 Fall

Introduction to Machine Learning and Data Mining (Undergraduate+Master level)
(Instructor, Department of Computer Science, University of Virginia)

2014 Spring

Special Topic: Machine Learning and Data Mining in Practice for Biomedicine (PhD Student level)
(This course covers 6 different topics about connecting machine learning and "big data" in biomedicine".)
(Instructor, Department of Computer Science, University of Virginia)

2013 Fall

Special Topic: Machine Learning (PhD Student level)
(This course covered the book: The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman).
(Instructor, Department of Computer Science, University of Virginia)

2006 fall

Machine Learning (Undergraduate+Master level)
(Teaching Assistant, School of Computer Science, Carnegie Mellon University)

2004 fall

Machine Learning (PhD Student level)
(Teaching Assistant, School of Computer Science, Carnegie Mellon University)

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"Success is not final, failure is not fatal: it is the courage to continue that counts." --- Winston Churchill

“Life is the art of drawing without an eraser.” --- John W. Gardner

"We are what we repeatedly do. Excellence, then, is not an act, but a habit." --- Aristotle

"I can accept failure, everyone fails at something. But I can't accept not trying." --- Michael Jordan