Qiang Liu

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Qiang Liu
Associate Professor
Computer Science
University of Texas at Austin
Office: GDC 4.806

("Qiang" sounds like "Chee-ah-ng", and "Liu" as "l-yo")


I am broadly interested in mathematical and computational techniques for learning, inference and making decision out of data and knowledge.

  • Algorithmic foundations: I am interested in developing fundamental yet computationally feasible algorithms for the basic learning, inference, and optimization problems that underpin the cutting-edge AI/ML/statistical technologies. I see myself as a mathematically-minded methodologist who uses advanced math to discover the missing algorithms that are otherwise non-obvious. I prefer simple and elegant methods influenced by Paul Erdos’ “The Book”.

  • Applications: I hope the algorithms we develop can help solve real-world problems of societal importance. One aspect of this is to tailor the basic algorithms to have desirable practical properties, such as computational/energy efficiency, robustness and fairness. Another aspect is to develop problem-specific solutions to address the unsolved problems in science and engineering.

<My Google scholar>

UT Statistical Learning & AI Group (to be updated)

Selected Works

  • Steepest Descent Methods for Neural Architecture Optimization: Going Beyond Black Boxes

  • Distributed Learning, Information Loss and Curved Exponential Families [paper, slides]

  • Variational Inference for Crowdsourcing [paper, slides]


  • Undergraduate intro to machine learning [here].

  • Undergraduate intro to optimization [here]

  • Lecture notes on probabilistic learning and inference [here]

  • Learning theory (graduate level, scribed notes, not proofreaded!) [here]

  • Advanced ML for undergraduates (scribed notes, not proofreaded!) [here]