Yuanming Shi
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RESEARCH

“We have not succeeded in answering all our problems. The answers we have found only serve to raise a whole set of new questions. In some ways we feel we are as confused as ever, but we believe we are confused on a higher level and about more important things.” — posted outside the mathematics reading room, Tromsø University

My research focuses on optimization, statistics, learning for data science, network science, and information science, including:

  • High-dimensional data analysis

  • Mobile AI and intelligent IoT

  • Deep and machine learning

  • Mathematical optimization

  • High-dimensional statistics

Resources

  1. Optimization

    1. Convex Optimization, by S. Boyd and L. Vandenberghe, Cambridge University Press, 2003. [EE364a][EE364b]

    2. Numerical Optimization, by J. Nocedal and S. Wright, Springer-Verlag, 2006.

    3. Nonlinear Programming, by D. Bertsekas, Athena Scientific. 2016.

  2. Statistics

    1. High-Dimensional Probability: An Introduction with Applications in Data Science, by Roman Vershynin, Cambridge University Press, 2018.

    2. High-Dimensional Statistics: A Non-Asymptotic Viewpoint, by Martin Wainwright, 2017.

    3. Topics in Random Matrix Theory, by Terence Tao, American Mathematical Society, 2012.

    4. Asymptotic Statistics, by A. W. van der Vaart, Cambridge University Press, 2012.

  3. Learning

    1. Pattern Recognition and Machine Learning, by C. M. Bishop, Springer, 2007. [CS229]

    2. Deep Learning, by I. Goodfellow, Y. Bengio and A. Courville, MIT Press, 2016. [CS231n]

    3. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by T. Hastie, R. Tibshirani, and J. Friedman, Springer, 2009.

  4. Information

    1. Fundamentals of Wireless Communication, by David Tse and Pramod Viswanath, Cambridge University Press, 2005.

    2. Elements of Information Theory, by Thomas M. Cover and Joy A. Thomas, Wiley, 2006.

Principal Collaborators