Yuanshun Yao

Yuanshun Yao


Department of Computer Science
University of California, Santa Barbara
Santa Barbara, CA 93106

Email: yao[at]cs.ucsb.edu (PGP key)

Curriculum vitae: [PDF]

I am a PhD student in the Department of Computer Science at the University of California, Santa Barbara. My research interests are machine learning (recently deep learning), data mining and their applications to security and privacy, user behavior modeling and mobile computing. I work in the SAND lab, co-advised by Prof. Ben Y. Zhao and Prof. Heather Zheng. Prior to joining UCSB, I received three Bachelors of Science in Computer Science, Statistics and Mathematics from the University of Minnesota - Twin Cities.

I have also spent one summer as software engineer intern at IBM Princeton lab.


Object Recognition and Navigation using a Single Networking Device
Yanzi Zhu, Yuanshun Yao, Ben Y. Zhao and Haitao Zheng.
Proceedings of 15th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys). Niagara Falls, NY, June 2017.

Abstract: Tomorrow’s autonomous mobile devices need accurate, robust and real-time sensing of their operating environment. Today’s solutions fall short. Vision or acoustic-based … more »

Identifying Value in Crowdsourced Wireless Signal Measurements
Zhijing Li, Ana Nika, Xinyi Zhang, Yanzi Zhu, Yuanshun Yao, Ben Y. Zhao and Haitao Zheng.
Proceedings of 26th World Wide Web Conference (WWW). Perth, Australia, April 2017.

Abstract: While crowdsourcing is an attractive approach to collect large-scale wireless measurements, understanding the quality and variance of the resulting data is difficult. … more »

A general framework to increase the robustness of model-based change point detection algorithms to outliers and noise
Xi C. Chen, Yuanshun Yao, Sichao Shi, Snigdhansu Chatterjee, Vipin Kumar and James H. Faghmous.
Proceedings of SIAM International Conference on Data Mining (SDM). Miami, FL, May 2016.
[PDF] [Code+Dataset] [Technical Report]

Abstract: The autonomous identification of time-steps where the behavior of a time-series significantly deviates from a predefined model, or time-series change point… more »

A daily global mesoscale ocean eddy dataset from satellite altimetry
James H. Faghmous, Ivy Frenger, Yuanshun Yao, Robert Warmka, Aron Lindell and Vipin Kumar.
Scientific Data, Nature Publishing Group, 2. 2015.
[PDF] [Code] [Dataset]

Abstract: We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days… more »


CS 16 Problem Solving with Computers , Winter 2016, University of California, Santa Barbara.

Fundamental building blocks for solving problems using computers. Topics include basic computer organization and programming constructs: memory CPU, binary arithmetic, variables, expressions, statements, conditionals, iteration, functions, parameters, recursion, primitive and composite data types, and basic operating system and debugging tools.

CS 8 Introduction to Computer Science , Fall 2015, University of California, Santa Barbara.

Introduction to computer program development for students with little to no programming experience. Basic programming concepts, variables and expressions, data and control structures, algorithms, debugging, program design, and documentation.

CSCI2033 Elementary Computational Linear Algebra , Spring 2015, Univerity of Minneosta.

This course offers a general introduction to linear algebra and the use of matrix methods to solve a variety of computer science problems. Topics will include fundamental concepts (vectors, matrices, orthogonality, rank, eigenvalues, ...) and standard algorithms for solving common problems (systems of linear equations, eigenvalue problems, least squares systems, ...). Some assignments will require programming in Matlab.

Math5651 Basic Theory of Probability and Statistics , Spring 2013, Univerity of Minneosta.

This is a course in the elements of probability and statistics, including topics such as probability spaces, random variables, their distributions, expected values, variances, law of large numbers, moments, moment generating functions, joint distributions, conditional and marginal distributions, Bayes theorem. We will also discuss the the normal distribution ("bell-shaped curve") and central limit theorem, as well as other standard distributions, such as the Bernoulli, binomial, hypergeometric, Poisson, gamma, exponential, beta distributions.


Department of Computer Science

University of California, Santa Barbara

Santa Barbara, CA 93106

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