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Tang, Gongguo

Associate Professor

Positions

Research Areas research areas

Research

research overview

  • My group and I work in the areas of machine learning, optimization, data science, signal processing, and computational imaging. We are particularly interested in the design of learning models and optimization formulations that come with theoretical guarantees and are scalable to large datasets. A common theme of my work is leveraging prior structures and domain knowledge in a computationally effective way; these structures could be sparsity, manifold, smoothness, dynamics, graphs, and so on. One of the most interesting parts of this work is to explore the trade-offs between computational time, statistical performance, and sampling complexity.

keywords

  • Machine Learning, Optimization, Data Science, Signal Processing, Computational Imaging

Publications

selected publications

Teaching

courses taught

  • ECEN 4322 - Data and Network Science
    Primary Instructor - Fall 2022
    The course covers the theory and design of algorithms that are used to model, analyze, and extract information from large scale datasets and networks. The course includes a project. Same as ECEN 5322.
  • ECEN 5002 - Special Topics
    Primary Instructor - Spring 2022
    Examines a special topic in Electrical, Computer and Energy Engineering. May be repeated for up to 9 total credit hours.
  • ECEN 5012 - Special Topics
    Primary Instructor - Fall 2021
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  • ECEN 5322 - Data and Network Science
    Primary Instructor - Fall 2022
    The course covers the theory and design of algorithms that are used to model, analyze, and extract information from large scale datasets and networks. The course includes a project. Same as ECEN 4322.
  • ECEN 5612 - Random Processes for Engineers
    Primary Instructor - Fall 2022
    Deals with random time-varying functions and is therefore useful in the broad range of applications where they occur. Topics include review of probability, convergence of random sequences, random vectors, minimum mean-square error estimation, basic concepts of random processes, Markov processes, Poisson processes, Gaussian processes, linear systems with random inputs, and Wiener filtering. Applications range from communications, communication networks, and signal processing to random vibration/stress analysis, mathematical finance, physics, etc.

Background

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