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Howard Heaton

PhD Candidate

Optimization/Deep Learning

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Welcome. I am a graduate student nearing completion of my PhD thesis in the UCLA Math Department, researching optimization algorithms for big data and deep learning under the joint advisement of Wotao Yin and Stanley Osher. My undergraduate studies were completed at Walla Walla University. Info about my research can be found via links in the menu.

Research Interests

My passion is using math to solve tech problems in meaningful applications for society. 

The focus of my research is the construction of  scalable optimization algorithms for problems with big data (e.g., image processing) that maintain robust theoretical guarantees. In other words, my research designs deep learning schemes that are stable, interpretable, memory efficient, and produce outputs that satisfy input constraints. My metaphorical hammer of choice consists of operator-based fixed point schemes.



H. Heaton,* D. McKenzie,* Q. Li, S. Wu Fung,  S. Osher, W. Yin. Learn to Predict Equilibria in Fixed Point Networks.  arXiv preprint: 2106.00906, 2021.

(Submitted to NeuRIPs 2021)

H. Heaton, S. Wu Fung, A. Gibali, W. Yin. Feasiblity-based Fixed Point Networks. 

arXiv preprint: 2104.12939, 2021

(Submitted to Springer's Fixed Point Theory and Algorithms

for Sciences and Engineering)

S. Wu Fung,* H. Heaton,* Q. Li, D. McKenzie, S. Osher, W. Yin. Fixed Point Networks. 

arXiv preprint: 2103.12803, 2021.

(Submitted to NeuRIPs 2021)

T. Chen, X. Chen, W. Chen, H. Heaton, J. Liu, Z. Wang, W. Yin, Learning to Optimize: A Primer and A Benchmark.

arXiv preprint: 2103.12828, 2021.

(Submitted to Journal of Machine Learning Research)

J.Shen,* X. Chen,* H. Heaton,* T. Chen, J. Liu, W. Yin, Z. Wang, Learning A Minimax Optimizer: A Pilot Study

ICLR, 2021.


Department of Mathematics
University of California Los Angeles

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