Daniel McKenzie

Applied Math and ML.

Positions

2019-present University of California, Los Angeles. Assistant Adjunct Professor

Research interests

Machine Learning, Signal Processing and Optimization. More specifically; zeroth-order optimization and applications, implicit depth neural networks, constrained optimization with non-smooth constraint sets and data-driven metrics for unsupervised learning.

Education

2013-2019 University of Georgia, USA. PhD. Advisor: Ming-Jun Lai.

03/2013-06/2013 University of Bayreuth, Germany. Short term visitor.

2011-2013 University of Cape Town, South Africa. M.Sc (with distinction).

2007-2010 University of Cape Town, South Africa. B.Sc (with distinction)

Awards

2021 Liggett Instructor Award.

2019 William Armor Wills Memorial Scholarship.

2017 University Outstanding Teaching Assistant Award.

2014-2016 NRF Doctoral Scholarship for Study Abroad.

2013 DAAD Short Term Research Exchange (to University of Bayreuth).

2012 DAAD-NRF Joint Masters Bursary.

2010 UCT Council Merit Scholarship.

2010 NRF Honours Bursary.

2009 Jakob Burlak Memorial Trust Scholarship.

2007,2008,2009 UCT Science Faculty Scholarship.

Publications

A list is also available online. *= undergraduate coauthor.

Journals

2021 Balancing geometry and density: Path distances on high-dimensional data. SIMODS (to appear.) With Anna Little and James M. Murphy

2020 Compressive sensing for cut improvement and local clustering. SIMODS. With Ming-Jun Lai.

2019 Power weighted shortest paths for clustering Euclidean data. Foundations of Data Science. With Steve Damelin

Conferences

2021 A zeroth-order, block coordinate descent algorithm for huge-scale black-box optimization. ICML. With HanQin Cai, Yuchen Lou* and Wotao Yin.

2020 Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction. IEEE Big Data GTA3 Workshop. With Mariam Alaverdian*, William Gilroy*, Veronica Kirgios*, Xia Li, Carolina Matuk*, Tachin Ruangkriengsin*, P. Jeffrey Brantingham and Andrea Bertozzi.

Submitted

2021 From the simplex to the sphere: Faster constrained optimization using the Hadamard parametrization With Qiuwei Li and Wotao Yin.

2021Curvature-Aware Derivative-Free Optimization With Bumsu Kim, HanQin Cai and Wotao Yin.

2021 Learn to predict equilibria via Fixed Point Networks. With Howard Heaton, Qiuwei Li, Samy Wu Fung, Stanley Osher and Wotao Yin.

2021 Fixed Point Networks: Implicit depth models with Jacobian-free backprop. With Samy Wu Fung, Howard Heaton, Qiuwei Li, Stanley Osher and Wotao Yin.

2020 Zeroth-order regularized optimization (ZORO): Approximately sparse gradients and adaptive sampling. With HanQin Cai, Wotao Yin and Zhenliang Zhang.

2020 A one-bit, comparison-based gradient estimator. With HanQin Cai, Wotao Yin and Zhenliang Zhang.

Talks

All talks invited unless otherwise stated.

10/2021 INFORMS2021: Recent Advances in Derivative-free Optimization. Anaheim, CA.

08/2021 Mathematics of Machine Learning. Online. (contributed)

07/2021 SIAM OP21: Optimization, Data Science and their Applications. Online.

06/2021 Optimal Transport and Mean Field Games Seminar. Online.

11/2020 INFORMS2020: Session on Recent Progress in Blackbox Optimization. Online.

04/2020 Tufts Math of Data Science Lecture Series. Online.

02/2020 UGA Applied Math Seminar. Athens, GA.

09/2019 SIAM South-Eastern Sectional. Knoxville, TN.

05/2018 International Conference on Computational Harmonic Analysis (ICCHA7). Nashville, TN. (contributed)

10/2018 AMS Central Sectional. Ann Arbor, MI.

Teaching

2021 Math 151BH: Honors Applied Numerical Methods II, UCLA. (I also co-developed this course).

2021 Math 151AH: Honors Applied Numerical Methods I, UCLA. (I also co-developed this course).

2020-present Math 118: Mathematical Methods of Data Theory, UCLA. (taught three times. I also co-developed this course).

2020 Math 170S: Statistics, UCLA.

2019 Math 151A: Applied Numerical Methods I, UCLA.

2019 Math 32A: Calculus III, UCLA.

2015-2019 Math2250: Calculus I, UGA. (taught three times).

2014-2018 Math1113: Precalculus, UGA. (taught six times).

Service

Organization

2021 Exploiting structure in zeroth-order optimization. Workshop at INFORMS2021.

2020 ZOOM: Zeroth Order Online Meeting. (Online) mini-conference.

Undergrad. Students Mentored

2021 Yuchen Lou (Hong Kong Univ. –> Northwestern Univ.), Isha Slavin (UCLA), Allen Zou (UCSD).

2020 Mariam Alaverdian (Los Angeles Community College –> Yale), William Gilroy (Harvey Mudd), Veronica Kirgios (Notre Dame), Carolina Matuk (Univ. Iowa), Tachin Ruangkriengsin (UCLA), Charles Stoksik (UCLA), Chenglin Yang (UCLA –> Columbia), Allen Zou (UCSD).

2018 Lucas Connell (UGA).

Graduate Students Mentored

2019-present Howard Heaton, Bumsu Kim.

Outreach

2020 CSST Mentor. The resulting paper was presented at ICML.

2020 UCLA REU Mentor. The resulting paper was presented at IEEE Big Data 2020

2018 UGA MathCamp. I mentored a group of five high school students on the “Monster Epidemiology” project.

2011-2012 SHAWCO. I tutored students, trained volunteers and co-led the KenSMART project.

Skills

Proficient in Python (specifically: NumPy, SciKitLearn and PyTorch), Matlab, LaTeX and Markdown.