David Hong

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Postdoctoral Scholar
Wharton Statistics
University of Pennsylvania

dahong67@wharton.upenn.edu
431-1 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA 19104

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About

I am currently a postdoctoral scholar in the Department of Statistics at the Wharton School of the University of Pennsylvania. My mentor is Edgar Dobriban. Previously, I was an NSF Graduate Research Fellow in the Department of Electrical Engineering and Computer Science at the University of Michigan, where I completed my PhD ("Learning Low-Dimensional Models for Heterogeneous Data") under the supervision of Jeff Fessler and Laura Balzano. I also spent a summer as a Data Science Graduate Intern at Sandia National Labs, where I worked with Tammy Kolda and Cliff Anderson-Bergman.

My current research interests include:

Education

Preprints

(* = equal contributors)

  1. Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
    David Hong, Jeffrey A. Fessler, Laura Balzano.
    In preparation.
    arXiv code
  2. Provable tradeoffs in adversarially robust classification
    Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey.
    In preparation.
    arXiv
  3. HYPER: Group testing via hypergraph factorization applied to COVID-19
    David Hong, Rounak Dey, Xihong Lin, Brian Cleary, Edgar Dobriban.
    Under review.
    code medRxiv talk web app
  4. HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
    David Hong, Kyle Gilman, Laura Balzano, Jeffrey A. Fessler.
    Under review.
    arXiv code software
  5. Selecting the number of components in PCA via random signflips
    David Hong*, Yue Sheng*, Edgar Dobriban.
    Under review.
    arXiv code
  6. Generic Properties of Koopman Eigenfunctions for Stable Fixed Points and Periodic Orbits
    Matthew D. Kvalheim, David Hong, Shai Revzen.
    Accepted to 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020).
    arXiv

Journal Publications

(* = equal contributors)

  1. Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings
    Brian Cleary*, James A. Hay*, Brendan Blumenstiel, Maegan Harden, Michelle Cipicchio, Jon Bezney, Brooke Simonton, David Hong, Madikay Senghore, Abdul K. Sesay, Stacey Gabriel, Aviv Regev, Michael J. Mina.
    Science Translational Medicine, 2021.
    doi medRxiv
  2. Subspace clustering using ensembles of K-subspaces
    John Lipor*, David Hong*, Yan Shuo Tan, Laura Balzano.
    Information and Inference, 2021.
    arXiv doi
  3. Baseline estimation of commercial building HVAC fan power using tensor completion
    Shunbo Lei, David Hong, Johanna L. Mathieu, Ian A. Hiskens.
    Electric Power Systems Research, 2020.
    arXiv doi
  4. Stochastic Gradients for Large-Scale Tensor Decomposition
    Tamara G. Kolda, David Hong.
    SIAM Journal on Mathematics of Data Science, 2020.
    arXiv doi
  5. Generalized Canonical Polyadic Tensor Decomposition
    David Hong, Tamara G. Kolda, Jed A. Duersch.
    SIAM Review, 2020.
    arXiv doi
  6. Convolutional Analysis Operator Learning: Dependence on Training Data
    Il Yong Chun*, David Hong*, Ben Adcock, Jeffrey A. Fessler.
    IEEE Signal Processing Letters, 2019.
    arXiv doi
  7. Asymptotic performance of PCA for high-dimensional heteroscedastic data
    David Hong, Laura Balzano, Jeffrey A. Fessler.
    Journal of Multivariate Analysis, 2018.
    arXiv doi
  8. Closed-Form Expressions for Minimizing Total Harmonic Distortion in Three-Phase Multilevel Converters
    David Hong, Sanzhong Bai, Srdjan M. Lukic.
    IEEE Transactions on Power Electronics, 2013.
    doi

Conference Proceedings

  1. Probabilistic PCA for Heteroscedastic Data
    David Hong, Laura Balzano, Jeffrey A. Fessler.
    2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019.
    doi
  2. Incorporating Handcrafted Filters in Convolutional Analysis Operator Learning for Ill-Posed Inverse Problems
    Caroline Crockett, David Hong, Il Yong Chun, Jeffrey A. Fessler.
    2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019.
    doi software
  3. Exploration of tensor decomposition applied to commercial building baseline estimation
    David Hong, Shunbo Lei, Johanna L. Mathieu, Laura Balzano.
    2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019.
    doi
  4. Learning Dictionary-Based Unions of Subspaces for Image Denoising
    David Hong, Robert P. Malinas, Jeffrey A. Fessler, Laura Balzano.
    2018 26th European Signal Processing Conference (EUSIPCO), 2018.
    doi
  5. Online Estimation of Coherent Subspaces with Adaptive Sampling
    Greg Ongie, David Hong, Dejiao Zhang, Laura Balzano.
    2018 IEEE Statistical Signal Processing Workshop (SSP), 2018.
    doi
  6. Enhanced online subspace estimation via adaptive sensing
    Greg Ongie, David Hong, Dejiao Zhang, Laura Balzano.
    2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017.
    doi
  7. Towards a theoretical analysis of PCA for heteroscedastic data
    David Hong, Laura Balzano, Jeffrey A. Fessler.
    2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016.
    arXiv doi
  8. Exploiting HF ambient noise to synchronize distributed receivers
    David Hong, Jeffrey L. Krolik.
    2013 US National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), 2013.
    doi