David Hong

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Assistant Professor
Dept. of Electrical and Computer Engineering
Resident Faculty at the Data Science Institute (DSI)
University of Delaware

hong@udel.edu
314 Evans Hall, Newark, DE 19716

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About

I am currently an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. Previously, I was an NSF Postdoctoral Research Fellow in the Department of Statistics and Data Science at the University of Pennsylvania. I completed my PhD in the Department of Electrical Engineering and Computer Science at the University of Michigan, where I was an NSF Graduate Research Fellow. I also spent a summer as a Data Science Graduate Intern at Sandia National Labs.

My current research interests include:

Education

Software

Preprints

(* = equal contributors)

  1. Annotation Vocabulary (Might Be) All You Need
    Logan Hallee, Niko Rafailidis, Colin Horger, David Hong, Jason P Gleghorn.
    In preparation.
    bioRxiv
  2. Selecting the number of components in PCA via random signflips
    David Hong, Yue Sheng, Edgar Dobriban.
    Under review.
    arXiv code software

Refereed Journal Papers

(* = equal contributors)

  1. Provable tradeoffs in adversarially robust classification
    Edgar Dobriban*, Hamed Hassani*, David Hong*, Alexander Robey*.
    IEEE Transactions on Information Theory, 2023.
    arXiv doi
  2. Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
    David Hong, Fan Yang, Jeffrey A. Fessler, Laura Balzano.
    SIAM Journal on Mathematics of Data Science, 2023.
    arXiv code doi software
  3. Group testing via hypergraph factorization applied to COVID-19
    David Hong, Rounak Dey, Xihong Lin, Brian Cleary, Edgar Dobriban.
    Nature Communications, 2022.
    code doi medRxiv talk web app
  4. HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
    David Hong, Kyle Gilman, Laura Balzano, Jeffrey A. Fessler.
    IEEE Transactions on Signal Processing, 2021.
    arXiv code doi software
  5. 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
  6. Subspace clustering using ensembles of K-subspaces
    John Lipor*, David Hong*, Yan Shuo Tan, Laura Balzano.
    Information and Inference, 2021.
    arXiv doi
  7. 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
  8. Stochastic Gradients for Large-Scale Tensor Decomposition
    Tamara G. Kolda, David Hong.
    SIAM Journal on Mathematics of Data Science, 2020.
    arXiv doi
  9. Generalized Canonical Polyadic Tensor Decomposition
    David Hong, Tamara G. Kolda, Jed A. Duersch.
    SIAM Review, 2020.
    arXiv doi software
  10. 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
  11. Asymptotic performance of PCA for high-dimensional heteroscedastic data
    David Hong, Laura Balzano, Jeffrey A. Fessler.
    Journal of Multivariate Analysis, 2018.
    arXiv doi
  12. 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

Refereed Conference Papers in Conference Proceedings

  1. Generic Properties of Koopman Eigenfunctions for Stable Fixed Points and Periodic Orbits
    Matthew D. Kvalheim, David Hong, Shai Revzen.
    24th International Symposium on Mathematical Theory of Networks and Systems (MTNS), 2020.
    arXiv doi
  2. 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 software
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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