I am currently a PhD student under the supervision of Dr. Quanquan Gu in Department of Computer Science,
University of California, Los Angeles.
My research interests broadly are machine learning, optimization, adversarial machine learning and data
mining. See my latest Curriculum Vitae.
I will join the College of Information Sciences and Technology (IST) at Penn State University (PSU) in Fall 2021 as a tenure-track assistant professor.
Multiple fully-funded PhD positions available in Spring 2022 or later. Multiple remote intern positions available in the Summer 2021 and Fall 2021.
2019 - Now
Ph.D., Computer Science
University of California, Los Angeles
2015 - 2019 (transferred)
Ph.D., Computer Science
University of Virginia
2011 - 2015
B.S., Electronic Engineering and Information Science
University of Science and Technology of China
News and Announcement
[04/2021] I will join the College of Information Sciences and Technology (IST) at Penn State University (PSU) in Fall 2021 as a tenure-track assistant professor.
[07/2020] Released Model Robustness (ADBD) Leaderboard under RayS attack:
Benchmarking state-of-the-art robust trained models with ADBD metric
[05/2020] Our paper is accepted to KDD 2020:
"RayS: A Ray Searching Method for Hard-label Adversarial Attack"
[04/2020] Our paper is accepted to IJCAI 2020:
"Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks"
[04/2020] We just launched a project using machine learning and AI to combat Covid-19!
Live data visualization and new cases / peak predictions
[01/2020] Our paper is accepted to AISTATS 2020:
Intrinsic Robustness of Image Distributions using Conditional Generative Models"
[11/2019] Our paper is accepted to AAAI 2020:
Framework for Efficient and Effective Adversarial Attacks"
Publications (*equal contribution)
Difan Zou, Lingxiao Wang, Pan Xu, Jinghui Chen, Weitong Zhang and Quanquan Gu,
Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States
, ICLR 2021 Workshop on Machine Learning for Preventingand Combating Pandemics.
Dongruo Zhou*, Jinghui Chen*, Yuan Cao*, Yiqi Tang, Ziyan Yang, and Quanquan Gu,
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
, NeurIPS 2020 Workshop on Optimization for Machine Learning.
Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu and Jingjing Liu,
Efficient Robust Training via Backward Smoothing
, arXiv:2010.01278, 2020.
Boxi Wu*, Jinghui Chen*, Deng Cai, Xiaofei He and Quanquan Gu,
Does Network Width Really Help Adversarial Robustness?
, arXiv:2010.01279, 2020.
COVID-19 Forecast Hub Consortium, Jinghui Chen,
Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.
, medRxiv:2020.08.19.20177493, 2020.
Jinghui Chen and Quanquan Gu,
RayS: A Ray Searching Method for Hard-label Adversarial Attack
, in Proc of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),
San Diego, CA, USA 2020.
A short version of this paper also appears on ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning
and ECCV 2020 Workshop on Adversarial Robustness in the Real World.
Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao and Quanquan Gu,
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
, In Proc. of 29th International Joint Conference on Artificial Intelligence (IJCAI),
Yokohama, Japan, 2020.
Xiao Zhang*, Jinghui Chen*, Quanquan Gu and David Evans, Understanding the
Intrinsic Robustness of Image Distributions using Conditional Generative Models , In
Proc of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS),
Palermo, Sicily, Italy, 2020.
Jinghui Chen, Dongruo Zhou, Jinfeng Yi and Quanquan Gu, A Frank-Wolfe
Framework for Efficient and Effective Adversarial Attacks , In Proc. of the 34th
Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020.
Pan Xu*, Jinghui Chen*and Quanquan Gu, Global Convergence of Langevin
Dynamics Based Algorithms for Nonconvex Optimization, In Proc. of the 32nd Advances in
Neural Information Processing Systems (NIPS), Montréal, Canada, 2018.
Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma and Quanquan Gu, Covariate
Adjusted Precision Matrix Estimation via Nonconvex Optimization , in Proc. of the 35th
International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.
(Long Oral Presentation, 4.8%)
Jinghui Chen and Quanquan Gu, Fast Newton Hard Thresholding Pursuit for
Sparsity Constrained Nonconvex Optimization, in Proc of the 23rd ACM SIGKDD Conference
on Knowledge Discovery and Data Mining (KDD), Halifax, Nova Scotia, Canada, 2017.
Jinghui Chen, Lingxiao Wang, Xiao Zhang, and Quanquan Gu, Robust Wirtinger
Flow for Phase Retrieval with Arbitrary Corruption , arXiv:1704.06256, 2017.
Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga, Outlier Detection
with Autoencoder Ensembles, in Proc of the 17th SIAM International Conference on Data
Mining (SDM), Houston, Texas, USA, 2017.
Jinghui Chen and Quanquan Gu, Stochastic Block Coordinate Gradient Descent
for Sparsity Constrained Optimization, in Proc of the 32th International Conference on
Uncertainty in Artificial Intelligence (UAI), New York / New Jersey, USA, 2016.
Florian Baumann, Jinghui Chen, Karsten Vogt, and Bodo Rosenhahn, Improved
threshold Selection by using Calibrated Probabilities for Random Forest Classifiers in
Proc of the 12th Conference on Computer and Robot Vision (CRV), Halifax, Nova Scotia, Canada,
06/2020 - 09/2020
Microsoft Research, Redmond, WA, USA
06/2019 - 08/2019
Machine Learning Intern
Twitter, San Francisco, CA, USA
06/2018 - 08/2018
JD.COM Silicon Valley Research Center, Mountain View, CA, USA
06/2016 - 08/2016
IBM T.J Watson Research Center, Yorktown Height, New York, USA
02/2015 - 06/2015
University of Hannover, Germany
07/2014 - 08/2014
University of Birmingham, England