Pin-Yu Chen (陳品諭)

Research Staff Member, Trusted AI Group & MIT-IBM AI Lab

IBM Research AI, IBM Thomas J. Watson Research Center, NY

Link to my Twitter Google scholar profile CV Academic activities

Contact: pinyuchen.tw at gmail.com (primary reviewer account) , pin-yu.chen at ibm.com

- I am a Research Staff Member of Trusted AI Group & PI of MIT-IBM AI Lab, IBM Thomas J. Watson Research Center. My recent research focus has been on adversarial machine learning and robustness of neural networks, and more broadly, making machine learning trustworthy. My research interest also includes graph learning, network data analytics and their applications to data mining, machine learning, signal processing, and cyber security. <bio>

- I am open to collaboration with highly motivated researchers!

- I am a PI of ongoing MIT-IBM research projects. Two of them are featured <here> and <here> and <MIT Quest for Intelligence Research> and <MIT_News>

- I received my Ph.D. degree in electrical engineering and computer science and M.A. degree in Statistics from the University of Michigan Ann Arbor in 2016, under the supervision of Prof. Alfred Hero.

- Editorial board: PLOS ONE, IEEE J-IOT (Guest)

- Workshop/Symposium organizer:

[KDD 2019] Adversarial Learning Methods for Machine Learning and Data Mining[IEEE GlobalSIP 2018] Signal Processing for Adversarial Machine Learning[IEEE ICME 2018] Machine Learning and Artificial Intelligence for Multimedia Creation

- Tutorial presenter:

[KDD 2019] Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning[IEEE BigData 2018] Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Deep Learning

- Selected conference reviewers:

NuerIPS, ICML, AAAI (Senior PC), ICLR, IJCAI, ICDM, WWW, INFOCOM, GLOBECOM, ICC, WCNC, ACC, ICASSP, ICME; ACMMM

- Selected journal reviewers:

Proc. IEEE, IEEE T-SP, T-IP, J-STSP, T-SIPN, T-KDE, T-PAMI, J-SAC, ToN, T-WC, T-VT, CL, SPL, T-PDS, T-IFS, T-NNLS, WCM, WCL, J-IoT, SPL, T-CNS, ACCESS, J-ETCAS; PLOS ONE
RecentEvents

Research Interests

  • Machine Learning: adversarial machine learning and robustness, online and distributed learning, unsupervised and semi-supervised learning on graphs
  • Cyber Security: attack and defense models, action recommendations for network resilience, malware propagation models
  • Graph Learning and Network Data Analytics: spectral graph theory and algorithms, graph signal processing, community detection, graph clustering, event propagation and control in networks, complex network

Selected Awards and Honors

  • NeurIPS Best Reviewer Award (2017) <Link>
  • Best Paper Finalist, ACM Workshop on Artificial Intelligence and Security (2017)
  • Outstanding Performance Award at Pacific Northwest National Laboratory (2015)
  • Univ. Michigan Rackham International Student Fellowship (Chia-Lun Lo Fellowship) (2013-2014) <Link>
  • EE:Systems Fellowship, University of Michigan, Ann Arbor (2012-2013)
  • Best Master Thesis Award of Graduate Institute of Communications Engineering, National Taiwan Univ. (2011)
  • Second Best Master Thesis Award of Chinese Institute of Electrical Engineering (2011)
  • IEEE GLOBECOM GOLD Best Paper Award (2010) <Link>
  • Ranked 1st Place (Full Scores) in Taiwan National College Entrance Exam (2005)

Selected Publications

Adversarial Machine Learning and Robustness of Neural Networks

-Attack & Defense

  1. “On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method,” ICCV 2019
    • Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, and Xue Lin
    • <ZOO_ADMM_code>
  2. “Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses,” IJCAI 2019
  3. “Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective,” IJCAI 2019
    • Kaidi Xu*, Hongge Chen*, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin,(*equal contribution)
    • <IBM_Research_Blog_GNN_HRS>
  4. “Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification,” SysML 2019
  5. “Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach,” ICLR 2019
    • Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, and Cho-Jui Hsieh
    • <Code>
  6. “Structured Adversarial Attack: Towards General Implementation and Better Interpretability,” ICLR 2019
    • Kaidi Xu* Sijia Liu*, Pu Zhao, Pin-Yu Chen, Huan Zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin (*equal contribution)
    • <StrAttack_code>
  7. “Characterizing Audio Adversarial Examples Using Temporal Dependency,” ICLR 2019
  8. “AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks,” AAAI 2019 (oral presentation)
  9. “Is Ordered Weighted $\ell_1$ Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR,” IEEE GlobalSIP 2018
    • Pin-Yu Chen*, Bhanukiran Vinzamuri*, and Sijia Liu (*equal contribution)
    • <poster>
  10. “Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning,” ACL 2018
    • Hongge Chen*, Huan Zhang*, Pin-Yu Chen, Jinfeng Yi, and Cho-Jui Hsieh (*equal contribution)
    • <ShowAndFool_code> <poster>
  11. “EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,” AAAI 2018
  12. “ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models,” ACM CCS Workshop on AI-Security, 2017

-Robustness Evaluation & Verification

  1. “PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach,” ICML 2019
    • Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Ivan Oseledets, Akhilan Boopathy, and Luca Daniel
    • <PROVEN_code> <slides>
  2. “CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks,” AAAI 2019 (oral presentation)
  3. “Efficient Neural Network Robustness Certification with General Activation Functions,” NeurIPS 2018
    • Huan Zhang*, Tsui-Wei Weng*, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel (*equal contribution)
    • <CROWN_code>
  4. “Is Robustness the Cost of Accuracy? A Comprehensive Study on the Robustness of 18 Deep Image Classification Models,” ECCV 2018
    • Dong Su*, Huan Zhang*, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao (*equal contribution)
    • <code> <slides>
  5. “Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” ICLR 2018

Cyber Security, Network Resilience

  1. “Traffic-aware Patching for Cyber Security in Mobile IoT,” IEEE Communications Magazine, 2017
  2. “Decapitation via Digital Epidemics: A Bio-Inspired Transmissive Attack,” IEEE Communications Magazine, 2016
    • Pin-Yu Chen, C.-C. Lin, S.-M. Cheng, C.-Y. Huang, and H.-C. Hsiao
  3. “Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection,” IEEE ICASSP, 2016
  4. “Action Recommendation for Cyber Resilience,” ACM CCS Workshop, 2015
    • S. Choudhury, Pin-Yu Chen, L. Rodriguez, D. Curtis, P. Nordquist, I. Ray, K. Oler, and P. Nordquist,
    • <PNNL research highlight>
  5. “Sequential Defense against Random and Intentional Attacks in Complex Networks”, Physical Review E, 2015
    • Pin-Yu Chen and S.-M. Cheng
  6. “Assessing and Safeguarding Network Resilience to Centrality Attacks,” IEEE Communications Magazine, 2014
  7. “Information Fusion to Defend Intentional Attack in Internet of Things,” IEEE Internet of Things Journal, 2014
    • Pin-Yu Chen, S.-M. Cheng, and K.-C. Chen
  8. “Smart Attacks in Smart Grid Communication Networks,” IEEE Communications Magazine, 2012

Graph Learning and Network Data Analytics

  1. “hpGAT: High-order Proximity Informed Graph Attention Network,” IEEE Access, 2019
    • Zhining Liu, Weiyi Liu, Pin-Yu Chen, Chenyi Zhuang and Chengyun Song,
  2. “Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications,” ICML 2019 (long oral presentation)
  3. “Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding,” Data Science and Engineering & ASONAM, 2019
  4. “Learning Graph Topological Features via GAN,” IEEE Access, 2019
    • Weiyi Liu, Pin-Yu Chen, Fucai Yu, Toyotaro Suzumura, Guangmin Hu,
  5. “Scalable Spectral Clustering Using Random Binning Features,” ACM KDD, 2018 (oral presentation)
  6. “Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering,” IEEE Transactions on Signal Processing, 2018
  7. “On the Supermodularity of Active Graph-based Semi-Supervised Learning with Stieltjes Matrix Regularization,” IEEE ICASSP, 2018
    • Pin-Yu Chen* and Dennis Wei* (*equal contribution)
    • <poster>
  8. “Revisiting Spectral Graph Clustering with Generative Community Models,” IEEE ICDM, 2017
  9. “Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms,” IEEE Transactions on Signal and Information Processing over Networks, 2017
    • Pin-Yu Chen and A. O. Hero
    • (awarded IEEE GlobalSIP Student Travel Grant) <slides> <MIMOSA_code>
  10. “Bias-Variance Tradeoff of Graph Laplacian Regularizer,” IEEE Signal Processing Letters, 2017
    • Pin-Yu Chen and S. Liu
  11. “Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications,” Social Network Analysis and Mining, 2018
    • Pin-Yu Chen, B. Zhang, and M. Hasan
    • <slides> <poster> <video> (awarded ACM KDD Student Travel Award)
  12. “When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective,” IEEE Communications Magazine, 2015
    • Pin-Yu Chen, S.-M. Cheng, P.-S. Ting, C.-W. Lien, and F.-J Chu
  13. “Deep Community Detection,” IEEE Transactions on Signal Processing, 2015
  14. “Phase Transitions in Spectral Community Detection,” IEEE Transactions on Signal Processing, 2015
    • Pin-Yu Chen and A. O. Hero
  15. “Universal Phase Transition in Community Detectability under a Stochastic Block Model,” Physical Review E, 2015
    • Pin-Yu Chen and A. O. Hero
  16. “Local Fiedler Vector Centrality for Detection of Deep and Overlapping Communities in Networks,” IEEE ICASSP, 2014

Event Propagation Models in Networks

  1. “Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach,” IEEE Transactions on Signal and Information Processing over Networks, 2018
    • Pin-Yu Chen, Chun-Chen Tu, Paishun Ting, Ya-Yun Luo, Danai Koutra, and Alfred Hero
  2. “Analysis of Data Dissemination and Control in Social Internet of Vehicles,” IEEE Internet of Things Journal, 2018
    • Pin-Yu Chen, Shin-Ming Cheng and Meng-Hsuan Sung
  3. “Analysis of Information Delivery Dynamics in Cognitive Sensor Networks Using Epidemic Models,” IEEE Internet of Things Journal, 2017
    • Pin-Yu Chen, S.-M. Cheng, and H.-Y. Hsu
  4. “Optimal Control of Epidemic Information Dissemination over Networks,” IEEE Transactions on Cybernetics, 2014
    • Pin-Yu Chen, S.-M. Cheng, and K.-C. Chen
  5. “On Modeling Malware Propagation in Generalized Social Networks,” IEEE Communications Letters, 2011
    • S.-M. Cheng, W. C. Ao, Pin-Yu Chen, and K.-C. Chen
  6. “Information Epidemics in Complex Networks with Opportunistic Links and Dynamic Topology," IEEE GLOBECOM, 2010
    • Pin-Yu Chen, and K.-C. Chen
    • (GOLD Best Paper Award)

Optimization for Machine Learning and Signal Processing

  1. “SignSGD via Zeroth-Order Oracle,” ICLR 2019
    • Sijia Liu, Pin-Yu Chen, Xiangyi Chen, and Mingyi Hung
  2. “Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization,” NeurIPS 2018
    • Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, and Lisa Amini
    • <poster>
  3. “Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity,” IEEE Transactions on Signal Processing, 2018
    • Sijia Liu, Pin-Yu Chen, and Alfred Hero
  4. “Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications,” AISTATS 2018
    • Sijia Liu, Jie Chen, Pin-Yu Chen, and Alfred Hero
    • <poster>

Interpretability, Explainability and Causality for Machine Learning Models

  1. “When Causal Intervention Meets Adversarial Perturbation and Image Masking for Deep Neural Networks,” IEEE ICIP 2019
    • Chao-Han Huck Yang*, Yi-Chieh Liu*, Pin-Yu Chen, Xiaoli Ma, Yi-Chang James Tsai (*equal contribution)
    • <Code>
  2. “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives,” NeurIPS 2018

Preprints

U.S. Patents

[PA1] System and Methods for Automated Detection, Reasoning, and Recommendations for Resilient Cyber Systems

Technical Reports

[T9] Sijia Liu, Pin-Yu Chen, Alfred Hero, and Indika Rajapakse, “Dynamic Network Analysis of the 4D Nucleome”

[T8] Sheng-Chun Kao*, Chao-Han Huck Yang*, Pin-Yu Chen, Xiaoli Ma, and Tushar Krishna, “Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization,” poster paper at IEEE/ACM International Symposium on Networks-on-Chip (NOCS), 2019 (*equal contribution)

[T7] Chia-Yi Hsu, Pin-Yu Chen, and Chia-Mu Yu, “Characterizing Adversarial Subspaces by Mutual Information,” poster paper at AsiaCCS, 2019

[T6] Pin-Yu Chen, Sutanay Choudhury, Luke Rodriguez, Alfred O. Hero, and Indrajit Ray, “Enterprise Cyber Resiliency Against Lateral Movement: A Graph Theoretic Approach,” technical report for a book chapter in “Industrial Control Systems Security and Resiliency: Practice and Theory,” Springer, 2019

[T5] Sijia Liu and Pin-Yu Chen, “Zeroth-Order Optimization and Its Application to Adversarial Machine Learning,” IEEE Intelligent Informatics BULLETIN (invited paper)

[T4] Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh, “Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning”

[T3] Yash Sharma and Pin-Yu Chen, “Bypassing Feature Squeezing by Increasing Adversary Strength”

[T2] Zhuolin Yang, Bo Li, Pin-Yu Chen, Dawn Song, “Towards Mitigating Audio Adversarial Perturbations”

[T1] Pin-Yu Chen, Meng-Hsuan Sung, and Shin-Ming Cheng, “Buffer Occupancy and Delivery Reliability Tradeoffs for Epidemic Routing”

Internship

  • Pacific Northwest National Laboratory (PNNL) - Data Science PhD Intern
    • action recommendations for real-time service degradation attacks
    • user segmentation and host hardening against lateral movement attacks

Fun and Proud Fact: My Erdos number is 4 (through two distinct paths)!!

  1. Me -> Alfred Hero -> Wayne Stark -> Robert McEliece -> Paul Erdos
  2. Me -> Pai-Shun Ting -> John. P. Hayes -> Frank Harary -> Paul Erdos