Pin-Yu Chen (陳品諭)

Research Staff Member, IBM Research AI; MIT-IBM Watson AI Lab; RPI-IBM AIRC

IBM Thomas J. Watson Research Center, NY, USA

Link to my Twitter Google scholar profile CV Bio

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

- I am a Principal Research Scientist of Trusted AI Group & PI of MIT-IBM Watson AI Lab, IBM Thomas J. Watson Research Center. I am also the Chief Scientist of RPI-IBM AI Research Collaboration program. My recent research focus has been on adversarial machine learning and robustness of neural networks, and more broadly, making machine learning trustworthy. Here is my <bio>. Check out my research vision and portfolio.

- My research contributes to IBM Adversarial Robustness Toolbox, AI Explainability 360, AI Factsheets 360, and Watson Studio

- I am open to collaboration with highly motivated researchers!

- 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.

- Workshop organizer (selected): ICML'22, KDD'19-'22, MLSyS'22, NeurIPS'21

- Tutorial presenter (selected): AAAI'22, IJCAI'21, MLSS'21, CVPR'21, ECCV'20, CVPR'20

- Area Chair/Senior PC: NeurIPS, ICML, AAAI, IJCAI

- Technical Program Committee: IEEE S&P, ACM CCS

- Editor: TMLR

RecentEvents

Featured Talks

Funded Research Projects

  1. IARPA program on Microelectronics in Support of Artificial Intelligence (MicroE4AI) [2022-current]

  2. IBM PI of the Department of Energy project "A Robust Event Diagnostics Platform: Integrating Tensor Analytics and Machine Learning into Real-time Grid Monitoring" [2019 -2021]

  3. RPI-IBM AI Research Collaboration (AIRC): PI of ongoing RPI-IBM research projects [2019 - present]

  4. MIT-IBM Watson AI Lab: PI of ongoing MIT-IBM research projects. Two of them are featured in <MIT Quest for Intelligence Research> and <MIT_News> [2018 - present]

  5. UIUC-IBM Center for Cognitive Computing System Research (C3SR) [2019 - present]

Research Interests

  • Machine Learning: adversarial machine learning and robustness, online and distributed learning, unsupervised and semi-supervised learning

  • Cyber Security: AI for 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

  • 3 IBM Research Accomplishments (2021): Robust AI, Generative AI, Optimization for AI

  • IBM Corporate Technical Award (2021) on trustworthy AI

  • Special IBM Research Division Team Award for COVID-19 Research (2020)

  • IBM Master Inventor (2020)

  • Listed as “Top Subject Matter Experts in AI & ML” by onalytica (2020) <Link>

  • 3 IBM Outstanding Research Accomplishments (2020): adversarial robustness, deep learning on graphs, trustworthy AI

  • 3 IBM Research Accomplishments (2019): adversarial robustness, deep learning on graphs, trustworthy AI

  • 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

I. Adversarial Machine Learning and Robustness of Neural Networks

-Attack & Defense

  1. “Distributed Adversarial Training to Robustify Deep Neural Networks at Scale,” UAI 2022 (*equal contribution)

    • Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, and Sijia Liu

    • <DAT_code>

  2. “A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction,” NAACL 2022

  3. “CAFE: Catastrophic Data Leakage in Vertical Federated Learning,” NeuIPS 2021

    • Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen

    • <CAFE_code>

  4. “Adversarial Attack Generation Empowered by Min-Max Optimization,” NeuIPS 2021

    • Jingkang Wang*, Tianyun Zhang*, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, and Bo Li (*equal contribution)

  5. “How Robust are Randomized Smoothing based Defenses to Data Poisoning?” CVPR 2021

  6. “On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning,” ICLR 2021

    • Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, and Meng Wang

    • <Robust_MAML_video>

  7. “Self-Progressing Robust Training,” AAAI 2021

    • Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, and Payel Das

    • <SPROUT_code>

  8. “Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning,” AAAI 2021

    • Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan

  9. “Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases,” ECCV 2020

  10. “Adversarial T-shirt! Evading Person Detectors in A Physical World,” ECCV 2020

  11. “Proper Network Interpretability Helps Adversarial Robustness in Classification,” ICML 2020

    • Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, and Luca Daniel

  12. “Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness,” ICLR 2020

  13. “DBA: Distributed Backdoor Attacks against Federated Learning,” ICLR 2020

    • Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li

    • <DBA_video>

  14. “Sign-OPT: A Query-Efficient Hard-label Adversarial Attack,” ICLR 2020

    • Minhao Cheng*, Simranjit Singh*, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, and Cho-Jui Hsieh (*equal contribution)

    • <Sign-OPT_IBM>

  15. “Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples,” AAAI 2020

  16. “Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent,” AAAI 2020

    • Pu Zhao, Pin-Yu Chen, Siyue Wang, and Xue Lin

    • <ZO_NGD_code>

  17. “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

    • <ZO_ADMM_code>

  18. “Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses,” IJCAI 2019

  19. “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>

  20. “Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification,” SysML 2019

  21. “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

    • <OptAttack_code>

  22. “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>

  23. “Characterizing Audio Adversarial Examples Using Temporal Dependency,” ICLR 2019

  24. “AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks,” AAAI 2019 (oral presentation)

  25. “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>

  26. “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>

  27. “EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,” AAAI 2018

  28. “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 & Certification

  1. “Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness,” ICML 2022

    • Tianlong Chen*, Huan Zhang*, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, and Zhangyang Wang (*equal contribution)

    • <Linearity_Grafting_code>

  2. “Vision Transformers are Robust Learners,” AAAI 2022

  3. “Training a Resilient Q-Network against Observational Interference,” AAAI 2022

    • Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, and Pin-Yu Chen

    • <CIQ_code>

  4. “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?,” NeurIPS 2021

  5. “Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning,” NeurIPS 2021

    • Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm

    • <UDA_limit_code>

  6. “CRFL: Certifiably Robust Federated Learning against Backdoor Attacks,” ICML 2021

    • Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li

    • <CRFL_code>

  7. “Non-Singular Adversarial Robustness of Neural Networks,” ICASSP 2021

    • Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, and Pin-Yu Chen

  8. “Hidden Cost of Randomized Smoothing,” AISTATS 2021

    • Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei (Lily) Weng, Sijia Liu, Pin-Yu Chen, and Luca Daniel

  9. “Fast Training of Provably Robust Neural Networks by SingleProp,” AAAI 2021

    • Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, and Luca Daniel

  10. “Higher-Order Certification For Randomized Smoothing,” NeurIPS 2020 (spotlight)

    • Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei (Lily) Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel

    • <IBM_Blog_Certification>

  11. “Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations,” CVPR 2020 (oral presentation)

  12. “Towards Certificated Model Robustness Against Weight Perturbations,” AAAI 2020

    • Tsui-Wei Weng*, Pu Zhao*, Sijia Liu, Pin-Yu Chen, Xue Lin, and Luca Daniel (*equal contribution)

    • <code> <poster>

  13. “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>

  14. “CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks,” AAAI 2019 (oral presentation)

  15. “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>

  16. “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)

    • <Tradeoff_code> <slides>

  17. “Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” ICLR 2018

-Applications to Other Machine Learning Tasks

  1. “Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning,” AAAI 2022

    • Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Lu, and Chia-Mu Yu

    • <UAE_code>

  2. “Optimizing Molecules using Efficient Queries from Property Evaluations,” Nature Machine Intelligence, 2021

    • Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, and Payel Das

    • <QMO_code> <IBM_QMO>

  3. “Voice2Series: Reprogramming Acoustic Models for Time Series Classification,” ICML 2021

    • Chao-Han Huck Yang, Yun-Yun Tsai, and Pin-Yu Chen

    • <V2S_code>

  4. “Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks,” IJCAI 2021

    • Siyue Wang, Xiao Wang, Pin-Yu Chen. Pu Zhao, and Xue Lin

  5. “AID: Attesting the Integrity of Deep Neural Networks,” DAC 2021

    • Omid Aramoon, Pin-Yu Chen, and Gang Qu,

  6. “Don't Forget to Sign the Gradients!,” MLSyS 2021

  7. “Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks,” AAAI 2021

  8. “Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources,” ICML 2020

II. 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

III. Graph Learning and Network Data Analytics

  1. “Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case,” ICML 2020

    • Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong,

  2. “hpGAT: High-order Proximity Informed Graph Attention Network,” IEEE Access, 2019

    • Zhining Liu, Weiyi Liu, Pin-Yu Chen, Chenyi Zhuang, and Chengyun Song,

  3. “Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications,” ICML 2019 (long oral presentation)

  4. “Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding,” Data Science and Engineering & ASONAM, 2019

  5. “Learning Graph Topological Features via GAN,” IEEE Access, 2019

    • Weiyi Liu, Hal Cooper, Min-Hwan Oh, Pin-Yu Chen, Sailung Yeung, Fucai Yu, Toyotaro Suzumura, Guangmin Hu

  6. “Scalable Spectral Clustering Using Random Binning Features,” ACM KDD, 2018 (oral presentation)

  7. “Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering,” IEEE Transactions on Signal Processing, 2018

  8. “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>

  9. “Revisiting Spectral Graph Clustering with Generative Community Models,” IEEE ICDM, 2017

  10. “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>

  11. “Bias-Variance Tradeoff of Graph Laplacian Regularizer,” IEEE Signal Processing Letters, 2017

    • Pin-Yu Chen and S. Liu

  12. “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)

  13. “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

  14. “Deep Community Detection,” IEEE Transactions on Signal Processing, 2015

  15. “Phase Transitions in Spectral Community Detection,” IEEE Transactions on Signal Processing, 2015

    • Pin-Yu Chen and A. O. Hero

  16. “Universal Phase Transition in Community Detectability under a Stochastic Block Model,” Physical Review E, 2015

    • Pin-Yu Chen and A. O. Hero

  17. “Local Fiedler Vector Centrality for Detection of Deep and Overlapping Communities in Networks,” IEEE ICASSP, 2014

IV. 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

V. Optimization and Algorithms for Machine Learning and Signal Processing

  1. “Zeroth-order Optimization for Composite Problems with Functional Constraints,” AAAI 2022 (oral presentation)

    • Zichong Li, Pin-Yu Chen*, Sijia Liu*, Songtao Lu*, and Yangyang Xu* (*alphabetical order)

  2. “Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination,” NeurIPS 2021

    • Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, and Payel Das

  3. “Rate-improved Inexact Augmented Lagrangian Method for Constrained Nonconvex Optimization,” AISTATS 2021

    • Zichong Li, Pin-Yu Chen*, Sijia Liu*, Songtao Lu*, and Yangyang Xu* (*alphabetical order)

  4. “Optimizing Mode Connectivity via Neuron Alignment,” NeurIPS 2020

  5. “ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training,” NeurIPS 2020

    • Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi (Viji) Srinivasan, Wei Zhang, and Kailash Gopalakrishnan

    • <IBM_blog_ScaleCom>

  6. “A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning,” IEEE Signal Processing Magazine, 2020

    • Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred Hero, and Pramod K. Varshney

  7. “SignSGD via Zeroth-Order Oracle,” ICLR 2019

    • Sijia Liu, Pin-Yu Chen, Xiangyi Chen, and Mingyi Hong

  8. “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>

  9. “Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity,” IEEE Transactions on Signal Processing, 2018

    • Sijia Liu, Pin-Yu Chen, and Alfred Hero

  10. “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>

VI. Interpretability, Explainability, Fairness, and Causality for Machine Learning Systems

  1. “AI Explainability 360: Impact and Design,” IAAI 2022

    • Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

  2. “Leveraging Latent Features for Local Explanations,” KDD 2021

    • Ronny Luss*, Pin-Yu Chen*, Amit Dhurandhar*, Prasanna Sattigeri*, Yunfeng Zhang*, Karthikeyan Shanmugam, and Chun-Chen Tu (*equal contribution)

  3. “AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models,” Journal of Machine Learning Research, 2020

    • Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang (alphabetical order)

  4. “An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy,” ICML 2020

    • Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush R. Varshney

  5. “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>

  6. “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives,” NeurIPS 2018

VII. Deep Learning and Generalization

  1. ”Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning,” ICML 2022

    • Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, and Tianyi Chen

    • <Sharp-MAML_code>

  2. ”Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework,” ICML 2022

    • Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, and Lily Weng

  3. “MAML is a Noisy Contrastive Learner in Classification,” ICLR 2022

    • Chia Hsiang Kao, Wei-Chen Chiu, and Pin-Yu Chen

  4. “Auto-Transfer: Learning to Route Transferable Representations,” ICLR 2022

    • Keerthiram Murugesan*, Vijay Sadashivaiah*, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, and Amit Dhurandhar (*equal contribution)

    • <AutoTransfer_code>

  5. “How Unlabeled Data Improve Generalization in Self-training? A One-hidden-layer Theoretical Analysis,” ICLR 2022

    • Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong

  6. “Predicting Deep Neural Network Generalization with Perturbation Response Curves,” NeurIPS 2021

    • Yair Schiff, Brian Quanz, Payel Das, and Pin-Yu Chen

  7. “Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations,” NeurIPS 2021

    • Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, and Pin-Yu Chen

  8. “Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks,” NeurIPS 2021

    • Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong

Preprints

Patents

[PA27] Transfer learning with machine learning systems

[PA26] Summarizing Videos Via Side Information

[PA25] Detecting Trojan Neural Networks

[PA24] State-augmented Reinforcement Learning

[PA23] Query-based Molecule Optimization and Applications to Functional Molecule Discovery

[PA22] Efficient Search of Robust Accurate Neural Networks

[PA21] Arranging content on a user interface of a computing device

[PA20] Filtering artificial intelligence designed molecules for laboratory testing

[PA19] Training robust machine learning models

[PA18] Robustness-aware quantization for neural networks against weight perturbations

[PA17] Inducing Creativity in an Artificial Neural Network

[PA16] Interpretability-Aware Adversarial Attack and Defense Method for Deep Learnings

[PA15] Mitigating adversarial effects in machine learning systems

[PA14] Designing and folding structural proteins from the primary amino acid sequence

[PA13] Contrastive explanations for images with monotonic attribute functions

[PA12] Efficient and secure gradient-free black box optimization

[PA11] Explainable machine learning based on heterogeneous data

[PA10] Computational creativity based on a tunable creativity control function of a model

[PA9] Integrated noise generation for adversarial training

[PA8] Framework for Certifying a lower bound on a robustness level of convolutional neural networks

[PA7] Adversarial input identification using reduced precision deep neural networks

[PA6] Model agnostic contrastive explanations for structured data

[PA5] Contrastive explanations for interpreting deep neural networks

[PA4] Computational Efficiency in Symbolic Sequence Analytics Using Random Sequence Embeddings

[PA3] Graph similarity analytics

[PA2] Testing adversarial robustness of systems with limited access

[PA1] System and methods for automated detection, reasoning, and recommendations for resilient cyber systems

Technical Reports

[T11] Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R Varshney, Dennis Wei, and Yunfeng Zhang. “One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques,”

[T10] Rise Ooi, Chao-Han Huck Yang, Pin-Yu Chen, Vìctor Eguìluz, Narsis Kiani, Hector Zenil, David Gomez-Cabrero, Jesper Tegnèr, “Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning”

[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”

Conference/Workshop Organizer:

  1. [ICML 2022] New Frontiers in Adversarial Machine Learning

  2. [MLSyS 2022] Cross-Community Federated Learning: Algorithms, Systems and Co-designs

  3. [ICASSSP 2022] Special Session for Quantum Machine Learning for Speech and Language Processing

  4. [NeurIPS 2021] New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership

  5. [KDD 2019-2022] Adversarial Learning Methods for Machine Learning and Data Mining

  6. [IEEE GlOBECOM 2020] Industrial Forum Co-Chair

  7. [IEEE GlobalSIP 2018] Signal Processing for Adversarial Machine Learning

  8. [IEEE ICME 2018] Machine Learning and Artificial Intelligence for Multimedia Creation

Tutorial Presenter:

  1. [ICASSP 2022] Adversarial Robustness and Reprogramming for Speech and Language Processing: Challenges and New Opportunities

  2. [AAAI 2022] Adversarial Machine Learning for Good

  3. [MLSS 2021] Holistic Adversarial Robustness for Deep Learning

  4. [IJCAI 2021] Quantum Neural Networks for Speech and Natural Language Processing

  5. [CVPR 2021] Practical Adversarial Robustness in Deep Learning: Problems and Solutions

  6. [IEEE HOST 2020] Security Issues in AI and Their Impacts on Hardware Security

  7. [ECCV 2020] Adversarial Robustness of Deep Learning Models: Attack, Defense, Verification, and Beyond

  8. [CVPR 2020] Zeroth Order Optimization: Theory and Applications to Deep Learning

  9. [ICASSP 2020] Adversarial Robustness of Deep Learning Models: Attack, Defense and Verification

  10. [KDD 2019] Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning

  11. [IEEE BigData 2018] Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Deep Learning

Service

Editorial board

  • Transactions on Machine Learning Research (TMLR)

  • PLOS ONE

  • KSII-TIIS (area editor): 2020-2021

  • IEEE J-IOT (Guest)

Area Chair/Senior PC

  • NeurIPS, ICML, AAAI, IJCAI

Featured conference reviewers

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

Featured journal reviewers

  • JMLR; 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, Netw. Mag., Comm. Mag.; PLOS ONE; Communications of the ACM

Mentorship

Students having me in PhD Thesis Committee:

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