Research Portfolio

My research vision: AI Model Inspector for AI Maintenance -- Make the robustness inspection pipeline for AI models as reliable, standard, and easy, as car maintenance. Check out this article for details.

See our survey paper on Holistic Adversarial Robustness of Deep Learning Models

Adversarial Machine Learning: Attack, Defense, and Robustness Evaluation & Verification

    • ZOO [AI-Sec'17]: powerful black-box attack to neural networks - nearly the same performance as white-box attacks

    • EAD [AAAI'18, two ICLR'18 Wksp, DSN'18 Wksp]: crafting L1 norm based adversarial examples - better attack transferability; weakened several defenses and adversary analysis

    • CLEVER [ICLR'18, GlobalSIP'18]: attack-agnostic network robustness measure - estimating certified attack lower bounds

    • Adversarial T-Shirt [ECCV'20]: physical adversarial examples evading person detector

    • Show-and-Fool [ACL'18]: adversarial examples for neural image captioning systems

    • Adversarial robustness v.s. classification accuracy tradeoff uncovered from 18 deep ImageNet models + attack transferability analysis between 306 pairs of these networks [ECCV'18]

    • Adversarial attack on sparse regression (feature identification) [GlobalSIP'18]

    • CROWN & CNN-Cert & PROVEN & Semantify [NeurIPS'18, AAAI'19, ICML'19, CVPR'20]: Formal (worst-case of probabilistic) and efficient robustness certification of neural networks with general activation functions, popular layer modules, and semantic perturbations

    • AutoZOOM & ZO-NGD & ZO-ADMM [AAAI'19, ICCV'19, AAAI'20]: query-efficient black-box attacking acceleration via dimensional reduction and advanced zeroth-order optimization techniques (soft-label & hard-label attacks)

    • Opt Attack & ZO-ADMM & Sign-OPT [ICLR'19, ICCV'19, ICLR'20]: Query-efficient zeroth-order optimization based black-box attack with limited information (decision-based model revealing only top-1 prediction label)

    • TD detection [ICLR'19]: Detecting adversarial audio inputs using temporal dependency

    • Structured adversarial attack [ICLR'19]: spatial structure guided adversarial attack and model interpretability

    • Paraphrasing Attack [SysML'19]: Joint text paraphrasing adversarial attacks at the word and sentence levels and adversarial training

    • First-order optimization based edge perturbation attack and defense (adversarial training) for graph neural networks [IJCAI'19, ICASSP'20]

    • HRS [IJCAI'19]: Hierarchical random switching to strengthen the robustness of a trained based model by increasing attacker's costs and improving robustness-accuracy tradeoff

    • Seq2Sick [AAAI'20]: Generating adversarial examples for sequence-to-sequence models (e.g., machine translation, summarization)

    • Certified robustness of neural network weight perturbation and its application to robust weight quantization [AAAI'20]

    • DBA [ICLR'20]: Distributed backdoor attack designed for federated learning; more effective and stealthy

    • Data leakage in federated learning [NeurIPS'21]: discovery and mitigation of large-scale training data leakage in vertical federated learning

    • Model sanitization with limited clean data [ICLR'20]: mitigate adversarial effect of a tampered (Trojan) model via mode connectivity

    • Adversarial robustness transfer and preservation in contrastive learning [NeurIPS'21] and meta learning [ICLR'21]

ZOO (black-box attack via direct model queries)

[AI-Sec'17] https://arxiv.org/abs/1708.03999

EAD (L1 distortion based white-box attack)

[AAAI'18] https://arxiv.org/abs/1709.04114 [ICLR'18 Wksp] https://arxiv.org/abs/1710.10733[ICLR'18 Wksp] https://arxiv.org/abs/1803.09638[DSN'18 Wksp] https://arxiv.org/abs/1805.00310

Show-and-Fool: adversarial examples for neural image captioning systems

[ACL'18] https://arxiv.org/abs/1712.02051

Accuracy v.s. robustness tradeoff of contrastive learning methods

[NuerIPS'21] https://arxiv.org/abs/2111.01124

Accuracy v.s. robustness tradeoff of 18 ImageNet models

Physical Adversarial T-Shirt

[ECCV'20] https://arxiv.org/abs/1910.11099

Accuracy v.s. robustness tradeoff of different vision transformers

https://arxiv.org/abs/2103.15670

AutoZOOM: query-efficient black-box adversarial attacking acceleration via dimensional reduction and zeroth-order optimization

Advanced zeroth order optimization = Query-efficient design of adversarial example generation process !

Robustness verification and evaluation for neural nets

Robustness certification for semantic perturbations

[CVPR'20] https://arxiv.org/abs/1912.09533

Adversarial attack on sparse regression

[GlobalSIP'18] https://arxiv.org/abs/1809.08706

HRS: Hierarchical random switching to strengthen the robustness of a trained based model

[IJCAI'19] https://arxiv.org/abs/1908.07116

Detecting adversarial audio inputs using temporal dependency

[ICLR'19] https://arxiv.org/abs/1809.10875

DBA attack exploits the distributed learning nature of federated learning to distribute a global trigger (Trojan) pattern over malicious agents

[ICLR'20] https://openreview.net/forum?id=rkgyS0VFvr

AI (Deep Learning) x [The Delta!]

AI x [Financial Applications]

A general framework of (deep) reinforcement learning for portfolio management with noisy and heterogeneous alternative data (e.g., stock prices + financial news)

[AAAI'20] https://arxiv.org/abs/2002.05780

AI x [Model IP Protection]

A general and practical framework for model watermark embedding and remote verification, and fingerprinting

[MLSyS'21] https://arxiv.org/abs/2103.03701

[IJCAI'21] https://arxiv.org/abs/2105.07078

AI x [Scientific Discovery]

Machine learning guided molecule optimization with design constraints

[Nature Machine Intelligence] https://arxiv.org/abs/2011.01921

Network Reprogramming: Data-Efficient & Model-Agnostic Transfer Learning

See our survey paper on Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning

Overview of model reprogramming framework: https://arxiv.org/abs/2202.10629

Reprogramming black-box machine learning systems

[ICML'20] https://arxiv.org/abs/2007.08714


Reprogramming human voice models for time series classification

[ICML'21] https://arxiv.org/abs/2106.09296

Community Detection: Theory and Algorithms

    • Phase transition analysis of community detection under general connectivity models [T-SP, Phy. Rev. E]

    • AMOS & MIMOSA: theory-driven automated community detection algorithms for single-layer [T-SP] and multi-layer graphs [T-SIPN]

    • Deep (core) community detection [T-SP]

    • SGC-GEN: pseudo-supervised community detection meta algorithm [ICDM'17]

To be detectable, or not to be... Performance characterization of community detection

Communication detection in multi-layer networks

Event Propagation and Control in Networks

    • Modeling malware propagation in heterogeneous networks [Comm. Mag, Comm. Lett., J-IoT, T-CB, GLOBECOM'10]

    • Event propagation control via node and edge patching in communication networks [Comm. Mag.]

    • Identifying influential links on Twitter networks using network of networks model [T-SIPN]

Information propagation in heterogeneous networks

Malware propagation via multiple paths

Tweet propagation and user language fields

Network Analytics and Graph Data Mining

    • FINGER: Fast incremental computation of Von Neumann graph entropy [ICML'19]

    • Neural network based Bayesian personalized ranking for attributed network embedding [Data Science and Engineering]

    • Graph Attention Network using High-Order (beyond 1-hop neighborhood) information [IEEE Access]

    • GAN-based graph generator learned from a single graph [IEEE Access]

    • Bifurcation analysis of cell reprogramming [ICASSP'18, iScience]

    • Scalable end-to-end spectral clustering using random features [KDD'18]

    • Structural feature extraction from a single graph or a graph sequence [ICASSP'16]

    • Anomaly detection based on graph connectivity [https://arxiv.org/abs/1905.01002]

Network Resilience

    • LFVC: effective centrality measure based attack for network disruption [ICASSP'14, Comm. Mag.]

    • Sequential and game-theoretic information fusion for defending connectivity attacks [Phy. Rev. E, J-IoT]

Optimization for Machine Learning and Signal Processing

    • Mode connectivity in loss landscapes of deep learning [NeurIPS'20]

    • Survey on Zeroth Order optimization [IEEE Signal Processing Magazine]

    • Zeroth-order signSGD: faster convergence of zeroth order optimization [ICLR'19]

    • Non-convex zeroth order stochastic variance reduced algorithm [NeurIPS'18]

    • Accelerated distributed dual averaging over networked agents [T-SP]

    • Zeroth-order ADMM: convergence and algorithm [AISTATS'18]

(Last updated in Feb. 2022)