個(gè)人簡介：

婁堅(jiān)，副教授，博士生導(dǎo)師。曾于美國埃默里大學(xué)(Emory University)從事博士后研究工作。主要研究方向包括可信人工智能、可信大模型、人工智能隱私保護(hù)、數(shù)據(jù)隱私保護(hù)、數(shù)據(jù)質(zhì)量評估等。近年在NeurIPS、ICCV、CVPR、SIGMOD、VLDB、WWW、ACM CCS、IEEE S&P、NDSS、TDSC等人工智能、數(shù)據(jù)庫、安全與隱私保護(hù)領(lǐng)域的頂會(huì)頂刊上發(fā)表論文60余篇，并獲得頂會(huì)ACM CCS 2024杰出論文獎(jiǎng)(ACM SIGSAC Distinguished Paper Award)，IEEE/WIC/ACM WI-IAT 2020最佳理論論文獎(jiǎng)(Best in Theoretical Paper Award)等，研究成果曾獲國際知名科技媒體New Scientist采訪報(bào)道。擔(dān)任人工智能頂會(huì)ICML領(lǐng)域主席、AAAI資深程序委員，安全與隱私保護(hù)頂會(huì)ACM CCS程序委員，數(shù)據(jù)庫頂會(huì)VLDB程序委員。

郵箱：louj5@mail.sysu.edu.cn

個(gè)人主頁：https://sites.google.com/view/jianlou

目前招收1～2名2025年9月入學(xué)博士生，招收2～3名2025年9月推免（2026年9月入學(xué)）碩士生與直博生，依托學(xué)院可信大模型研究中心常年招收多名博士后，常年招收大二、大三有志于科研的本科生，歡迎感興趣的同學(xué)聯(lián)系！

 

研究與招生：

招生方向包括但不限于可信人工智能、可信大模型、人工智能隱私保護(hù)、數(shù)據(jù)治理與服務(wù)、數(shù)據(jù)質(zhì)量評估、數(shù)據(jù)隱私保護(hù)等，重點(diǎn)研究如何利用數(shù)據(jù)治理、數(shù)據(jù)質(zhì)量評估及隱私保護(hù)等理論與方法，確保人工智能和大模型在實(shí)際應(yīng)用中具備可信性，符合社會(huì)倫理和法規(guī)要求，規(guī)避潛在風(fēng)險(xiǎn)與有害行為，同時(shí)保護(hù)數(shù)據(jù)提供者與模型使用者的隱私。課題組為科研表現(xiàn)優(yōu)異的同學(xué)提供多種形式的海內(nèi)外高校學(xué)術(shù)交流訪問和深造機(jī)會(huì)，為優(yōu)秀碩士生提供碩轉(zhuǎn)博銜接培養(yǎng)機(jī)會(huì)。

  • 歡迎有意攻讀必贏3003no1線路檢測中心博士與碩士研究生的同學(xué)與我們聯(lián)系，目前招收1～2名2025年9月入學(xué)博士生！

  • 歡迎有意來必贏3003no1線路檢測中心做博士后的同學(xué)與我們聯(lián)系，目前我們團(tuán)隊(duì)依托學(xué)院可信大模型研究中心招收多名博士后！

  • 歡迎對科研感興趣或想體驗(yàn)科研的本科同學(xué)加入我們，參與科研實(shí)習(xí)、大創(chuàng)、學(xué)科競賽、答疑解惑等形式都可以！

聯(lián)系方式為郵箱louj5@mail.sysu.edu.cn或軟工學(xué)院323線下交流。

 

學(xué)術(shù)服務(wù)：

領(lǐng)域主席(Area Chair)與資深程序委員(Senior PC Member): 人工智能頂會(huì)ICML 2024-2025；AAAI 2025

程序委員(PC Member): 信息安全頂會(huì)ACM CCS 2024-2025、2022；IEEE EuroS&P 2025；數(shù)據(jù)庫頂會(huì)VLDB 2023-2024

審稿人：NeurIPS、ICLR、KDD、AAAI、IJCAI、TDSC、TKDE等頂會(huì)頂刊

 

代表性論文（全部列表詳見個(gè)人主頁https://sites.google.com/view/jianlou，其中*代表指導(dǎo)的學(xué)生）：

  • Zhihao Liu*, Jian Lou, et al., “Differentially Private Zeroth-Order Methods for Scalable Large Language Model Fine-tuning", NDSS'25 [CCF-A].
  • with Xiaoyu Zhang, Chenyang Zhang*, Kai Wu, Zilong Wang, Xiaofeng Chen, “DuplexGuard: Safeguarding Deletion Right in Machine Unlearning via Duplex Watermarking", IEEE Transactions on Dependable and Secure Computing, 2024 [CCF-A].
  • Haoyu Tong*, Xiaoyu Zhang, Yulin Jin*, Jian Lou, Kai Wu, Xiaofeng Chen, “Balancing Generalization and Robustness in Adversarial Training via Steering through Clean and Adversarial Gradient Directions", ACM MM'24 [CCF-A].
  • with Jiawen Zhang*, Kejia Chen*, Zunlei Feng, Mingli Song, “SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models", ECAI'24.
  • Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, Xiaofeng Meng, “Cross-silo Federated Learning with Record-level Personalized Differential Privacy", ACM CCS'24 [CCF-A] (Distinguished Paper Award).
  • with Yuke Hu*, Jiaqi Liu*, et al., “ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach", ACM CCS'24 [CCF-A].
  • Wen Yin, Jian Lou, Pan Zhou, Yulai Xie, Dan Feng, Yuhua Sun, Tailai Zhang, Lichao Sun, “Temperature-based Backdoor Attacks on Thermal Infrared Object Detection", CVPR'24 [CCF-A].
  • Qiuchen Zhang, Hong kyu Lee, Jing Ma, Jian Lou, Carl Yang, Li Xiong, “DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy", WWW'24 [CCF-A].
  • Wenjie Wang, Pengfei Tang, Jian Lou, Yuanming Shao, Lance Waller, Yi-an Ko, Li Xiong, “IGAMT: Privacy Preserved Electronic Health Record Synthetic Approach with Heterogeneity and Irregularity", AAAI'24 [CCF-A].
  • Lanlan Chen, Kai Wu, Jian Lou, Jing Liu, “Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics", AAAI'24.
  • with Hongwei Yao*, et al., “PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification", S&P/Oakland'24 [CCF-A].
  • Hongwei Yao*, Jian Lou, Zhan Qin, “PoisonPrompt: Backdoor Attack on Prompt-based Large Language Models", ICASSP'24.
  • Congcong Fu*, Hui Li, Jian Lou, Huizhen, Li, Jiangtao Cui, “DP-starJ: A Differentially Private Scheme towards Analytical Star-Join Queries", SIGMOD'24 [CCF-A].
  • with Jiaqi Liu*, et al., “Certified Minimax Unlearning with Generalization Rates and Deletion Capacity", NeurIPS'23 [CCF-A].
  • with Shuijing Zhang*, Li Xiong, Xiaoyu Zhang, Jing Liu, “Closed-form Machine Unlearning for Matrix Factorization", CIKM'23.
  • Junxu Liu, Jian Lou, Li Xiong, Xiaofeng Meng, “Personalized Differentially Private Federated Learning without Exposing Privacy Budgets", CIKM'23.
  • Yulin Jin*, Xiaoyu Zhang, Jian Lou, Xiaofeng Chen, “ACQ: Few-shot Backdoor Defense via Activation Clipping and Quantizing", ACM MM'23 [CCF-A].
  • with Junxu Liu*, Mingsheng Xue*, Xiaoyu Zhang, Li Xiong, Zhan Qin, “MUter: Machine Unlearning on Adversarial Training Models", ICCV'23 [CCF-A].
  • Yulin Jin*, Xiaoyu Zhang, Jian Lou, Xu Ma, Xiaofeng Chen, Zilong Wang, “Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective", ICCV'23 [CCF-A].
  • Haocheng Xia, Jinfei Liu, Jian Lou, et al., “Equitable Data Valuation Meets the Right to be Forgotten in Model Markets", VLDB'23 [CCF-A].
  • Fereshteh Razmi, Jian Lou, Li Xiong, Yuan Hong, “Interpretation Attacks on Interpretable Models with Electronic Health Records", ECML-PKDD'23.
  • Yiling He*, Jian Lou, et al., “FINER: Enhancing State-of-the-art Classifiers with Feature  Attribution to Facilitate Risk Analysis", ACM CCS'23 [CCF-A].
  • Farnaz Tahmasebian*, Jian Lou, Li Xiong, “RobustFed: A Truth Inference Approach for Robust Federated Learning", CIKM'22.
  • Congcong Fu*, Hui Li, Jian Lou, Jiangtao Cui, “DP-HORUS: Differentially Private Hierarchical Count Histograms under Untrusted Server", CIKM'22.
  • with Xiaoyu Zhang, Yulin Jin*, Tao Wang, Xiaofeng Chen, “Purifier: Plug-and-play Backdoor Mitigation for Pre-trained Models Via Anomaly Activation Suppression", ACM MM'22 [CCF-A].
  • Junxu Liu*, Jian Lou, Li Xiong, Jinfei Liu, Xiaofeng Meng, “Projected Federated Averaging with Heterogeneous Differential Privacy", VLDB'22 [CCF-A].
  • Pengfei Tang*, Wenjie Wang*, Jian Lou, Li Xiong, “Generating Adversarial Examples with Distance Constrained Adversarial Imitation Networks", IEEE Transactions on Dependable and Secure Computing, 2022 [CCF-A].
  • with Haowen Lin*, Li Xiong, Cyrus Shahabi, “Integer-arithmetic-only Certified Robustness for Quantized Neural Networks", ICCV'21 [CCF-A].
  • with Qiuchen Zhang*, Jing Ma*, Li Xiong, “Private Stochastic Non-convex Optimization with Improved Utility Rates", IJCAI'21 [CCF-A]. 
  • with Wenjie Wang*, Pengfei Tang*, Li Xiong, “Certified Robustness to Word Substitution Attack with Differential Privacy", NAACL'21.
  • with Jing Ma*, Qiuchen Zhang*, Li Xiong, Joyce Ho, “Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics", WWW'21 [CCF-A].
  • Jinfei Liu, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, Jimeng Sun, “Dealer: An End-to-End Model Marketplace with Differential Privacy", VLDB'21 [CCF-A].
  • Jing Ma*, Qiuchen Zhang*, Jian Lou, Li Xiong, Joyce Ho, Sivasubramanium Bhavani, “Communication Efficient Tensor Factorization for Decentralized Healthcare Networks", ICDM'21.
  • Jing Ma*, Qiuchen Zhang*, Jian Lou, Li Xiong, Joyce Ho, “Temporal Network Embedding via Tensor Factorization", CIKM'21.
  • with Yiu-ming Cheung, “An Uplink Communication Efficient Approach to Feature-wise Distributed Sparse Optimization with Differential Privacy”, IEEE Transactions on Neural Networks and Learning Systems, 2021.
  • with Yiu-ming Cheung, “Projection-free Online Empirical Risk Minimization with Privacy-preserving and Privacy Expiration", WI-IAT'20 (Best in Theoretical Paper Award).
  • with Yifei Ren*, Li Xiong, Joyce Ho, “Robust Irregular Tensor Factorization and Completion for Temporal Health Data Analysis", CIKM'20.
  • with Yiu-ming Cheung, “Robust Low-rank Tensor Minimization via a New Tensor Spectral k-Support Norm”, IEEE Transactions on Image Processing, 2020 [CCF-A].
  • Jing Ma*, Qiuchen Zhang*, Jian Lou, Joyce Ho, Li Xiong, Xiaoqian Jiang,“Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis", CIKM'19.
  • with Yiu-ming Cheung, "Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data", AAAI'18 [CCF-A].
  • with Yiu-ming Cheung, “Proximal Average Approximated Incremental Gradient Descent for Composite Penalty Regularized Empirical Risk Minimization”, Machine Learning, 2017.
  • with Yiu-ming Cheung, “Scalable Spectral k-Support Norm Regularization for Robust Low Rank Subspace Learning", CIKM'16.
  • with Yiu-ming Cheung, “Efficient Generalized Conditional Gradient with Gradient Sliding for Composite Optimization", IJCAI'15 [CCF-A].
  • with Yiu-ming Cheung, “Proximal Average Approximated Incremental Gradient Method for Composite Penalty Regularized Empirical Risk Minimization", ACML'15.