Lu Lin (林璐)

:wave: Bio: Hi, I’m Lu. I am currently a tenure-track assistant professor in the College of Information Sciences and Technology, Penn State University starting 2022 Fall. Prior to that, I received my Ph.D. in Computer Science at University of Virginia in 2022, under supervision of Dr. Hongning Wang. I recieved my M.s. and B.S. in 2017 and 2014 respectively, from Computer Science at Beihang University. I have also interned at Didi Lab, LinkedIn and Pinterest Lab.

:pushpin: Research interests: I have a broad interest in machine learning and data mining. My current focus is practical and trustworthy machine learning, especially on graph-structured data, concerning multiple aspects including robustness, fairness, interpretability and privacy. To be more specific:

  • machine learning on graphs, e.g. graph neural networks, graph representation learning
  • trustworthy machine learning, w.r.t. adversarial robustness, fairness, interpretability, privacy
  • data mining and data science in real-world applications

:fire: Openings for 2023-2024: I’m looking for highly motivated students, including PhDs (fully-funded), Masters, undergraduates, and interns to join my group. Please kindly read Open Position for more information before contact me.

News

Apr 23 :pushpin: Our work is accepted by ICML 2023 on Graph Contrastive Backdoor Attack .
Apr 23 :pushpin: Our work is accepted by ICML 2023 on Molecule In-Context Learning for Retrosynthetic Planning.
Jan 23 :pushpin: Our work is accepted by ICLR 2023 on Spectral Augmentation for Self-Supervised Learning on Graphs.
Sep 22 :rocket: I am officially on board as a tenure-track faculty at IST@PSU. Multiple open positions available!
Jun 22 :mortar_board: I passed my dissertation defense!
May 22 :pushpin: Our work is accepted by KDD 2022 on Graph Attack by Perturbing Spectrum.
May 22 :pushpin: Our work is accepted by ICML 2022 on communication-efficient adaptive federated learning.
May 22 :rocket: I am honored to receive CS John A. Stankovic Graduate Research Award from UVa
Feb 22 :microphone: I gave a talk on Graph Structure as A Double-Edged Sword in Machine Learning at IST@PSU.
Feb 22 :microphone: I gave a talk on Graph Structure as A Double-Edged Sword in Machine Learning at DS@NJIT.

Selected Publications [full list]

  1. ICML
    FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
    Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, and Dinghao Wu
    Proceedings of the 40th International Conference on Machine Learning, ICML 2023
  2. ICML
    Graph Contrastive Backdoor Attacks
    Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia, and Dinghao Wu
    Proceedings of the 40th International Conference on Machine Learning, ICML 2023
  3. ICLR
    Spectral augmentation for self-supervised learning on graphs
    Lu Lin, Jinghui Chen, and Hongning Wang
    Proceedings of the 11th International Conference on Learning Representations, ICLR 2023
  4. KDD
    Graph Structural Attack by Perturbing Spectral Distance
    Lu Lin, Ethan Blaser, and Hongning Wang
    Proceedings of the 28th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2022
  5. ICML
    Communication-Efficient Adaptive Federated Learning
    Yujia Wang, Lu Lin, and Jinghui Chen
    Proceedings of the 39th International Conference on Machine Learning, ICML 2022
  6. AISTATS
    Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
    Yujia Wang, Lu Lin, and Jinghui Chen
    Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022
  7. WWW
    Unbiased Graph Embedding with Biased Graph Observations
    Nan Wang*, Lu Lin*, Jundong Li, and Hongning Wang
    Proceedings of the Web Conference, WWW 2022
  8. KDD
    Graph Attention Networks over Edge Content-based Channels
    Lu Lin, and Hongning Wang
    In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2020
  9. TKDE
    Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-source Data
    Lu Lin, Jianxin Li, Feng Chen, Jieping Ye, and Jinpeng Huai
    IEEE Transactions on Knowledge and Data Engineering, TKDE 2017