1. Home ›
  2. News

News

Sep 2024

  • We launch the IEEE CS North America Student Challenge 2024 today. Welcome to participate the competition on Inferring User Latent Preference from Conversations with LLM, and win the First Prize: $2,500; Second Prize: $1,500; Third Prize: $500. It will end soon on Oct 21, 2024.
  • Congratulations to Kehan, Yue Huang, Yujun, Ziyi and Tianyu, for their NeurIPS’24 and EMNLP’24 papers! We have made new progress on LLM trustworthiness and their applications to chemistry. Check out these amazing work:
    • Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation. Accepted by NeurIPS 2024 Datasets and Benchmarks Track as a spotlight.
    • The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. Accepted by NeurIPS 2024. arXix Link.
    • 1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? Accepted by EMNLP 2024 Main. arXiv link.
    • Defending Jailbreak Prompts via In-Context Adversarial Game. Accepted by EMNLP 2024 Main. arXiv link.
    • RAt: Injecting Implicit Bias for Text-To-Image Prompt Refinement Models. Accepted by EMNLP 2024 Main.
    • SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering. Accepted by EMNLP 2024 Findings.

May 2024

Oct 2023

Sep 2023

Aug 2023

  • PI Dr. Zhang is awarded an NSF grant (1 million US dollars) for the research project of Cybertraining for Chemical Data scientists, abbreviated as C2D.
  • Call for papers!
    • Dr. Zhang is the co-chair of WSDM’24 Demo (Due: Sep 24, 2023). Accepted papers will be included in the WSDM’24 proceedings.
    • Dr. Zhang is the co-chair of the User Modeling and Recommendation track at The Web Conference 2024. (Due: Oct 5 (abstract), Oct 12 (full-paper), 2023)
    • SIGKDD Explorations (the December 2023 issue is accepting submission of surveys, news, opinion papers, vision papers and so on).

Nov 2022

  • We have 5 papers accepted by AAAI 2023! Congratulations to Zhenwen, Qiannan, Hongyan, Xiuying and Xiaoting! These papers present our recent study of data-efficient learning from graphs, trustworthy machine learning and natural language understanding. More can be found here.

Aug 2022

  • NSF Center for Computer-Assisted Synthesis led by Prof. Olaf Wiest is funded! In the 5-year project, PI Dr. Zhang’s team will work on designing novel machine learning models for chemical research problems.
  • We have another molecular generation paper got accepted by CIKM 2022. It attempts to build an understandable embedding space for molecular generation.

Aug 2022

  • We have two papers about Recommendation Systems got accepted by CIKM 2022.
    • One designs a novel zero-shot learning model for cold-start news recommendation.
    • The other investigates the negative sampling issue in the optimization process of recommendation systems, and proposes a fast dynamic negative sampler for the personalized ranking task.
    • More of our recent work in this topic can be found at Recommendation Systems.
  • Dr. Zhang is appointed as the Editor-in-Chief of ACM SIGKDD Explorations. Welcome submissions!

May 2022

  • Our work of Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer is accepted by KDD 2022. Anther few-shot learning on graphs survey paper will appear in IJCAI 2022
  • Our work of applying PU-learning on graph completion, Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion will also appear in IJCAI 2022
  • More of our recent work on graph learning can be found at Learning with Graphs.

January 2022

  • Our work of Understanding the Robustness Against Evasion Attack on Categorical Data is accepted by ICLR 2022. More of our work in this topic can be found at Robust and Trustworthy Machine Learning
  • Our work of Graph Alignment with Noisy Supervision is accepted by TheWebConf 2022. A related work of handling noisy labels in knowledge graph alignment can be found in our KDD 2020 paper, entitled REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs.

December 2021

  • Our work of Self-Augmented Graph Contrastive Learning is accepted by AAAI 2022. More of our graph learning work can be found at Learning with Graphs/Networks
  • Our work of GNN-Retro: Retrosynthetic planning with Graph Neural Networks is accepted by AAAI 2022. This paper uses GNN to predict the synthesis routes for target molecules. Another related work of molecular graph generation can be found in our CIKM 2021 paper, entitled GF-VAE: A Flow-based Variational Autoencoder for Molecule Generation.

August 2021

Prof. Zhang joined the department of Computer Science and Engineering at University of Notre Dame in August 2021.