Please check our recently released survey paper on Knowledge Editing in Large Language Models #LLM arxiv.org/abs/2310.16218. A big shoutout to my amazing students 👏

Check out our latest work with the LinkedIn team on using #LLM for recommendation😀. Led by my amazing student Yaochen Zhu @ ICLR 2025

Check out our recently released PyGDebias package that integrates the SOTA fairness and bias mitigation algorithms in graph machine learning, along with the benchmark datasets. github.com/yushundong/PyG… #graphlearning #fairness #bias

I will be attending #NeurIPS2023 from Dec 11 to Dec 12. Look forward to meet old and new friends. DM if you would like to catch up

🌟Glad to share that our paper "Adversarial Attacks on Fairness of Graph Neural Networks" has been accepted at #ICLR2024! We proposed G-FairAttack to jeopardize the fairness of different types of GNNs with an unnoticeable effect on the utility. 🔗Paper: openreview.net/forum?id=q3KNr…

🔍 We are thrilled to invite PhD students to submit your research to the SIAM SDM Doctoral Forum's poster session and gain invaluable insights from academic and industry leaders. Apply by March 10, 2024. Application forms.gle/A2aCZieHUoGBqa… #PhD #SIAMSDM24

Check out our recent paper: Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era

Glad to share that we have two papers on Machine Unlearning accepted to #ICML2024! One is about certified unlearning for DNNs and the other is about vulnerabilities in the verification of machine unlearning. Kudos to all brilliant coauthors! Stay tuned for the final versions…

I will be presenting our poster on “Adversarial Attacks on Fairness of Graph Neural Networks” in Poster Session 8, Friday 4:30 pm CEST. Stop by to have a chat or just say hi! #ICLR2024 Poster: Halle B # 196 Arxiv: arxiv.org/abs/2310.13822

Congrats to our 2024 Outstanding Ph.D. Student Award winners! The award recognizes excellence in service, teaching, research or leadership. Read more about their achievements here: at.virginia.edu/tgwhiq Virginia BME UVA Computer Science

Humbled and grateful to receive the #SIGKDD Rising Star Award this year in Barcelona! Deepest thanks to my students, advisors, mentors, colleagues, friends, and family for their unwavering support and encouragement.

Honored to see our work mentioned by such a prominent expert in the field! Causal inference in LLMs is undoubtedly an area that deserves greater attention. Welcome to check out our survey arxiv.org/pdf/2409.09822

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction • LLM-GCE introduces a novel framework that leverages Large Language Models (LLMs) to enhance the explainability of Graph Neural Networks (GNNs) in

Thrilled to share our new survey paper, **Harnessing Large Language Models for Disaster Management: A Survey** arxiv.org/abs/2501.06932 - We systematically introduce the current explorations handling disaster management with LLMs across four disaster phases.

How can Large Language Models revolutionize #DisasterManagement? Our new survey explores their potential in scenarios like the recent LA wildfires—enhancing predictions, coordination, and emergency responses. arxiv.org/pdf/2501.06932 #AIforGood #Wildfires #ClimateCrisis #LLM

🚨 PhD students in AI/Data Science: submit to the KDD 2025 PhD Consortium! Get feedback, mentorship & connect with top researchers. 📝 Light submission 📅 Deadline: May 15 (two days away) 🔗 kdd2025.kdd.org/call-for-phd-c… Co-organized by Arijit K & me. #KDD2025 #PhDLife #AIResearch