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  1. Wei Wang is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles and the director of the Scalable Analytics Institute (ScAi).

  2. Wei Wang, Ph. D. Leonard Kleinrock Professor Computer Science Department University of California, Los Angeles Los Angeles, CA 90095-1596 Voice: (310) 794-0009 E-mail: weiwang@cs.ucla.edu URL: http://www.cs.ucla.edu/~weiwang/. RESEARCH INTEREST. Big Data Analytics, Data Mining, Machine Learning, Natural Language Processing, Bioinformatics and ...

  3. Research: Scalable Graph Representation Learning via Locality Sensitive Hashing, by Xiusi Chen, Jyun-Yu Jiang, and Wei Wang, Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022.

  4. Tanmay Parekh, Jeffrey Kwan, Jiarui Yu, Sparsh Johri, Hyosang Ahn, Sreya Muppalla, Kai-Wei Chang, Wei Wang, and Nanyun Peng, in EMNLP, 2024. Abstract

  5. Graph neural networks (GNNs) have been demonstrated to besuccessful in modeling graph-structured data. However, training GNNs requires abundant task-specific labeled data, which is often arduously expensive to obtain.

  6. Dec 4, 2018 · In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables –such as gender– in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased datasets. Surprisingly, we show ...

  7. Muhao Chen: PhD 2019 (informally co-advised with Carlo Zaniolo and Wei Wang)-> Postdoc at UPenn -> Assitant Professor at UC Davis Industry . Masoud Monajatipoor: PhD 2024 (Co-Adivsed with Yang) -> Optum Labs Harold (Liunian) Li: PhD 2024 -> OpenAI Tao Meng: PhD 2024 -> Zoom Wasi Uddin Ahmad: PhD 2021 -> Amazon

  8. Fei Wang, Ninareh Mehrabi, Palash Goyal, Rahul Gupta, Kai-Wei Chang, and Aram Galstyan, in EMNLP, 2024. Abstract

  9. Jun 24, 2019 · Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent.

  10. Kewei Cheng, Jiahao Liu, Wei Wang, Yizhou Sun, "RLogic: Recursive Logical Rule Learning from Knowledge Graphs," in Proc. of 2022 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’22), Washington, DC, Aug. 2022.