David Bau

(he/him/his)

Assistant Professor

David Bau

Research interests

  • Machine learning 
  • Computer vision 
  • Artificial intelligence 
  • Natural language processing 
  • Human–computer interaction 

Education

  • PhD in Computer Science, Massachusetts Institute of Technology 
  • MS in Computer Science, Cornell University 
  • AB in Mathematics, Harvard University 

Biography

David Bau is an assistant professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.

Bau's research focuses on human-computer interaction and machine learning. Before joining Northeastern, he worked as a software engineer at Google, BEA, and Crossgain. He has been published in journals such as CVPR, NeurIPS, ICCV, ECCV, and SIGGRAPH.  

 Outside of research, Bau enjoys astronomy and puzzle collecting.   

Projects

Recent publications

  • Agents of Chaos

    Citation: Natalie Shapira, Chris Wendler, Avery Yen, Gabriele Sarti, Koyena Pal, Olivia Floody, Adam Belfki, Alexander R. Loftus, Aditya Ratan Jannali, Nikhil Prakash, Jasmine Cui, Giordano Rogers, Jannik Brinkmann, Can Rager, Amir Zur, Michael Ripa, Aruna Sankaranarayanan, David Atkinson, Rohit Gandikota, Jaden Fiotto-Kaufman, EunJeong Hwang, Hadas Orgad, P. Sam Sahil, Negev Taglicht, Tomer Shabtay, Atai Ambus, Nitay Alon, Shiri Oron, Ayelet Gordon-Tapiero, Yotam Kaplan, Vered Shwartz, Tamar Rott Shaham, Christoph Riedl, Reuth Mirsky, Maarten Sap, David Manheim, Tomer Ullman, David Bau. (2026). Agents of Chaos CoRR, abs/2602.20021. https://doi.org/10.48550/arXiv.2602.20021
  • Position-aware Automatic Circuit Discovery

    Citation: Tal Haklay, Hadas Orgad, David Bau, Aaron Mueller, Yonatan Belinkov. (2025). Position-aware Automatic Circuit Discovery ACL (1), 2792-2817. https://aclanthology.org/2025.acl-long.141/
  • Language Models use Lookbacks to Track Beliefs

    Citation: Nikhil Prakash, Natalie Shapira, Arnab Sen Sharma, Christoph Riedl, Yonatan Belinkov, Tamar Rott Shaham, David Bau, Atticus Geiger. (2025). Language Models use Lookbacks to Track Beliefs CoRR, abs/2505.14685. https://doi.org/10.48550/arXiv.2505.14685
  • MIB: A Mechanistic Interpretability Benchmark

    Citation: Aaron Mueller, Atticus Geiger, Sarah Wiegreffe, Dana Arad, Iván Arcuschin, Adam Belfki, Yik Siu Chan, Jaden Fried Fiotto-Kaufman, Tal Haklay, Michael Hanna , Jing Huang, Rohan Gupta, Yaniv Nikankin, Hadas Orgad, Nikhil Prakash, Anja Reusch, Aruna Sankaranarayanan, Shun Shao, Alessandro Stolfo, Martin Tutek, Amir Zur, David Bau, Yonatan Belinkov. (2025). MIB: A Mechanistic Interpretability Benchmark ICML. https://openreview.net/forum?id=sSrOwve6vb
  • NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals

    Citation: Jaden Fried Fiotto-Kaufman, Alexander Russell Loftus, Eric Todd, Jannik Brinkmann, Koyena Pal, Dmitrii Troitskii, Michael Ripa, Adam Belfki, Can Rager, Caden Juang, Aaron Mueller, Samuel Marks, Arnab Sen Sharma, Francesca Lucchetti, Nikhil Prakash, Carla E. Brodley, Arjun Guha, Jonathan Bell , Byron C. Wallace, David Bau. (2025). NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals ICLR. https://openreview.net/forum?id=MxbEiFRf39
  • Linearity of Relation Decoding in Transformer Language Models

    Citation: Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, David Bau. (2024). Linearity of Relation Decoding in Transformer Language Models ICLR. https://openreview.net/forum?id=w7LU2s14kE
  • Function Vectors in Large Language Models

    Citation: Eric Todd, Millicent L. Li, Arnab Sen Sharma, Aaron Mueller, Byron C. Wallace, David Bau. (2024). Function Vectors in Large Language Models ICLR. https://openreview.net/forum?id=AwyxtyMwaG
  • Erasing Concepts from Diffusion Models

    Citation: Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, David Bau. (2023). Erasing Concepts from Diffusion Models ICCV, 2426-2436. https://doi.org/10.1109/ICCV51070.2023.00230
  • FIND: A Function Description Benchmark for Evaluating Interpretability Methods

    Citation: Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba . (2023). FIND: A Function Description Benchmark for Evaluating Interpretability Methods NeurIPS. http://papers.nips.cc/paper_files/paper/2023/hash/ef0164c1112f56246224af540857348f-Abstract-Datasets_and_Benchmarks.html
  • Mass-Editing Memory in a Transformer

    Citation: Kevin Meng, Arnab Sen Sharma, Alex J. Andonian, Yonatan Belinkov, David Bau. (2023). Mass-Editing Memory in a Transformer ICLR. https://openreview.net/pdf?id=MkbcAHIYgyS
  • Toward a Visual Concept Vocabulary for GAN Latent Space

    Citation: Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, Antonio Torralba . (2021). Toward a Visual Concept Vocabulary for GAN Latent Space ICCV, 6784-6792. https://doi.org/10.1109/ICCV48922.2021.00673
  • Disentangling visual and written concepts in CLIP

    Citation: Joanna Materzynska, Antonio Torralba , David Bau. (2022). Disentangling visual and written concepts in CLIP CVPR, 16389-16398. https://doi.org/10.1109/CVPR52688.2022.01592
  • Sketch Your Own GAN

    Citation: Sheng-Yu Wang, David Bau, and Jun-Yan Zhu. Sketch Your Own GAN. Proceedings of the IEEE/CVF International Conference on Computer Vision. (ICCV 2021)
  • Diverse Image Generation via Self-Conditioned GANs

    Citation: Steven Liu, Tongzhou Wang , David Bau, Jun-Yan Zhu, Antonio Torralba . (2020). Diverse Image Generation via Self-Conditioned GANs CVPR, 14274-14283. https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Diverse_Image_Generation_via_Self-Conditioned_GANs_CVPR_2020_paper.html

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