Yatao Bian

BlueWhaleLab

Hi, this is Yatao. I am an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), and a Fellow of National Research Foundation, Singapore. I am also affiliated with the NUS AI Institute. I received my Doctor of Sciences degree from the Institute for Machine Learning at ETH Zürich Computer Science. I was lucky to be supervised by Prof. Joachim M. Buhmann and also worked closely with Prof. Andreas Krause (PhD co-examiner). My dissertation committee featured Prof. Yisong Yue and was chaired by Prof. Martin Vechev. Before that I obtained both of my M.Sc.Eng. and B.Sc.Eng. from Shanghai Jiao Tong University.

I am dedicated to expanding the frontiers of AI capabilities in scientific intelligence, driving transformative technological advancements to address the most critical challenges in science and society. Some of my research topics include: reasoning-empowered scientific foundation models, modern entropy/energy based learning (e.g., energy based reasoning/guidance/refinement), LLM/Agent for science, graph machine learning and neural sets.

I like various sports, e.g., swimming, hiking & skiing. Here's a fun fact about me: I had an official name change while I was pursuing my PhD. For a time, my passport name was Andrew An Bian, which is a name I don't use anymore. The change was due to a personal family matter.

Contact:         X@yataobian   Rednote(小红书)  WeChatOfficialAccount(公众号)
Research:     Google Scholar     ORCID
Biography:    Short Bio

Prospective Students: Please check the "Job Openings" session on this page, fill the Google Form and contact **this** email:

Research Focus

My research centers on AI capability-driven basic research, structured along two deeply interconnected and synergistic lines of inquiry:
- Sci4AI: Advancing AI with Science. AI's progress has long been nurtured by scientific disciplines—for example, the Boltzmann machine rooted in statistical physics. I am dedicated to developing principled AI methodologies grounded in fundamental mathematical and scientific theories, aiming to push the frontiers of AI capabilities.
- AI4Sci: Advancing Science with AI. I am committed to leveraging AI's transformative potential to accelerate discovery and address critical challenges in science and society. My work focuses on developing AI4Sci toolkits that are not only highly accurate and robust but are also increasingly endowed with advanced reasoning capabilities, enabling new modes of scientific inquiry.

Job Openings

I am looking for highly motivated PhDs, Postdocs, Research Fellows, and vistiting students (e.g., CSC students). Here is a recent job description (in English) and job description (in Chinese). To gain a better understanding of the PhD program, I highly recommend watching the presentation by Prof Silvija Gradecak-Garaj: Pursuing PhD from a research perspective.

◽ Google Form: To help me learn about your aspirations and background, please fill out this Google Form *before* sending an email (to ). This will allow me to give your application the attention it deserves. I sincerely appreciate your understanding and cooperation in this matter.

◽ Prospective Ph.D. students (2027 Spring, 2027 Fall, 2028 Spring, 2028 Fall): please apply through the NUS Graduate Admission System: https://gradapp.nus.edu.sg/portal/app_manage. More info: https://www.comp.nus.edu.sg/programmes/pg/phdcs/admissions/.

◽ You can follow my rednote(小红书)@bluewhalelab or Twitter X@yataobian to get the latest news/guidelines.

Selected Awards/Honours/Achievements

Selected Talks

Blue Whale Lab (BWLab)

Vision and Mission

• The BlueWhaleLab, established in 2020 as an internal research team, was born from my passion for unlocking AI’s potential to tackle some of science and society’s most pressing challenges—alongside a deep eagerness to collaborate with like-minded individuals in realizing this vision.
• Why is it called BlueWhaleLab? The blue whale is the largest mammal on Earth, possessing immense potential and capabilities, much like AI with its limitless possibilities. Additionally, the blue whale is one of my favorite animals, with its unique charm and intelligence. It embodies freedom and the pursuit of the stars and the ocean.

Alumni@BlueWhaleLab

Academic Services

Conference Reviewing/PC/Chair

• Area Chair of ICML 2026, ICLR 2026, NeurIPS 2025, ICLR 2025
• Session Chair of ICLR 2025
• ICML 2025, NeurIPS 2024, ICML 2024, NeurIPS 2023, ICLR 2023, NeurIPS 2022, ICML 2022, ICLR 2022, CVPR 2022, AAAI 2022, AISTATS 2022, NeurIPS 2021, ICCV 2021, CVPR 2021, AAAI 2021, NeurIPS 2020, AAAI 2020, NeurIPS 2019, ICML 2019, AISTATS 2019, STOC 2018, ITCS 2017, NIPS 2016

Journal Reviewing

• Nature, JMLR, T-PAMI

Tutorials @Top Conferences

• Co-tutoring a tutorial on Trustworthy Graph Learning at KDD 2022: Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.
• Co-tutoring a tutorial on Advanced Deep Graph Learning at WWW 2021: Advanced Deep Graph Learning: Deeper, Faster, Robuster, and Unsupervised.

Research Experience

Selected Papers & Code (Not Updated, see the [Full List])

• Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
  ICLR 2022. Bian, Y. Rong, T. Xu, J. Wu, A. Krause, J. Huang [pdf] [project page] [video] [poster] [slides] [bib]

      @inproceedings{bian2022energybased,
      title={Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning},
      author={Yatao Bian and Yu Rong and Tingyang Xu and Jiaxiang Wu and Andreas Krause and Junzhou Huang},
      booktitle={International Conference on Learning Representations},
      year={2022},
      url={https://openreview.net/forum?id=xLfAgCroImw}
      }
    

• Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
  ICLR 2022 Spotlight. O. Ganea, X. Huang, C. Bunne, Bian, R. Barzilay, T. Jaakkola, A. Krause [pdf] [code] [bib]

      @inproceedings{ganea2022independent,
      title={Independent {SE}(3)-Equivariant Models for End-to-End Rigid Protein Docking},
      author={Octavian-Eugen Ganea and Xinyuan Huang and Charlotte Bunne and Yatao Bian and Regina Barzilay and Tommi S. Jaakkola and Andreas Krause},
      booktitle={International Conference on Learning Representations},
      year={2022},
      url={https://openreview.net/forum?id=GQjaI9mLet}
      }
    

• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery--A Focus on Affinity Prediction Problems with Noise Annotations
  Preprint 2022. Ji*, Zhang*, Wu, Wu, Huang, Xu, Rong, Li, Ren, Xue, Lai, Xu, Feng, Liu, Luo, Zhou, Huang, Zhao, Bian [pdf] [project page] [bib]

        @ARTICLE{2022arXiv220109637J,
         author = {{Ji}, Yuanfeng and {Zhang}, Lu and {Wu}, Jiaxiang and {Wu}, Bingzhe and {Huang}, Long-Kai and {Xu}, Tingyang and {Rong}, Yu and {Li}, Lanqing and {Ren}, Jie and {Xue}, Ding and {Lai}, Houtim and {Xu}, Shaoyong and {Feng}, Jing and {Liu}, Wei and {Luo}, Ping and {Zhou}, Shuigeng and {Huang}, Junzhou and {Zhao}, Peilin and {Bian}, Yatao},
          title = "{DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations}",
        journal = {arXiv e-prints},
       keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Quantitative Methods},
           year = 2022,
          month = jan,
            eid = {arXiv:2201.09637},
          pages = {arXiv:2201.09637},
  archivePrefix = {arXiv},
         eprint = {2201.09637},
   primaryClass = {cs.LG},
         adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220109637J},
        adsnote = {Provided by the SAO/NASA Astrophysics Data System}
            }
      

• On Self-Distilling Graph Neural Network
  IJCAI 2021. Chen, Bian, Xiao, Rong, Xu, J. Huang [pdf]
• Graph Information Bottleneck for Subgraph Recognition
  ICLR 2021. Yu, Xu, Rong, Bian, J. Huang, Ran [pdf]
• Self-Supervised Graph Transformer on Large-Scale Molecular Data
  NeurIPS 2020. Rong*, Bian*, Xu, Xie, Wei, W. Huang, J. Huang [pdf] [code] [video] [bib]

• From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
  ICML 2020. Sahin, Bian, Buhmann, Krause [pdf] [bib]

        @inproceedings{sahin2020sets,
          title={From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models},
          author={Sahin, Aytunc and Bian, Yatao and Buhmann, Joachim M and Krause, Andreas},
          booktitle={Proceedings of the 37th International Conference on Machine Learning},
          year={2020},
          publisher={PMLR},
        }
      

• Provable Non-Convex Optimization and Algorithm Validation via Submodularity
  Doctoral thesis, ETH Zurich. Bian [pdf] [slides] [bib]

      @phdthesis{bian2019provable,
        title={Provable Non-Convex Optimization and Algorithm Validation via Submodularity},
        author={Bian, Yatao An},
        year={2019},
        school={ETH Zurich}
      }
    

• Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
  ICML 2019. Bian, Buhmann, Krause [pdf] [code] [poster] [bib]

      @inproceedings{bian2019optimalmeanfield,
        title={Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference},
        author={Bian, Yatao A. and Buhmann, Joachim M. and Krause, Andreas},
        booktitle={Proceedings of the 36th International Conference on Machine Learning},
        pages={644--653},
        year={2019},
        volume={97},
        series={Proceedings of Machine Learning Research},
        address={Long Beach, California, USA},
        month={09--15 Jun},
        publisher={PMLR},
      }
    

• CoLA: Decentralized Linear Learning
  NeurIPS 2018. He*, Bian*, Jaggi [arXiv] [code] [poster] [bib]

      @inproceedings{he2018cola,
      title={COLA: Communication-Efficient Decentralized Linear Learning},
      author={He, Lie and Bian, An and Jaggi, Martin},
      booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
      pages = {4537--4547},
      year={2018}
      }
      

• A Distributed Second-Order Algorithm You Can Trust
  ICML 2018. Duenner, Lucchi, Gargiani, Bian, Hofmann, Jaggi [paper] [bib]

    @inproceedings{Celestine2018trust,
      title={A Distributed Second-Order Algorithm You Can Trust},
      author={D{\"u}nner, Celestine and Lucchi, Aurelien and Gargiani, Matilde and Bian, An and Hofmann, Thomas and Jaggi, Martin},
      booktitle={ICML},
      pages={1357--1365},
      year={2018}
    }
    

• Continuous DR-submodular Maximization: Structure and Algorithms
  NIPS 2017. Bian, Levy, Krause, Buhmann [arXiv] [code] [poster] [video] [bib]

  @inproceedings{biannips2017nonmonotone,
    title={Continuous DR-submodular Maximization: Structure and Algorithms},
    author={Bian, An and Levy, Kfir Y. and Krause, Andreas and Buhmann, Joachim M.},
    booktitle={Advances in Neural Information Processing Systems (NIPS)},
    pages={486--496},
    year={2017}
  }
  

• Guarantees for Greedy Maximization of Non-submodular Functions with Applications
  ICML 2017. Bian, Buhmann, Krause, Tschiatschek [arXiv] [code] [poster] [bib]

  @inproceedings{bianicml2017guarantees,
    title={Guarantees for Greedy Maximization of Non-submodular Functions with Applications},
    author={Bian, Andrew An and Buhmann, Joachim M. and Krause, Andreas and Tschiatschek, Sebastian},
    booktitle={International Conference on Machine Learning (ICML)},
    pages={498--507},
    year={2017}
  }
  

• Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
  AISTATS 2017. Bian, Mirzasoleiman, Buhmann, Krause [full version][introductory post] [poster] [bib]

  @inproceedings{bian2017guaranteed,
    title={Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains},
    author={Bian, Andrew An and Mirzasoleiman, Baharan and Buhmann, Joachim M. and Krause, Andreas},
    booktitle={International Conference on Artificial Intelligence and Statistics (AISTATS)},
    pages={111--120},
    year={2017}
  }
  

• Model Selection for Gaussian Process Regression
  GCPR 2017. *Gorbach, *Bian, Fischer, Bauer, Buhmann [paper] [bib]

  @inproceedings{gorbach2017model,
    title={Model Selection for Gaussian Process Regression},
    author={Gorbach, Nico S and Bian, Andrew An and Fischer, Benjamin and Bauer, Stefan and Buhmann, Joachim M},
    booktitle={German Conference on Pattern Recognition},
    pages={306--318},
    year={2017}
  }
  

• Information-Theoretic Analysis of MAXCUT Algorithms
  ITA 2016. Bian, Gronskiy, Buhmann [pdf] [bib]

  @inproceedings{bian2016information,
      title={Information-theoretic analysis of MaxCut algorithms},
      author={Bian, Yatao and Gronskiy, Alexey and Buhmann, Joachim M},
      booktitle={IEEE Information Theory and Applications Workshop (ITA)},
      pages={1--5},
      url={http://people.inf.ethz.ch/ybian/docs/pa.pdf},
      year={2016}
  }
  

• Greedy MAXCUT Algorithms and their Information Content
  ITW 2015. Bian, Gronskiy, Buhmann [short version] [full version][slides][bib]

  @inproceedings{ITW15_BianGB,
    author    = {Yatao Bian and Alexey Gronskiy and Joachim M. Buhmann},
    title     = {Greedy MaxCut algorithms and their information content},
    booktitle = {IEEE Information Theory Workshop (ITW) 2015, Jerusalem, Israel},
    pages     = {1--5},
    year      = {2015}
  }
  

• Parallel Coordinate Descent Newton for Efficient L1-Regularized Minimization
  Technical report 2013. Bian, Li, Liu, Yang [pdf][code][bib]

  @article{bian2013parallel,
    title={Parallel Coordinate Descent Newton Method for Efficient L1-Regularized Minimization},
    author={Bian, An and Li, Xiong and Liu, Yuncai and Yang, Ming-Hsuan},
    journal={arXiv preprint arXiv:1306.4080},
    year={2013}
  }
  

• Bundle CDN: A Highly Parallelized Approach for Large-scale L1-regularized Logistic Regression
  ECML 2013. Bian, Li, Cao, Liu [pdf][bib]

  @inproceedings{ecmlBian13,
    author    = {Yatao Bian and Xiong Li and Mingqi Cao and Yuncai Liu},
    title     = {Bundle CDN: A Highly Parallelized Approach for Large-Scale L1-Regularized
      Logistic Regression},
    booktitle = {ECML/PKDD},
    year      = {2013},
    pages     = {81-95}
  }
  

• Parallelized Annealed Particle Filter for Real-Time Marker-Less Motion Tracking Via Heterogeneous Computing
  ICPR 2012. Bian, Zhao, Song, Liu [pdf][ bib]

  @inproceedings{icprbian12,
    author    = {Yatao Bian and Xu Zhao and Jian Song and Yuncai Liu},
    title     = {Parallelized Annealed Particle Filter for real-time marker-less motion tracking via heterogeneous computing},
    booktitle = {ICPR},
    year      = {2012},
    pages     = {2444-2447}
  }
  

• Digitize Your Body and Action in 3-D at Over 10 FPS: Real Time Dense Voxel Reconstruction and Marker-less Motion Tracking via GPU Acceleration
  Champion technical report of AMD China Accelerated Computing Contest, 2011 [demo].
  Song*, Bian*, Yan, Zhao, Liu [pdf][bib]

  @article{songbian2013digitize,
    title={Digitize Your Body and Action in 3-D at Over 10 FPS: Real Time Dense Voxel
      Reconstruction and Marker-less Motion Tracking via GPU Acceleration},
    author={Song, Jian and Bian, Yatao and Yan, Junchi and Zhao, Xu and Liu, Yuncai},
    journal={arXiv preprint arXiv:1311.6811},
    year={2013}
  }
  

Research Projects (to be updated)

Ongoing Projects:

Previous Projects:

Links