[Curriculum Vitae]; [Google Scholar]; beibin.li at microsoft.com;
Cloud Operation Research (CORE), Microsoft Research, Redmond, WA
Before joining MSR, I was a Ph.D. candidate at Paul G. Allen School of Computer Science and Engineering, the University of Washington, advised by Linda Shapiro and Frederick Shic. My Ph.D. dissertation focuses on Low-Resource Neural Adaptation: A Unified Data Adaptation Framework for Neural Networks, where we adapt deep learning models for histopathological images, eye tracking models, autism behavior analyses, and database optimization.
Li, B.; Lu, Y.; Kandula, S.
In 2022 ACM Management of Data (SIGMOD).
[PDF] [Page] [Talk Slides PDF] [Talk Video]
Nofallah, S.; Li, B.; Mokhtari, M.; Wu, W.; Knezevich, S.; May, C. J.; Chang, O. H.; Elmore, J.; Shapiro, L.
In Diagnostics, 2022
Atyabi, A.; Shic, F.; Jiang, J.; Foster, C.E.; Barney, E.; Kim, M.; Li, B.; Ventola, P.; Chen, C.H..
In 2022 ACM Transactions on Knowledge Discovery from Data (TKDD)
Zhu, G.; Wang, J.; Xiao, L.; Yang, K.; Huang, K.; Li, B.; Huang, S.; Xiao, B.; Liu, D.; Feng,L.; Wang, Q.
Frontiers in Neuroscience 2021
Li, B.; Lu, Y.; Wang, C.; Kandula, S..
The 3rd International Workshop on Applied AI for Database Systems and Applications (AIDB).
Li, B.; Lu, Y.; Wang, C.; Kandula, S..
2021
Li,B.; Nuechterlein, N.; Barney, E.; Foster, C.; Kim, M.; Mahony, M.; Atyabi, A.; Feng, L.; Wang, Q.; Ventola, P.; Shapiro, L.; Shic, F.
In 2021 ACM International Conference In Multi-modal Interaction (ICMI)
Liu, K.; Mokhtari, M.; Li, B.; Nofallah, S.; May, C.; Chang, O.; Knezevich, Stevan.; Elmore, J.; Shapiro, L.
In 2021 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
Nuechterlein, N.; Li, B.; Feroze, A.; Holland, E; Shapiro, L; Haynor, D.; Fink, J.; Cimino, P.
In 2021 Neuro-Oncology Advances (NOA)
Nuechterlein, N.; Li, B.; Feroze, A.; Holland, E; Shapiro, L; Haynor, D.; Fink, J.; Cimino, P.
In 2021 Neuro-Oncology, Volume 22, Issue Supplement 2, November 2020
Li, B.; Mercan, E.; Mehta, S.; Knezevich, S.; Arnold, C.; Weaver, D.; Elmore, J.; Shapiro, L.
In 2020 25th International Conference on Pattern Recognition. IEEE.
[PDF] [Slides] [Poster] [Presentation]
Nuechterlein, N.; Li, B.; Seyfioglu, M.; Mehta, S.; Cimino, P.; Shapiro, L.
In 2020 25th International Conference on Pattern Recognition. IEEE.
Li, B.; Barney, E.; Hudac, C.; Nuechterlein, N.; Ventola, P.; Shapiro, L.; Shic, F.
In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research and Applications. ACM. (ACM ETRA 2020).
Wu, W.; Li, B.; Ezgi, M.; Mehta, S.; Bartlett, J.; Weaver, D.; Elmore, J.; Shapiro, L.
In Journal of Clinical Oncology (JCO). 2020
[Link], [PDF], [Code], [Website]
Li, B.; Nuechterlein, N.; Barney, E.; Hudac, C.; Ventola, P.; Shapiro, L.; Shic, F.
arXiv preprint arXiv:1911.13068 (2019).
Li, B.; Mehta, S.; Aneja, D.; Foster, C.; Ventola, P.; Shic, F.; Shapiro, L.
In Proceedings of the IEEE International Conference on Image Processing (ICIP 2019)
[arXiv], [Code], [IEEE SPS Travel Grant]
Li, B., Atyabi, A., Kim, M., Barney, E., Ahn, A., Luo, Y., Aubertine, M., Corrigan, S., John, T., Wang, Q., Mademtzi, M., Best, M., & Shic, F.
In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (p. 443) (ACM SIGCHI 2018).
Atyabi, A., Li, B., Ahn, A., Kim, M., Barney, E., & Shic, F.
In IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2017)
Wang, Q, , Barney, E., Wall, C., Dinicola, L., Foster, C., Ahn, Y., Li, B., & Shic, F.
In Journal of Vision 16(12):1355. September 2016.
Boccanfuso, L., Wang, Q., Leite, I., Li, B., Torres, C., Chen, L., Salomons, N., Foster, C., Barney, E., Ahn, Y., Scassellati, B., & Shic, F.
In IEEE International Symposium on Robot and Human Interactive Communication 2016 (IEEE RO-MAN 2016).
Li, B., Boccanfuso, L., Wang, Q., & Shic, F.
In IEEE International Symposium on Robot and Human Interactive Communication 2016 (IEEE RO-MAN 2016).
Wang, Q., Boccanfuso, L., Li, B., Ahn, A. Y. J., Foster, C. E., Orr, M. P., … & Shic, F.
In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research and Applications (pp. 307-310). ACM. (ACM ETRA 2016).
Li, B., Wang, Q., Barney, E., Hart, L., Wall, C., Chawarska, K., … & Shic, F.
In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research and Applications (pp. 337-338). ACM. (ACM ETRA 2016).
Li, B., Wang, Q., Boccanfuso, L., & Shic, F.
In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research and Applications (pp. 339-340). ACM. (ACM ETRA 2016).
2021 Fall, University of Washington. Course Website
Teaching Assistant
Topics vary and may include vision for graphics, probabilistic vision and learning, medical imaging, content-based image and video retrieval, robot vision, or 3D object recognition.
2021 Spring, University of Washington. Course Website
Teaching Assistant
Image analysis and interpreting the 3D world from image data. Topics include segmentation, motion estimation, image mosaics, 3D-shape reconstruction, object recognition, and image retrieval.
2020 Fall, University of Washington. Course Website
Teaching Assistant
Explore classic and recent research on the close ties between the fields of artificial intelligence and neuroscience, with the goal of understanding how ideas and tools from one field can be applied to the other. Topics to be covered include Bayesian brain models, predictive coding, the free energy principle, deep learning, and reinforcement learning.
2020 Spring, University of Washington. Course Website
Teaching Assistant
Introduction to image analysis and interpreting the 3D world from image data. Topics include segmentation, motion estimation, image mosaics, 3D-shape reconstruction, object recognition, and image retrieval.
2019 Winter, University of Washington. Course Website
Teaching Assistant
Principal ideas and developments in artificial intelligence: Problem solving and search, game playing, knowledge representation and reasoning, uncertainty, probabilistic graphical models, machine learning, reinforcement learning, natural language processing, etc.
2018 Fall, University of Washington. Course Website
Teaching Assistant
Explores methods for designing systems that learn from data and improve with experience. Supervised learning and predictive modeling; decision trees, rule induction, nearest neighbors, Bayesian methods, neural networks, support vector machines, and model ensembles. Unsupervised learning and clustering.
2015 Spring, University of Michigan
Teaching Assistant
Introduction to theory of computation. Models of computation: finite state machines, Turing machines. Decidable and undecidable problems. Polynomial time computability and paradigms of algorithm design. Computational complexity emphasizing NP-hardness. Coping with intractability. Exploiting intractability: cryptography.
| 2022 | Ph.D. | Computer Science and Engineering | University of Washington | Seattle, WA |
| 2015 | Bachelor of Science | Mathematics | University of Michigan | Ann Arbor, MI |
| 2015 | Bachelor of Science | Computer Science | University of Michigan | Ann Arbor, MI |
| 2022 - Now | Senior Research Engineer | CORE | Microsoft Research | Redmond, WA |
| 2016 - 2017 | Research Associate | SCITL | Seattle Children’s Research Institute | Seattle, WA |
| 2015 - 2016 | Research Fellow | Technology Innovation Lab | Yale University | New Haven, CT |