Beibin Li

August/2021



drawing

[Curriculum Vitae]; [Google Scholar]; beibin at uw.edu;



As a Ph.D. candidate at University of Washington, I focus on self-supervision and few-shot learning in computer vision. I work with Prof. Linda Shapiro and Prof. Frederick Shic to diagnose and intervene in Autism Spectrum Disorder by using eye tracking, facial expression detection, and augmented reality. I also work on medical data analysis including MRI, fMRI, fNIRS, breast cancer diagnosis, and prostate cancer diagnosis.

Publication

Memory Deficit in Patients with Temporal Lobe Epilepsy: Evidence from Eye Tracking Technology

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

[Link]

Cardinality Estimation: Is Machine Learning a Silver Bullet?

Li, B.; Lu, Y.; Wang, C.; Kandula, S..

The 3rd International Workshop on Applied AI for Database Systems and Applications (AIDB).

[PDF]

Q-error Bounds of Random Uniform Sampling for Cardinality Estimation

Li, B.; Lu, Y.; Wang, C.; Kandula, S..

[arXiv]

Learning Oculomotor Behaviors from Scanpath

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)

[arXiv] [Code]

Learning Melanocytic Proliferation Segmentation in Histopathology Images from Imperfect Annotations

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

[PDF]

Radiogenomic Modeling Predicts Survival-Associated Prognostic Groups in Glioblastoma

Nuechterlein, N.; Li, B.; Feroze, A.; Holland, E; Shapiro, L; Haynor, D.; Fink, J.; Cimino, P.

In 2021 Neuro-Oncology Advances (NOA)

[Link]

Radiogenomic Features Predict Clinically Relevant Genome-Wide Alteration Signatures In Glioblastoma

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, Page ii158

[Link]

Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline

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]

Leveraging Unlabeled Data for Glioma Molecular Subtype and Survival Prediction

Nuechterlein, N.; Li, B.; Seyfioglu, M.; Mehta, S.; Cimino, P.; Shapiro, L.

In 2020 25th International Conference on Pattern Recognition. IEEE.

[PDF]

Selection of Eye-Tracking Stimuli for Prediction by Sparsely Grouped Input Variables for Neural Networks: towards Biomarker Refinement for Autism

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).

[PDF], [Code]

MLCD: A Unified Software Package for Cancer Diagnosis

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]

Sparsely Grouped Input Variables for Neural Networks

Li, B.; Nuechterlein, N.; Barney, E.; Hudac, C.; Ventola, P.; Shapiro, L.; Shic, F.

arXiv preprint arXiv:1911.13068 (2019).

[arXiv], [Code]

A Facial Affect Analysis System for Autism Spectrum Disorder

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]

Social Influences on Executive Functioning in Autism: Design of a Mobile Gaming Platform

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).

[PDF]

An Exploratory Analysis Targeting Diagnostic Classification of AAC App Usage Patterns

Atyabi, A., Li, B., Ahn, A., Kim, M., Barney, E., & Shic, F.

In IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2017)

[PDF]

Hybrid Calibration for Eye Tracking: Smooth Pursuit Trajectory with Anchor Points

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.

[Link]

A Thermal Emotion Classifier for Improved Human-Robot Interaction

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).

[PDF]

Human Robot Activity Classification based on Accelerometer and Gyroscope

Li, B., Boccanfuso, L., Wang, Q., & Shic, F.

In IEEE International Symposium on Robot and Human Interactive Communication 2016 (IEEE RO-MAN 2016).

[PDF]

Thermographic eye tracking

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).

[PDF]

Modified DBSCAN algorithm on oculomotor fixation identification

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).

[PDF] [Code]

Optimality of the distance dispersion fixation identification algorithm

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).

[PDF]

Teaching

CSE 576: Computer Vision

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.

CSE 599B: AI and the Brain

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.

CSE 455: Computer Vision

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.

CSE 473: Introduction to Artificial Intelligence

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.

CSE 546: Machine Learning

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.

EECS 376: Foundation of Computer Science (Theory of Computation)

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.

Education

2017 - Current Ph.D. Student Computer Science and Engineering University of Washington Seattle, WA
Graduated 2015 Bachelor of Science Mathematics University of Michigan Ann Arbor, MI
Graduated 2015 Bachelor of Science Computer Science University of Michigan Ann Arbor, MI

Experience

2016 - 2017 Research Associate SCITL Seattle Children’s Research Institute Seattle, WA
2015 - 2016 Research Fellow Technology Innovation Lab Yale University New Haven, CT