Beibin Li

李辈滨

Updated: June/2024

About Publication Teaching Experience



drawing

[Curriculum Vitae]; [Google Scholar]; beibin.li at microsoft.com;


Machine Learning and Optimization (MLO), Microsoft Research, Redmond, WA


I’m currently a senior research engineer at Microsoft Research, where my work centers on AI and combinatorial optimization for cloud operations. Prior to joining MSR, I pursued a Ph.D. at the Paul G. Allen School of Computer Science and Engineering, University of Washington, under the guidance of Linda Shapiro and Frederick Shic. During that time, I dedicated my research to developing a Unified Data Adaptation Framework for Neural Networks, with a particular focus on low-resource neural adaptation. In my dissertation, I delved into the adaptation of deep learning models for a range of applications, including histopathological images, eye tracking models, autism behavior analyses, and database optimization.

Publication

Small Language Models for Application Interactions: A Case Study

Li, B., Zhang, Y., Bubeck, S., Pathuri, S., Menache, I.

[arXiv]

Reflect-RL: Two-Player Online RL Fine-Tuning for LMs

Zhou, R., Du, SS., Li, B.

[arXiv]

Autogen: Enabling next-gen llm applications via multi-agent conversation framework

Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B., Jiang, L., Zhang, X., Wang, C.

[arXiv] [GitHub]

Large Language Models for Supply Chain Optimization

Li, B.; Mellou, K.; Zhang, B.; Pathuri, J.; Menache, I.;

[arXiv] [GitHub]

Kerveros: Efficient and Scalable Cloud Admission Control

Sajal, S.; Marshall, L.; Li, B.; Zhou, S.; Pan, A.; Mellou, K.; Narayanan1, D.; Zhu, T.; Dion, D.; Moscibroda, T.; Menache, I.

In OSDI, 2023

VM Allocation with Lifetime Predictions

Barbalho, H.; Kovaleski, P.; Li, B.; Marshall, L.; Molinaro, M.; Pan, A.; Cortez, E.; Leao, M.; Patwari, H.; Tang, Z.; Santos, T.; Goncalves, L.; Dion, D.; Moscibroda, T.; Menache, I.

In MLSys, 2023

VSGD-Net: Virtual Staining Guided Melanocyte Detection on Histopathological Images

Liu, K.; Li, B.; Wu, W.; May, C.; Chang, O.; Knezevich, S.; Reische, L.; Elmore, J.; Shapiro, L.;

In WACV, 2023

The autism biomarkers consortium for clinical trials: evaluation of a battery of candidate eye-tracking biomarkers for use in autism clinical trials

Shic, F., Naples, A.J., Barney, E.C., Chang, S.A., Li, B., McAllister, T., Kim, M., Dommer, K.J., Hasselmo, S., Atyabi, A. and Wang, Q.

In Molecular Autism, 13(1), pp.1-17. 2022

Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts

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

In 2022 ACM Management of Data (SIGMOD).

[PDF] [Page] [Talk Slides PDF] [Talk Video]

Improving the Diagnosis of Skin Biopsies using Tissue Segmentation

Nofallah, S.; Li, B.; Mokhtari, M.; Wu, W.; Knezevich, S.; May, C. J.; Chang, O. H.; Elmore, J.; Shapiro, L.

In Diagnostics, 2022

Stratification of Children with Autism Spectrum Disorder through Fusion of Temporal Information in Eye-gaze Scan-paths

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)

[Link]

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). 2021

[PDF]

Q-error Bounds of Random Uniform Sampling for Cardinality Estimation

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

2021

[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

[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 577: Medical Image Analysis

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.

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.

Experience

Education

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

Work

2022 - Now Senior Research Engineer Machine Learning and Optimization 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