Beibin Li

Microsoft Research, Redmond, WA

I am currently a Senior Research Software Engineer at Microsoft Research, where my work focuses on combinatorial optimization for cloud operations and Microsoft cloud supply chain. In this role, I develop efficient algorithms to enhance cloud computing performance, resource allocation, logistics, and device installation. My research aims to solve mathematical optimization problems related to cloud infrastructure, ensuring optimal utilization of resources and improved operational efficiency.

During my Ph.D. studies at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, under the guidance of Professors Linda Shapiro and Frederick Shic, I concentrated on developing a Unified Data Adaptation Framework with an emphasis on low-resource adaptation. My work involved adapting data analysis tools and softwares to various applications, including the analysis of histopathological images, eye tracking, autism research, and database optimization.

During my Ph.D., I also interned at Google (summer 2021) and Microsoft (summer 2020), for data analysis and database optimization.

Before my Ph.D., I had full time jobs at Yale University and Seattle Children’s Research Institute, where my research focused on analyzing eye-tracking data and autism research.

Experience

Work

Education

Publications

Efficient Cloud Server Deployment Under Demand Uncertainty

Liu, R.P., Mellou, K., Gong, E.X.Y., Li, B., Coffee, T., Pathuri, J., Simchi-Levi, D. and Menache, I.

In MSOM, 2025

Towards Safer Heuristics With Xplain

Karimi, P., Pirelli, S., Kakarla, S., Beckett, R., Segarra, S., Li, B., Namyar, P., Arzani, B.

In HotNet, 2024

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

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

Zhou, R., Du, SS., Li, B. 2023

In ACL

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. 2023

In COLM

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

A Functional Model for Studying Common Trends Across Trial Time in Eye Tracking Experiments

Dong, M., Telesca, D., Sugar, C., Shic, F., Naples, A., Johnson, S.P., Li, B., Atyabi, A., Xie, M., Webb, S.J. and Jeste, S.;

In Statistics in Biosciences, pp.1-27. 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).

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)

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

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

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)

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

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)

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

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.

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.

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).

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

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)

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).

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)

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.

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).

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).

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).

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).

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).