Sijie He (思捷)

Research Interests

My main research interests are in spatiotemporal data analysis and causal learning. I’ve been studying and applying those techniques on problems from a variety of areas such as climate sciences, and condition monitoring data analysis.

Publications

• Learning and dynamical models for sub-seasonal climate forecasting: comparison and collaboration

Sijie He, Xinyan Li, Laurie Trenary, Benjamin Cash, Timothy DelSole, and Arindam Banerjee.

AAAI Conference on Artificial Intelligence, 2022 [pdf][code][data]

• Machine learning and dynamical models for sub-seasonal climate forecasting

Sijie He, Xinyan Li, Laurie Trenary, Benjamin Cash, Timothy DelSole, and Arindam Banerjee.

NeurIPS workshop on Machine Learning and the Physical Sciences, 2021 [pdf][code][data]

• Sub-seasonal climate forecasting via machine learning: challenges, analysis, and advances

Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, and Arindam Banerjee

AAAI Conference on Artificial Intelligence, 2021 [pdf][code][data] [website]

• Flight data anomaly detection and diagnosis with variable association change

Sijie He, Hao Huang, Shinjae Yoo, Weizhong Yan, Tianyi Wang, Feng Xue, and Chenxiao Xu

ACM/SIGAPP Symposium on Applied Computing, 2021 (Acceptance rate: 23%) [pdf]

• Interpretable predictive modeling for Azure demand

Sijie He, Catherine Fan, and Eithon Cadag

Machine Learning and Data Sciences Conference, Microsoft, 2020

• Interpretable predictive modeling for climate variables with weighted lasso

Sijie He, Xinyan Li, Vidyashankar Sivakumar, and Arindam Banerjee

AAAI Conference on Artificial Intelligence, 2019 [pdf][code]

(Acceptance rate: 16.2%, selected for oral presentation)

• Land climate prediction using sea surface temperatures

Sijie He, Xinyan Li, Vidyashankar Sivakumar, and Arindam Banerjee

International Workshop on Climate Informatics, 2018 (Spotlight presentation, acceptance rate: 7/45)

• High-dimensional dependency structure learning for physical processes

Jamal Golmohammadi, Imme Ebert-Uphoff, Sijie He, Yi Deng, and Arindam Banerjee

IEEE International Conference on Data Mining (ICDM), 2017 [pdf][arxiv]

(Acceptance rate: 19.9%)

• Flight mode recognition method of the unmanned aerial vehicle based on telemetric data

Sijie He, Datong Liu, and Yu Peng

Chinese Journal of Scientific Instrument, 2016 (Best paper award, acceptance rate: 10%)

• Fault diagnosis for discrete monitoring data based on fusion algorithm

Sijie He, Yu Peng, and Datong Liu

IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 2015 [pdf]

(Best presentation in PHM subsection)

Work Experience

• Summer 2021 Visiting scholar at University of Illinois Urbana-Champaign

• Summer 2020 Data scientist internship in Cloud+AI group, Microsoft

• Summer 2019 Research internship in Global Research Center, General Electric - Mentor: Dr. Hao Huang

• Fall 2018 Teaching assistant of Computational Aspects of Matrix Theory, UMN - Prof. Yousef Saad

• Summer 2015 Teaching assistant of System Reliability Engineering and Technology, HIT - Prof. Haitao Liao

Awards

• Student Travel award, AAAI (2019)

• Student Travel award, CI Workshop (2018)

• Student Travel Award, ICDM (2017)

• Quality Metrics Fellowship for Graduate Study, University of Minnesota (2016 - 2017)

• Best Paper award, Chinese Journal of Scientific Instrument (2016)

• Best Graduate Thesis Award in Harbin Institute of Technology (2016)

• National Scholarship for Graduate Students in China (2015)

• Best Oral Presentation Award, ICEMI (2015)

• Endress+Hauser SC China Scholarship (2013)