About
Highly accomplished Data Science and Big Data Technology student with a 3.61 GPA, specializing in advanced machine learning, spatiotemporal analysis, and big data management. Proven ability to conduct impactful research, develop innovative models, and publish in top-tier journals, including Applied Soft Computing and Applied Energy. Eager to leverage expertise in deep learning, graph neural networks, and data-driven problem-solving to excel in a challenging data science or machine learning engineering role.
Work
New York University Shanghai
|Research Internship
Shanghai, Shanghai, China
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Summary
Engaged in a research internship at NYU Shanghai, focusing on advanced topics in data science and machine learning to contribute to ongoing academic studies.
Highlights
Conducted in-depth literature reviews and experimental design for research initiatives in spatiotemporal analysis and machine learning.
Developed and refined data processing pipelines for complex datasets, ensuring accuracy and efficiency for research objectives.
Contributed to the analysis and interpretation of research findings, preparing preliminary reports and presentations for faculty.
Key Laboratory of Big Data Management Optimization and Decision
|Student Member
Dalian, Liaoning, China
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Summary
Contributed to cutting-edge research and development initiatives within a leading big data laboratory, focusing on optimizing data management and decision-making processes.
Highlights
Assisted in the development and implementation of advanced data analytics models, supporting key research projects in big data optimization.
Collaborated with senior researchers on data collection, processing, and analysis tasks, enhancing project efficiency and data integrity.
Gained practical experience in applying theoretical knowledge to real-world big data challenges, contributing to the lab's research output.
Education
Dongbei University of Finance and Economics
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Bachelor
Data Science and Big Data Technology
Grade: GPA: 3.61
Courses
Data Scraping and Data Cleaning (97/100)
Mathematical Modeling (97/100)
Natural Language Processing (97/100)
Machine Learning and Financial Modeling (98/100)
Deep Learning (96/100)
Awards
National Project Grant
Awarded By
College Student Innovation and Entrepreneurship Project
Awarded as a Team Leader for a national-level project grant.
National First Prize
Awarded By
National College Student Market Research and Analysis Competition
Achieved top 0.43% in a national competition for market research and analysis.
Honorable Mention
Awarded By
Mathematical Contest in Modeling (MCM)
Received honorable mention for outstanding performance in the Mathematical Contest in Modeling.
National Bronze Medal
Awarded By
China International College Students Innovation Contest
Awarded a national bronze medal in a prestigious innovation contest.
National Bronze Medal
Awarded By
9th National Innovation and Entrepreneurship Competition for Financial and Economic Institutions
Received a national bronze medal in a competition focused on financial and economic innovation.
Publications
IMMA: Incident-aware Momentum and Memory Adaptation on Streaming Traffic Data under Compound Drift
Published by
Working paper
Summary
Co-authored a working paper introducing IMMA for adaptive analysis of streaming traffic data with compound drift.
Languages
English (IELTS 6.5)
Mandarin Chinese (Native)
Certificates
Skills
Web Technologies
HTML, Vue, Streamlit.
Database Systems
SQL, Spark, DASK.
Data Science & Machine Learning
Python, Torch, Deep Learning, Natural Language Processing, Machine Learning, Spatiotemporal Analysis, Graph Neural Networks, Pattern Recognition, Knowledge Distillation, Multimodal Sentiment Recognition, Traffic Forecasting.
DevOps & Version Control
Git.
Mathematical & Statistical Tools
SPSS, R, Mathematical Modeling, Data Scraping, Data Cleaning.
Programming Languages
Python, Java, R.
Projects
Multi-Source Spatiotemporal Analysis with Graph-based Temporal Modeling
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Summary
Developed a novel graph signal processing framework for dynamic spatiotemporal data analysis, integrating advanced architectures to enhance pattern recognition and optimize information flow in sensor networks.
Compound Drift Adaptation for Urban Traffic Forecasting
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Summary
Formalized the concept of compound drift in streaming traffic data and engineered an adaptive framework to reduce adaptation latency for transient anomalies while maintaining prediction accuracy.