# Guangyu Wang - Data Scientist | Machine Learning Engineer | Researcher
Source: https://hello.cv/guangyuwang
Dalian

## Links
- GitHub | https://guangyu-dufe.github.io
- Email
- Phone

## 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
### Research Internship | New York University Shanghai
Engaged in a research internship at NYU Shanghai, focusing on advanced topics in data science and machine learning to contribute to ongoing academic studies.
- 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.
### Student Member | Key Laboratory of Big Data Management Optimization and Decision
Contributed to cutting-edge research and development initiatives within a leading big data laboratory, focusing on optimizing data management and decision-making processes.
- 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 | Data Science and Big Data Technology
GPA: 3.61
- 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
College Student Innovation and Entrepreneurship Project
Awarded as a Team Leader for a national-level project grant.
### National First Prize
National College Student Market Research and Analysis Competition
Achieved top 0.43% in a national competition for market research and analysis.
### Honorable Mention
Mathematical Contest in Modeling (MCM)
Received honorable mention for outstanding performance in the Mathematical Contest in Modeling.
### National Bronze Medal
China International College Students Innovation Contest
Awarded a national bronze medal in a prestigious innovation contest.
### National Bronze Medal
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
### Frequency as identity: A Fourier hypernetwork for spatiotemporal forecasting
Applied Soft Computing | https://doi.org/10.1016/j.asoc.2025.114297
Co-authored a manuscript on Fourier hypernetworks for spatiotemporal forecasting, published in a JCR Q1 journal (IF=6.6).
### RoseNet: A Cross-Modal Incongruity Adaptive Graph Learning Network in Multimodal Sentiment Recognition
International Joint Conference on Neural Networks (IJCNN) | https://doi.org/10.1109/IJCNN64981.2025.11227414
Co-authored a manuscript on RoseNet for cross-modal sentiment recognition, published in a CCF-C conference.
### Enhancing PV power forecasting accuracy through nonlinear weather correction based on multi-task learning
Applied Energy | https://doi.org/10.1016/j.apenergy.2025.125525
Co-authored a manuscript on enhancing PV power forecasting accuracy using multi-task learning, published in a JCR Q1 journal (IF=10.1).
### IMMA: Incident-aware Momentum and Memory Adaptation on Streaming Traffic Data under Compound Drift
Working paper
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
### Software Copyright: Multimodal Interactive Robot Platform V1.0
China National Copyright Administration | https://www.cnipa.gov.cn/ (Registration No.: 2025SR0099810)
### Software Copyright: Multi-Sensor Fusion Robot Platform V1.0
China National Copyright Administration | https://www.cnipa.gov.cn/ (Registration No.: 2025SR0099815)
### Software Copyright: Service Robot Management Platform V1.0
China National Copyright Administration | https://www.cnipa.gov.cn/ (Registration No.: 2024SR0995070)
### Software Copyright: Multimodal Speech Audio Adjustment System V1.0
China National Copyright Administration | https://www.cnipa.gov.cn/ (Registration No.: 2024SR0827021)
### Patent: A Deep Multimodal Sentiment Analysis Method Based on Graphic-Text Interaction Information and Multimodal Sentiment Influence Factors
China National Intellectual Property Administration | https://www.cnipa.gov.cn/ (Registration No.: 2024105534352)

## Skills
### Programming Languages
- Python
- Java
- R
### 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

## Projects
### Multi-Source Spatiotemporal Analysis with Graph-based Temporal Modeling
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
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.

## Source
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