Hao Zhang
Contact Information 📞
Education 🎓
- Guangzhou University: Bachelor of Computer Science (Sept. 2015 - Jun. 2019)
- TU Darmstadt: Master of Computer Science (Informatik) (Apr. 2020 - May 2024)
- GPA: 1.3
Language Skills 🌐
Technical Skills 💻
- Proficient in: Python agile development, time series data processing, SQL, Linux, GitHub version control, CI/CD, cloud infrastructure (Azure, AWS), microservices (Docker, Kubernetes, AWS Lambda)
- Knowledge of: Intraday continuous power trading, data platform management, Epex auction data, trading strategies back-test and algo-trading, operation of options and contracts
Career Path 💼
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Energy Data Engineer at Impuls Energy Trading GmbH (Oct. 2024 - Apr. 2025, Cologne)
- Designed and developed data models and analytic pipelines for both manual and algorithmic energy trading.
- Built dashboards and visualizations for historical and real-time data using Grafana and SQL.
- Responded promptly to trading desk requests, delivering actionable data and insights.
- Managed and maintained AWS infrastructure, including AWS Lambda, Cloud Watch, schedulers, and PostgreSQL databases.
- Processed with Agile development, utilized sprints, pair programming, and pair code reviews to deliver high-quality software efficiently.
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Data Engineer Intern at Allianz Global Investor (Nov. 2023 - Jul. 2024, Frankfurt am Main)
- Developed software for automating the bonds data-uploading procedure.
- Implemented frontend UI via Python Streamlit library and SQL to simplify the operation of SQL server.
- Deployed the microservice via Kubernetes in Azure Cloud Maintain access.
- Conducted quantitative research on bond yields and credits.
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Student Researcher at UKP Lab (Apr. 2022 - Apr. 2023, Darmstadt)
- Developed Full-stack project UKP-SQuARE (square.ukp-lab.de) with FastAPI as backend framework and Vue.Js as frontend framework.
- Researched, replicated, and modified the state-of-the-art LLM transformer model.
- Developed and deployed LLM on Question-Answering tasks in an online platform via Docker Compose.
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Student Researcher at PTW Lab (Apr. 2022 - Oct. 2023, Darmstadt)
- Implemented a machine learning project using a tree model to classify the quality of products from a lathe.
- Fine-tuned the machine learning model and achieved 0.92 F1-score on unseen data.
- Independently developed a Full-stack project on embedded devices, utilizing Flask API and Vue.js.
Projects 📚
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Master Thesis: Grounding Text Generation with Knowledge Graphs (May. 2023 - Oct. 2023)
- Fine-tuned and pretrained large language models (T5) with knowledge graphs and large-scale datasets, achieving significant performance improvements (+5%) on CSQA tasks.
Published Papers 📝