I am a data scientist and AI engineering specialist focused on the full AI lifecycle: large-scale data processing, model development, deployment-aware optimization, and the evaluation of trustworthy behavior in machine learning systems. Across collaborations with Fraunhofer IAO, Audi, Schwarz Group, and the Technical University of Munich, I have worked on real-world industrial data challenges while maintaining a strong academic orientation toward robust and responsible AI.

My portfolio brings together edge AI optimization for manufacturing, bias assessment for AI systems, scalable data engineering, and quantitative research on digital transformation and financial markets. I am particularly interested in translating technically rigorous methods into systems that remain reliable under practical operational constraints.

Name

Youngwon Cho

Studies

B.Sc. Information Engineering; Management and Technology background at TUM

Primary areas

Industrial AI, edge optimization, trustworthy AI, data engineering

Recent affiliations

Fraunhofer IAO, Audi, Schwarz Group, TUM SOM CDT

I organize my work as a small set of threads—each with a clear question, a deployment constraint, and an evaluation story. This keeps projects coherent across industry and academic settings.

I present projects as evidence: what problem it solved, how it was designed, and what reliability constraints shaped the result.

The document emphasized project-based outputs, technical research contributions, and academic visibility rather than a long publication list. This section presents those outcomes in a portfolio-friendly format.

1. Europe-Korea Conference 2025 poster presentation. Presented BEACON and its fairness evaluation framework during the EKC 2025 poster session in Vienna.
2. Fraunhofer IAO internal research and development. Conceived and implemented a real-time dashboard for bias assessment in ML and LLM systems, integrating multiple benchmark datasets and fairness metrics.
3. Industrial deployment-oriented AI work with Audi. Contributed to a real-time anomaly detection system with explicit attention to low-resource inference, calibration, and operational false positive control.
4. Quantitative research support at TUM SOM CDT. Developed SEC report scraping workflows, refined ETF and ESG datasets, and supported sustainable finance research activities using Bloomberg, Morningstar, and LSEG data.
5. Awards and distinctions. Includes the Foundation Scholarship from the Korean Scientists and Engineers Association in Germany and a second award in the 42 Heilbronn and TUM Campus Heilbronn cyber security competition in 2025.

• Programming and systems. Python, Rust, C++, SQL, gRPC, Docker, and service-oriented engineering for research-grade and industrial applications.
• AI and machine learning. Time-series anomaly detection, VRNN-style modeling, OmniAnomaly, continual learning, LLM safety, fairness analysis, and deployment-aware optimization with CUDA and AMP.
• Data science. Large-scale unstructured log analysis, financial data scraping, exploratory data analysis, and benchmark-driven evaluation workflows.
• Domain perspective. Manufacturing AI, smart infrastructure, ethical AI evaluation, digital transformation, and sustainable finance research contexts.
• Languages. Korean (native), English (IELTS Academic 7.0, TOEIC 810), and German (Goethe Zertifikat A2).

Contact details below were aligned with the provided CV.

Location

Heilbronn, Germany

CV file path (for hosting)

public/cv/Youngwon Cho Lebenslauf.pdf

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