Dr. Jan Philip Göpfert
AI Consultant & Entrepreneur | Machine Learning PhD
Executive Summary
Machine Learning PhD and AI entrepreneur with a decade of experience transforming complex AI concepts into scalable business solutions. Proven track record of delivering practical AI systems with measurable ROI across enterprise clients and startups. Combines deep technical expertise in adversarial robustness, data engineering, and end-to-end system architecture with strategic business acumen — increasingly within strict European regulatory frameworks (CSRD, EU AI Act).
Core Competencies
Professional Experience
Founding AI & Technical Lead
Enterprise AI Startup (Stealth)
End-to-End Ownership: Architected and shipped the product prototype as sole engineer — data model, ingest and inference pipelines, APIs, and the deployed cloud stack
Technical Authority: Sole technical decision-maker alongside founders from private equity and enterprise IT; own AI, infrastructure, and product-engineering decisions under regulatory ambiguity
Regulated Data Workflows: Designing EU-sovereign data workflows for regulated corporate reporting (CSRD) — normalization, audit trails, and signal provenance
AI Consultant & Entrepreneur
Independent Practice
Strategic Consulting: Advise businesses on AI adoption strategies, automation opportunities, and digital transformation roadmaps
Industry Speaking: Deliver keynote presentations and technical talks at industry conferences on AI applications and implementation strategies
Product Development: Architect and develop AI-powered systems including content generation platforms and intelligent community engagement tools
AI Training: Designed and delivered AI-literacy training for IT consultancies and public administration — capabilities, limits, data protection, and EU AI Act literacy duties
Data Engineering: Designed and deployed a multi-source health-data correlation engine with a FastAPI aggregation layer fusing heterogeneous high-frequency sensor and nutrition data
Principal Machine Learning Engineer
VisionAI (formerly Recommendy)
Pipeline Optimization: Identified critical flaws in ML training and data-loading workflows; replaced the loss architecture with a contrastive approach delivering a 200x speedup in training time
Product Innovation: Built and deployed a multimodal semantic search engine from scratch, with a purpose-built inference layer on raw CLIP embeddings — became the company's flagship product and drove the rebrand to VisionAI
Engineering Culture: Standardized CI/CD automation and enforced typed data serialization (Pydantic, Parquet) across ML deployments
Research Assistant — Machine Learning
CITEC, Bielefeld University
Research Leadership: Led investigations into adversarial robustness, uncertainty quantification, and human-machine interaction
PhD Completion: Completed doctoral thesis "Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition" (2022)
Team Management: Supervised 15+ Bachelor and Master theses, developing next-generation researchers
Publication Success: Co-authored papers in top-tier venues (IEEE, ICANN, ECML) with significant industry impact
Research Assistant — Computer Graphics & AI
CITEC, Bielefeld University
Applied Research: Pioneered machine learning applications in 3D scanning and geometry processing
Educational Leadership: Designed and delivered Scientific Computing lecture to 50+ students
Cross-Disciplinary Innovation: Bridged computer graphics and ML, contributing to 3D head reconstruction using CNNs
International Experience
SAP Labs Shanghai
Researched Monte Carlo Methods and Predictive Analytics for SAP HANA in-memory database
Conducted market analysis and developed prototypes for enterprise-scale analytics
Education
PhD, Machine Learning
Bielefeld University
Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
MSc, Intelligent Systems
Bielefeld University
Self-Improving Blendshape Models
BSc, Mathematics
Bielefeld University
Knot Theory and the Jones Polynomial
A Levels
Gordonstoun School, Scotland
Technical Expertise
AI & Machine Learning
Data Engineering & Architecture
Programming & Development
Regulatory & Compliance
Languages
Selected Publications & Research Impact
Robustness in Machine Learning
Comprehensive investigation of adversarial robustness in neural networks with practical implications for production AI systems.
Deep Learning for Understanding Satellite Imagery
Co-authored survey achieving state-of-the-art results in building detection using U-Net and Mask R-CNN architectures.
Adversarial Attacks Hidden in Plain Sight
Demonstrated perceptually inconspicuous adversarial perturbations, with implications for the robustness of deployed vision systems.
Intuitiveness in Active Teaching
Proposed framework for human-AI interaction, directly applicable to enterprise AI adoption strategies.
Additional Publications: 15+ peer-reviewed papers in machine learning, computer graphics, and human-computer interaction — full list on Google Scholar.
Leadership & Management
Team Development
Mentorship: Supervised 15+ graduate theses, developing talent pipeline for AI industry
Teaching Excellence: Delivered university-level courses to 250+ students
Cross-Cultural Leadership: Managed international student programs
Strategic Initiatives
Community Building: Founded and led student organizations
Process Innovation: Developed research-to-production AI workflows
International Experience: Research across Germany, China, and Scotland
Philosophy & Approach
My approach to technology leadership combines rigorous scientific thinking with practical business acumen. I believe in building AI systems that deliver measurable value while maintaining ethical standards and technical robustness. My experience spans the complete spectrum from fundamental research to production deployment, enabling me to guide organizations through the complexities of AI adoption with both technical depth and strategic clarity.
I'm particularly passionate about taking research-grade machine learning and making it work in production — helping organizations avoid common pitfalls while maximizing the value of intelligent systems.