Academic teaching

Applied learning for AI, analytics, and digital strategy.

My teaching combines technical fluency, business application, responsible AI, and a classroom culture built around discussion, feedback, and professional growth.

Talks & executive training →
4.9/5 learned a lot 4.9/5 organisation & supervision MSc + Executive audiences Hands-on Python, LLMs & analytics

Courses

From AI literacy to analytics and causal reasoning.

Master’s elective

Applied AI for Marketing

Functional, ethical, and rhetorical AI literacy through Python, transformers, LLM workflows, agentic AI, business data, and responsible deployment.

MSc + Executive Master’s

Digital Marketing & Analytics

Customer journeys, campaign measurement, attribution, structured and unstructured data, predictive models, and AI-supported decision-making.

Bachelor Economics

Quantitative Data Analysis 2

Statistical inference, regression, ANOVA, model diagnostics, and individualized support for applied quantitative reasoning.

Teaching approach

Technical depth works better when students feel able to experiment.

Applied

Real business data, code, cases, and decision problems.

Responsible

Bias, transparency, fairness, explainability, governance, and the EU AI Act.

Human

Discussion, mentoring, peer connection, and feedback that extends beyond the assignment.

Student feedback

“Thanks to your teaching style, I had fun while learning AI.”
“Really appreciate how the lecturers explain and break down the topic to make it understandable for people who are new to AI.”
“She creates a community—her feedback goes beyond the classroom.”