Harness the Power of AI: Transforming Ideas into Innovation

Who am I?

Stijn Geuens, operating under the legal entity Recommendit, is a highly skilled freelance machine learning expert and AI architect with over 15 years of experience in both academia and industry. His extensive background spans various sectors, where he has developed a deep expertise in creating AI-driven solutions.

Stijn specializes in overseeing the complete lifecycle of AI application development, from initial design through data engineering, model development, and deployment of production-ready systems. His experience covers a broad range of industries such as marketing, retail, online sales, automotive, pharmaceuticals, supply chain, and public services, showcasing his versatility in applying AI to diverse domains.

This broad expertise allows Stijn to tailor AI solutions to specific business needs, leveraging his deep knowledge of machine learning and artificial intelligence to help organizations enhance operational efficiency, boost customer engagement, and drive innovation.

Professional Experience

ML Engineer / Engineering Lead

MLOps Engineer

AI Architect / Expert

Lead Data Scientist

Big Data Engineer

Data Scientist

Software developer

Startup - CTO

Adjunct professr

Education

PhD
Data Science

Master Commercial Engineering

Master Marketing Analytics

Bachelor Commercial Engineering

Coussement, K., De Bock, K.W. and Geuens, S., 2022, A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer, Annals of Operations Research, 315, pp. 671–694

Geuens, S., Coussement, K. and De Bock, K.W., 2018, A framework for configuring collaborative filtering-based recommendations derived from purchase data. European Journal of Operational Research, 265 (1), pp. 208-218.

Geuens, S., De Bock, K.W. and Coussement, K., 2018, Beyond clickthrough rate: measuring the true impact of personalized e-mail product recommendations. Proc. Business Analytics for Finance and Industry (BAFI) Conference 2018, Santiago, Chile.

Geuens, S., Coussement, K. and De Bock, K.W., 2016, Towards better online personalization: a framework for empirical evaluation and real-life validation of hybrid recommendation systems, Proc. World Marketing Congress of the Academy of Marketing Science, Paris, France.

Geuens, S., Coussement, K. and De Bock, K.W., 2015, An Evaluation Framework for Collaborative Filtering on Purchase Information in Recommendation Systems, 2nd Conference on Business Analytics in Finance and Industry (BAFI), Santiago, Chile.

Geuens, S., Coussement, K. and De Bock, K.W., 2015, Integrating Behavioral, Product, and Customer Data in Hybrid Recommendation Systems Based on Factorization Machines, 2nd Conference on Business Analytics in Finance and Industry (BAFI 2015), Santiago, Chile.

Geuens S, 2015, I Know What You’ll Buy Next Summer, presented at the BAQMaR Conference, Ghent, Belgium

Geuens S., 2015, Recommendation Systems for E-commerce, Evaluating Collaborative Filtering in a Binary Setting, presented at the Marketing Research Seminar IESEG School of Management, Lille, France

Geuens, S., Coussement, K. and De Bock, K.W., 2014, Evaluating Collaborative Filtering: Methods within a Binary Purchase Setting. ECML/PKDD Conference, Nancy, France.