About me

I’m currently working as a ML Engineer at Loop Earplugs and teaching “Data Science for Business” at the Antwerp Management School (Master in Management).

Previously, I worked as a consultant at QuantumBlack (AI by McKinsey), designing and implementing advanced analytics solutions across various industries, topics, and countries.

Before McKinsey, I’ve obtained my PhD in Data Science from the University of Antwerp (fellowship granted by the Research Foundation-Flanders), with Prof. David Martens as my supervisor. In my research, I’ve contributed to the field of Explainable AI by developing new methods to explain black-box AI models that are trained on big human behavioral data (think of web browsing data, GPS locations, financial transactions, social media data,…).

• CV (June 2025)
• Google Scholar
• LinkedIn

ML Project Highlights

AI-driven demand & supply planning
Contributed to QuantumBlack's Frontline.ai module, an integrated demand & supply planning engine using time-series forecasting & optimization. Applied this solution across industries, from call center planning to energy tank maintenance.

Service-level-aware workforce optimization
Implemented service-level constraints (e.g. average wait time < 60 sec) in an optimization model for workforce planning. Integrated into QuantumBlack's Frontline.AI package.

Read more about Frontline.ai →

GenAI campaign visuals at scale
Used open-source Stable Diffusion models with fine-tuned LoRAs to generate large batches of static marketing images aligned with brand identity (e.g., color, vibe, style). Project in collaboration with Inect.

Search-based product recommendations
Developed a recommendations engine leveraging a fine-tuned OpenAI model to match user queries to the relevant product, bypassing rigid use-case selection. Resulted in higher conversion rates.

Auto-tagging of sentiment & topics in customer data
Built a classification pipeline using fine-tuned OpenAI model to auto-tag sentiment & topics in customer reviews and support interactions. Enabled faster, more consistent insights for decision-making across business teams.

Generative ideation for Creative teams
Built a GenAI toolchain to assist marketers with ad concepting, variant briefings, and creative angles. Prompted on brand tone and audience profiles to reduce campaign development time & increase ideation throughput.

Research Publications

• Implications of Cloaking Digital Footprints for Privacy and Personalization. (2023). Sofie Goethals, Sandra Matz, Foster Provost, David Martes and Yanou Ramon. Working paper available here.

• Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records. (2021). Yanou Ramon, R.A. Farrokhnia, Sandra C. Matz, and David Martens. Information, 12(12), 518. Available online.

• Can Metafeatures Help Improve Explanations of Prediction Models When Using Behavioral and Textual Data? (2021). Yanou Ramon, David Martens, Theodoros Evgeniou and Stiene Praet. Machine Learning. Available online. Full pdf available here.

• A Comparison of Instance-level Counterfactual Explanation Algorithms for Behavioral and Textual Data: SEDC, LIME-C and SHAP-C. (2020). Yanou Ramon, David Martens, Foster Provost, and Theodoros Evgeniou. Advances in Data Analysis and Classification. Available online. Full pdf available here.

• Deep Learning on Big, Sparse, Behavioral Data. (2019).
  Sofie De Cnudde, Yanou Ramon, David Martens, and Foster Provost.
  Big Data, 7(4), p.286-307. Available online. Full pdf available here.

• Understanding Consumer Preferences for Explanations Generated by XAI Algorithms. (2021). Yanou Ramon, Tom Vermeire, Olivier Toubia, David Martens and Theodoros Evgeniou. Working paper available here.

Teaching

• 2025 - present: Lecturer of the course “Data Science for Business” to students of the Master in Management program at the Antwerp Management School (Belgium).

• 2022 - present: Guest lecturer “AI for Everyone: Demystifying the basics of Artificial Intelligence” at the Master’s course Business Contracts & Technology of Prof. Jan Blockx (Faculty of Law, University of Antwerp, Belgium). Find the presentation here.

• 2022-24: Ambassador of Women in Data Science. More information here.

• 2018-22: Teaching assistant of “Data Mining”, “Ethics in Data Science”, “Case studies & trends in Data Mining”, and “Data Engineering” (Major Data Science, University of Antwerp, Belgium). Responsible for the Python tutorials and the Data Science Challenge in collaboration with AXA Insurance.