In the rapidly evolving fields of data science and AI, efforts often focus on building complex models and working with large datasets. Our speakers propose a different approach: one that emphasizes a purpose-driven mindset that starts not with data, but with the underlying business challenges and research questions.

Drawing on real-world experience, this perspective highlights a common issue in many data initiatives: the gap between technically advanced solutions and the actual needs of organizations.

This talk explores practical frameworks and strategies, offering key lessons and recommendations on identifying the right problems, fostering effective collaboration, and ensuring that data efforts lead to meaningful, responsible, and ethical impact. By treating data not as an end, but as a means to solve defined challenges, this session aims to inspire a more strategic, value-oriented approach to data science.

Program AI Meet-Up

Date: 11 September 2025

Location: AI Innovation Center, High Tech Campus Eindhoven (HTC 5)

Program:

15:30 – 16:00 Walk-in

16:00 – 16:15 Welcome and update AI Innovation Center by Philipp Werle (HTCE)

16:15 – 17:00 Talk and Q&A by Juan Manuel González Huesca (ASML) and Mykola Pechenizkiy (TU/e)

17:00 – 17:15 Open mic

17:15 – 18:30 Drinks and networking

Our speakers

Juan Manuel González Huesca is a Senior Data Science and ML Architect at ASML who leverages AI to unlock business value, driving innovation through strategic, impact-focused solutions. He brings together deep technical expertise with business analysis and project management to bridge the gap between technical teams and real-world business challenges and user needs. Beyond his professional work, Juan is an author, speaker, and passionate triathlete who also enjoys giving back through volunteering.

Mykola Pechenizkiy is Professor at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). He is Director of the Center for Safe AI, where he leads research on addressing technical and socio-technical challenges of AI reliability, AI fairness, and AI compliance. He holds the Data Mining chair that is part of the Data and AI cluster.