PyData Amsterdam

Scheduling at Scale: Inside Timetable Optimization & Operational Flight Models

12 mrt 2026 17:30 CET

Lynxx office, Stationsplein 61, Amersfoort, NL

⚠️ Important Note:
PyData Amsterdam is transitioning to Luma. ✨

• Please subscribe to PyData Amsterdam on Luma: https://luma.com/pydataamsterdam
• And register at the Luma registration link for this event: https://luma.com/ref9worx

---------------------------------------------------------------------------------

Join us at PyData Amsterdam’s very first meetup in Amersfoort!
We are excited to announce our next meetup on Thursday, March 12, 2026, hosted by Lynxx, near Amersfort Central Station! This edition dives deep into Operations Research and large-scale optimization algorithms. We will explore how complex logistical puzzles, from railway disruptions to aircraft assignments, are solved using mathematical models and Python.

This evening goes beyond the textbook basics of mixed linear programming. We’re diving into the engineering challenges of building solvers in Python that are fast, robust, and capable of handling massive scale. From creating alternative timetables to allow for railway maintenance to solving the complex puzzle of aircraft assignments, you’ll discover the architectural decisions—and the necessary trade-offs—that turn raw mathematical models into operational efficiency.

Thrilled as well?! We'd love to welcome you for an evening full of knowledge sharing, deep technical dives, and, of course, great conversations, networking, and a fun evening with the community!

Agenda

• 17:30 - 18:25: Walk-in with drinks & food (🍕/🍻)
• 18:25 - 19:15: Talk 1 - RAAD - A timetable optimizer build in Python,
  by Merel Groen & Robin Weber
• 19:15 - 19:30: Break
• 19:30 - 20:15 Talk 2: The optimisation engine behind tail assignment, by Tessa van Kleef
• 20:15 - 21:00: Networking + drinks & bites 🍻

Talk 1
RAAD: a Timetable optimizer build in Python, by Merel Groen & Robin Weber

RAAD is an optimization tool developed by Lynxx for ProRail. RAAD can quantify the impact of service disruptions on the railway network and the consequences for an alternative timetable, balancing between the different railway operators. In this presentation, we will further elaborate on the developed codebase, with special attention to the class structure, performance, solver integration, and more!

Merel Groen is a Data Scientist at Lynxx, where she specializes in optimizing operational processes through analytical problem-solving. She joined the team in 2023, starting her journey by writing her Master's thesis in Econometrics on industry-relevant challenges. At Lynxx, she leverages her background to dive into complex logistical details, translating theoretical models into practical solutions for rail and transport networks.

Robin Weber is a Data Scientist at Lynxx, where he specializes in shaping and delivering complex Operations Research projects. Before joining Lynxx in 2022, he worked as an Operations Research Scientist at Dassault Systèmes. With a strong academic background in Econometrics and Operations Research from the University of Groningen, Robin combines deep theoretical knowledge with practical application to solve intricate logistical puzzles.

Talk 2
The optimization engine behind tail assignment, by Tessa van Kleef

In this session, I’ll show how a mathematical optimization model transforms the complex tail assignment puzzle, i.e., deciding which aircraft operates which flight, into a fast, automated, and robust process. By integrating operational constraints, maintenance needs, and fuel considerations, the model delivers more efficient schedules while saving planners significant time.

Tessa van Kleef is an Operations Research Specialist at Transavia, where she focuses on optimizing airline logistics and planning. With a strong background in mathematics and aviation, she specializes in solving complex operational puzzles like aircraft tail assignment. Her work bridges the gap between theoretical models and real-world flight schedules, ensuring efficiency and robustness in daily airline operations.

Directions

Address: Stationsplein 61, Amersfoort
We are located directly inside the station building!

1. Head to the main hall: Look for Bakker Bart and Smullers.
2. The entrance: Keep both shops on your right-hand side. Our entrance (number 61) is one of the first doors on the right as you walk outside toward the bus station.
3. To the 3rd floor: Take the elevator or stairs up to the 3rd floor.

By car? The following P+R garages are the most convenient and lead directly to the station:

• Barchman Wuytierslaan
• Piet Mondriaanplein

Applied AI Stories: Solving Cold Cases and Powering Product Search

26 mrt 2026 17:30 CET

ML6 Amsterdam, Geldersekade 101e, Amsterdam, NH, NL

⚠️ Important Note:
PyData Amsterdam is transitioning to Luma. ✨

• Please subscribe to PyData Amsterdam on Luma: https://luma.com/pydataamsterdam
• And register at the Luma registration link for this event: https: https: //luma.com/bm0o8s4r

—————————————————————
—————————————————————

PyData Amsterdam is back with another evening of inspiring talks, food, and networking on Thursday the 26th of March, from 17:30 until 21:30 CET at ML6 in Amsterdam! Join us for an exciting meetup where we explore how modern machine learning systems are being applied to real-world problems at scale - from cold case investigations to large-scale e-commerce search. We’ll kick off with drinks and networking, followed by two in-depth technical talks from industry practitioners, with plenty of time to connect over food and drinks afterwards.

Schedule

• ​17.30 – 18.00: 🥤 Welcome & Networking
• ​18.00 – 18.15: ML6 / HR introduction
• ​18.15 – 19.00: 🎤 Talk 1 – Can AI agents help solve Europe’s Largest Cold Case? - By Titus Naber
• ​19.00 – 20.00: 🍕 Food & Networking
• ​20.00 – 20.45: 🎤 Talk 2 – From Keywords to Concepts: A Late Interaction Approach to Semantic Product Search on IKEA.com - By Amritpal Singh
• ​20.40: 21.30: 🥤 Drinks & Networking​

## Talk 1: Can AI agents help solve Europe’s Largest Cold Case?

​by Titus Naber
​The Olof Palme investigation lasted 34 years and resulted in one of the largest police archives in modern European history. Even reading the digitized portion alone would take a single person years.
​So what happens when you give that archive to AI agents?
​In this talk, Titus will walk us through the design and deployment of a Deep Research agent system capable of autonomous hypothesis generation, iterative search, and cross-referencing across thousands of police documents. The session will dive into how AI agents can be orchestrated to explore massive, unstructured datasets and support complex investigative workflows.

​Bio
Titus Naber is a Machine Learning Engineer at ML6, based in Amsterdam. He holds an MSc in Artificial Intelligence from Delft University of Technology and has experience across machine learning, full-stack development, and autonomous systems. Previously, he worked at Adyen on open-source payment integrations and was involved in autonomous racing through the Formula Student team. Titus combines strong academic foundations with hands-on industry experience to build practical, high-impact ML systems.
​

## Talk 2: From Keywords to Concepts: A Late Interaction Approach to Semantic Product Search on IKEA.com

​by Amritpal Singh
​Traditional keyword-based search struggles to capture the rich, multi-attribute intent behind customer queries. In this talk, Amritpal will present IKEA’s journey from keyword matching to concept-based semantic search using a late-interaction retrieval model.
​Instead of relying on Boolean logic or single-vector embeddings, this approach uses token-level scoring to preserve keyword specificity while capturing fine-grained intent. The system is trained using large-scale synthetic query generation, strong negative mining, and adaptive ranking — all while maintaining low latency at production scale.
​Deployed live on IKEA.com across multiple markets and languages, the new system outperformed the existing Boolean search, delivering meaningful gains in relevance, engagement, and commercial impact, especially for long-tail queries.

​Bio
Amritpal Singh is a Senior Data Scientist at IKEA Amsterdam with over 10 years of experience in machine learning, computer vision, and NLP. Previously, he was a Staff Data Scientist at Huawei and a Senior Research Engineer at HERE Technologies. He holds an MSc in Embedded Systems from TU Delft and a BTech in Electrical Engineering from Vellore Institute of Technology.
​

We look forward to welcoming you for an evening of cutting-edge ML, practical insights, and great conversations - see you there!