Amsterdam AI Meetup: Times-Series for Formula1; Tackling Uncertainty in Medical AI

by Amsterdam AI


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Amsterdam AI Meetup: Times-Series for Formula1; Tackling Uncertainty in Medical AI

After a year intermission, Amsterdam AI is back in business and will be bringing some AI applications to their next meetup. During this event, they'll explore the world of Time-Series, covering the challenges of processing real-time data in some of the most demanding markets: Formula 1, Space, and Banking. 

They'll finish the evening with an exploration into the results of a young startup from Amsterdam applying AI in the dentistry world, offering a glimpse into the other side of the spectrum.  


  • 18:00 - 18:30: Registration and Networking 
  • 18:30 - 18:40: Sean Lang, Global Business Development, Kx - "The Need for Speed" 
  • 18:40 - 19:00: Conor McCarthy, Machine Learning Engineer, Kx - "Analyzing Social Media Data for Disaster Management using kdb+" 
  • 19:00 - 19:10: Short Break 
  • 19:10 - 19:30: Teo Cherici, Data Scientists and Deep Learning Engineer, Promaton 
  • 19:30 - 19:40: Closing remarks led by Amsterdam AI Meetup Group 
  • 19:40 - 20:10: Networking, light bites and beer!


  • Sean Lang is the Business Development Lead for Kx in the Benelux region. He's in charge of introducing the power of Kx software to leading manufacturers, energy operators, and telecommunications firms here in the Netherlands. Abstract for The Need for Speed: Sean will be introducing you to various use cases paying particular attention to the work Kx are doing with the Red Bull Racing team and their leading driver, Max Verstappen.
  • Conor McCarthy is a Machine Learning Engineer for Kx with a background in experimental physics. He works primarily on the design of highly scalable machine learning pipelines and architectures alongside the production of interfaces between q/kdb+ and complimentary systems and languages including R, Python, Java, and Kafka. Conor's talk Analyzing Social Media Data for Disaster Management using kdb+ will concern the production of an ML algorithm for the classification of disaster-related tweets as well as the use of publicly available libraries to create a demonstration of a live scoring system built on kdb+. This is work Conor has recently carried out on behalf of the European Space Agency, and NASA's Frontier Development Labs.
  • Teo Cherici is a Data Scientist and Deep Learning Engineer at Promaton. Born and raised in Rome, Italy, Teo Cherici moved to the Netherlands to study Mechanical Engineering at the Delft University of Technology. His interest for robotics and machines has grown over the years into a passion for neural networks and Machine Learning. After learning the basics and theory at university, Teo started deploying practical deep learning solutions during his internship at Accenture, and completed his Master's degree with a thesis on Deep Reinforcement Learning. 
  • Promaton is bringing AI to the dental industry, turning imaging data into clinical insights and enabling dental companies to deliver revolutionary new products to dentists. The results and suggestions of an automated tool must however be trustworthy to a human professional if the tool is to be used effectively, more than ever when dealing with medical data. To achieve this trust, models must be able to "fail gracefully," that is to know when they do not know. In this talk, Teo Cherici will present the application of Bayesian Neural Networks for dental image analysis.