Leon Ingelse

Journalism

Global Voices

For Global Voices I have participated in their Civic Media Observatory project. I collaborated on two works:

• Portugal and the myth of the good colonizer. On racial discourse in Portugal.
• If on welfare, better learn dutch and be a man. On the discourse around AI usage by the Dutch government.

Opinion pieces

In Dutch (if you wish, the built-in Google translate does quite a good job at translating):

• Voor Trouw: De 'context' kan genocide niet goedpraten.
• Voor Trouw: Angst voor ‘vrije wil’ van AI leidt af van echte problemen.
• Voor Het Parool: De emoties van ChatGPT zijn net zo echt als die van een psychopaat die compassie toont
• Voor Het Parool: Een Marokkaanse jongen die ’s avonds tegen me praat wantrouw ik eerder dan een witte jongen, jij niet?
• Voor Trouw: Kijk ook eens met de niet-westerse bril naar de oorlog.

In English:

• For Amsterdam Alternative: Open Relationsips.
• For Amsterdam Alternative: Men in Feminism and Sexual Discourse.

Data Science

Onja

Data analyst

(March 2024 - Present)

As a data analyst at Onja, I analyse candidate selection and course impact, and help with multiple data-related efforts throughout the organization. As Onja is a start-up, I am often involved in other parts of the organization.

LASIGE

Research Fellow (with Prof. Alcides Fonseca)

(Sept 2021 - Nov 2023)

I have written my thesis with Professor Alcides Fonseca on interpretable feature learning using Grammar-Guided Genetic Programming (GGGP) as part of the CAMELOT project. As part of the thesis, we build Genetic Engine, an open-source GGGP framework in Python. Afterwards, we researched GGGP-based feature learning and GGGP individual representations.

Ingelse, L. and Fonseca, A., (2023). Domain-Aware Feature Learning with Grammar-Guided Genetic Programming. EuroGP 2023 Proceedings. Springer Nature.

Espada, G., Ingelse, L., et al. 2022. Data Types as a More Ergonomic Frontend for Grammar-Guided Genetic Programming. In Proceedings of the 21st ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE 2022). Association for Computing Machinery, New York, NY, USA, 86–94. https://doi.org/10.1145/3564719.3568697

Ingelse, L., et al. Benchmarking Individual Representation in Grammar-Guided Genetic Programming. EasyChair (2022): 7821.

de Franca, F. O., et al. "Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition." arXiv preprint arXiv:2304.01117 (2023).

Research fellow (with Prof. Tiago Guerreiro)

(Feb 2021 - Aug 2021)

Worked with Tiago Guerreiro through a Master fellowship, researching gait analysis for motor impaired people. Experimented with transfer learning to personalise gait analysis to make gait analysis more inclusive.

Ingelse, Leon, et al. Personalised Gait Recognition for People with Neurological Conditions. Sensors 22.11 (2022): 3980.

Impact Institute

Research and Development

(Feb 2019 - Aug 2020)

As a Data Scientist and a Software Engineer I was part of the Research and Development team at Impact Institute. I started of as an intern, and later worked as a Project Support Officer, working with Impact Institute for a total of one and a half year. Our main achievement was the creation of the Global Impact Database.