Mees Lindeman

MSc — Artificial Intelligence

University of Amsterdam • 2024 — 2026 (Expected)

Covers advanced topics in machine learning, deep learning, natural language processing, information retrieval, reinforcement learning, and AI ethics.

BSc — Bèta Gamma, major Artificial Intelligence

University of Amsterdam • 2019 — 2024

GPA: 7.5/10

Gained a strong foundation in logic, calculus, linear algebra, programming, and basic machine learning.

High School Diploma

Jac. P. Thijsse College • 2017

IT Developer

Amsterdam University of Applied Sciences • Sept, 2023 — Present

Explored opportunities to integrate AI within the department by contributing to research initiatives and small-scale implementations. Developed a Microsoft Azure-based chatbot to assist employees with frequently asked questions and provided ongoing AI consultancy. To support awareness and adoption, I initiated and led the "AI for Dummies" workshop series, making key AI concepts accessible to all colleagues. Additionally, I built simple, functional web tools to help team members prototype and present their AI-driven ideas.

Student Assistant

Amsterdam University of Applied Sciences • Oct, 2022 — Sept, 2023

Conducted statistical research and provided IT support for student platforms. Assisted with small research projects and helped integrate AI solutions within the department, leading to a promotion to a more strategic role.

On the Optimality of Discrete Object Naming: a Kinship Case Study

Phong Le, Mees Lindeman, Raquel G. Alhama • EMNLP • 2025

Conference Paper (Under Review)

Presented a novel information-theoretic framework for discrete naming systems, relaxing assumptions of optimal listeners and universal communicative need. Theoretical and empirical results—using kinship semantics and referential games—demonstrate that optimal trade-offs between informativeness and complexity emerge when listener and speaker models align.

Reassessing Fairness: A Reproducibility Study of NIFA's Impact on GNN Models

Mees Lindeman, Ruben Figge, Sjoerd Gunneweg, Aaron Kuin • TMLR • 2025

Journal Article (Accepted)

Selected for inclusion in the ML Reproducibility Challenge 2025. This study evaluates the claims and results of Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections. Assessed implementation, experimental setup, and generalizability to provide insights into the robustness of the original findings.

Enhancing Few-Shot Robotic Manipulation by Replacing Depth Sensors with Learned 3D Geometry from MASt3R

Deep Learning 2 Course Project • 2025

Co-authored with Ruben Figge, Sjoerd Gunneweg, and Jurgen de Heus

Investigated whether learned 3D geometry from RGB images can replace depth sensors in few-shot robotic manipulation. Integrated MASt3R, a state-of-the-art 3D reconstruction method, into the RVT-2 robotic vision pipeline to evaluate performance without RGB-D sensors. Demonstrated that learned geometry can enable robust manipulation under challenging visual conditions. Possibly offering a more flexible, cost-effective alternative to traditional calibrated setups.

Experience: Python, PyTorch, Docker, Singularity

AI for Dummies

Creator & Instructor • 2025 — Present

AI for Dummies is an educational initiative I started to explain AI concepts to beginners. Sparked by growing interest and common misconceptions observed during my work at HvA, the project aims to deliver intuitive lessons and workshops on topics like machine learning, probability, and generative AI.

Experience: Node.js, React, Python

Exploring Language Compositionality in Multi-Agent Systems

Bachelor Thesis • 2024

Grade: 8.0/10

Investigated the emergence of compositional language in referential games between artificial agents, using Graph Neural Networks (GNNs) to model structured input. Explored how agents develop communication strategies to describe family relations, analyzing the impact of different training setups on language expressiveness and efficiency.

Experience: Python, PyTorch

Programming

Python (i.e. NumPy, PyTorch, scikit-learn, OpenCV, Matplotlib, Pandas...), HTML, CSS, SQL

AI Domains

Machine Learning, Deep Learning, NLP, Computer Vision, Reinforcement Learning

Language A: language and literature

International Baccalaureate • 2017

Earned as part of the International Baccalaureate program, recognizing advanced skills in literary analysis and communication.

Certificate in Advanced English (CAE)

Cambridge English • 2014

Achieved through Cambridge English, demonstrating proficiency in academic and professional English at an advanced level.

Semester High School

Iowa, USA • 2017

During a gap year in my studies, I attended a semester of High School in the United States, where I had the opportunity to enhance my English skills and embrace diverse experiences and cultures.