My main research interests are Bayesian statistics, Reinforcement Learning (Inverse RL, Deep RL) and Brain-inspired AI.
I read papers following Andrew Ng's invaluable tips on How to read research papers (Andrew NG)

Publications

• A Hybrid-AI approach to Competence Assessment for Automated Driving Functions, Jan-Pieter Paardekooper, Mauro Comi, Corrado Grappiolo, Ron Snijders, Willeke van Vught, Rutger Beekelaar, SafeAI AAAI 2021

Talks and Lectures

• A Hybrid-AI approach to Competence Assessment for Automated Driving Functions @SafeAI AAAI, February 2021
• Guest Lecturer, Anomaly Detection using Machine Learning @Jheronimus Academy of Data Science, July 2021

Non-exhaustive list of papers I like

• Maximum Entropy Inverse Reinforcement, Ziebart et al. [My summary notes] [Paper]
• Bayesian decision theory in sensorimotor control, Körding et al. [My summary notes] [Paper, not open access]
• PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection, Shi et al. [Paper]
• Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning, Jacques et al. [Paper]
• Learning Light Transport the Reinforced Way, Dahm et al. [My implementation][Paper]
• Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science, Mocanu D.C et al. [My summary notes][Paper]
• ..... work in progress .....