I’m a PhD student at the VU University Amsterdam. My research focuses on combining symbolic reasoning and machine learning, or “Neuro-Symbolic AI”. I’ve done work on differentiable fuzzy logics, and on optimization with discrete latent variables. I developed the Storchastic PyTorch library, which implements many gradient estimation methods.
I expect to finish my PhD July 2023 and am looking for opportunities. Contact me at info@emilevankrieken.com if interested.
I’m also interested in Personal Knowledge Management. I’ve developed Juggl, a plugin for Obsidian.md that adds a useful and customizable graph view. Other plugins include Graph Analysis, which uses graph theory algorithms to find similarities between notes, and Supercharged Links.
Another hobby of mine is music. Here are some songs I made!
Selected publications
- Emile van Krieken, Erman Acar, and Frank van Harmelen. “Analyzing differentiable fuzzy logic operators.” Artificial Intelligence 302 (2022)
- Emile van Krieken, Jakub Tomczak, and Annette Ten Teije. “Storchastic: A Framework for General Stochastic Automatic Differentiation.” Advances in Neural Information Processing Systems 34 (2021).
- Alessandro Daniele*, Emile van Krieken*, Luciano Serafini, and Frank van Harmelen. “Refining neural network predictions using background knowledge” (joint first authors, preprint)
Latest news
- Attended UAI 2022, and was a discussant for this great paper
- Gave a talk on ‘Policy Gradient’ at the Reinforcement Learning Summer School 2022
- Attended HHAI 2022.
- Presented “Analyzing differentiable fuzzy logic operators” at the KR4HI 2022 workshop.
- New preprint: “Refining neural network predictions using background knowledge”.
- Attended AI in Bergen Summer School on Knowledge Graphs in Machine Learning. Presented “Bridging the Discrete-Continuous gap in Neuro-Symbolic AI“, which got the best presentation award.
- Attended BeNeRL 2022 and presented a poster on Storchastic.
- Part of visitation committee of the Computer Science department of VU Amsterdam.
- “Analyzing differentiable fuzzy logic operators” is officially published in Artificial Intelligence Journal.
- Presented “Storchastic: A Framework for General Stochastic Automatic Differentiation” at NeurIPS 2022.
- Teaching Assistant for DLVU (Deep Learning @ VU) course, gave two lectures on Reinforcement Learning.