I’m a PhD student at the Vrije Universiteit Amsterdam. My research combines symbolic reasoning and machine learning, or “Neurosymbolic 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. Recently, I’ve developed A-NeSI, a highly scalable Neurosymbolic method that uses neural networks for symbolic inference.
We are organizing an exciting workshop on Neurosymbolic Generative models at ICLR 2023! Join us in Kigali.
I expect to finish my PhD in July 2023 and am looking for opportunities as a postdoc or a researcher in industry. I have a preference for positions in or close to the Netherlands. 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 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).
- Emile van Krieken, Thiviyan Thanapalasingam, Jakub Tomczak, Frank van Harmelen, and Annette ten Teije. “A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference” (preprint)
- Alessandro Daniele*, Emile van Krieken*, Luciano Serafini, and Frank van Harmelen. “Refining neural network predictions using background knowledge” (joint first authors, to appear in Machine Learning Journal)
Latest news
- We are organizing the NeSy-GeMs workshop at ICLR 2023
- Attended IJCLR 2022 and presented our paper with Alessandro Daniele
- Our team won the ISWC 2022 LM-KBC challenge
- Attended UAI 2022 and was a discussant for this great paper
- Gave a talk on ‘Policy Gradient’ at the Reinforcement Learning Summer School 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.
- “Analyzing differentiable fuzzy logic operators” is officially published in Artificial Intelligence Journal.
- Presented “Storchastic: A Framework for General Stochastic Automatic Differentiation” at NeurIPS 2022.