About me

Welcome to my website where I provide an overview of my scientific work and photography. I am Dutch and currently work at Delft University of Technology. I have a background in human-computer interaction, visual perception, and computational neuroscience. The last couple of years my research has been moving more towards computational neuroscience where I use various machine learning techniques to create models of our visual system. In my research I try to understand how we visually estimate properties of objects and stuff around us.

Next to my research I really enjoy photography. I lived two years in Japan where I started using a medium format analog camera, a selection of my work can be found at the photography section.

If you have any questions please don’t hesitate to contact me at mail [at] janjaap [dot] info.

Collective Flow

In 2020 I received a Marie Curie Individual Fellowship for the project ‘Our Elemental Sense of Collective Flow’. In September 2020 I started this project at the Perceptual Intelligence Lab at Delft University of Technology.

Collective flow consists of bodies of individual entities or agents that show both collective and individual behaviours following a coordinated set of rules. There are inanimate occurrences of collective flow (e.g., shaken metallic rods, nematic fluids), microscopic occurrences (e.g., macromolecules, cells, bacteria colonies), and richer manifestations with more intelligent organisms (e.g., insect swarms, flocks of birds, humans).

It is really impressive how we are able to perceive a wide range of behaviours from even very abstract motion patterns. To investigate this I build an online simulator for my experiments. This simulator can show a wide variety of collective flow behaviours. You can check the online simulator here: www.janjaap.info/flow. This project is ongoing and hope to update this section regularly in the upcoming months.

Optical Flow

Together with Shin’ya Nishida from Kyoto University I investigate the influence of optical flow on perceived motion constancy across different optical material properties. This is a tough computational problem under real-world conditions because retinal optical flow drastically changes with the optical material properties of the moving object. Specular and diffuse reflections, as well as refractions at object surfaces can produce complex patterns of optical flow that do not correspond with the object motions. (more info will follow soon)


During my PhD at the department of Experimental Psychology at the Justus-Liebig-Universität Gießen I studied the visual perception of deformable materials. Supervised by Roland Fleming I studied how our visual system estimates the viscosity of liquids. I continued this work with Shin’ya Nishida at NTT Communication Science Laboratories in Japan. Here I researched how we estimate material properties using machine learning techniques that are inspired by our visual system.

The studies

Fluids and other deformable materials have highly mutable shapes, which are visibly influenced by both intrinsic properties (e.g. viscosity, velocity) and extrinsic forces (e.g. gravity, object interactions). How is it possible that we achieve good viscosity constancy despite large differences in the retinal image? In my research I try to identify the image cues we use to estimate viscosity. We found that mid-level features (e.g. ripples, clumping, spread, piling up) are very important to achieve viscosity constancy across a wide range of contexts. It is hard to quantify these features with image statistics or 3D shape measurements because they can vary in orientation and scale, and occur locally on liquid shapes. Therefore we are now concentrating on deep learning models and try to learn more about higher order image-based feature extraction.

The stimuli

During my PhD I spend quite some time setting up a technical pipeline to generate stable, precise and realistic liquid stimuli. To be able to do this I followed a three month secondment in Madrid at Next Limit. Here I learned to work with their particle simulation software RealFlow made for the VFX industry. The stimuli were rendered using Maxwell Render. The computational costs of the stimuli are quite high. I wrote specific scripts to distribute the calculations over various systems and clusters. In 2018 I became a certified Realflow Xpert.


For my Master degree I graduated on a research project studying gloss perception. The actual research took place in the Perceptual Intelligence Lab at Delft University of Technology with Sylvia Pont and Maarten Wijntjes.

The study

We investigated the influence of the spatial structure of the illumination on gloss perception. The inspiration came from various art works like the paintings of Vermeer, where much simpler highlight shapes are used to depict real world situations. We find that more complex highlight shapes were perceived to produce a less glossy appearance than simple highlight shapes such as a disk or square. These results show that, contradictory to some beliefs, the complexity of a highlight shape’s spatial structure alone is not the main criterion for increases in perceived glossiness.

The stimuli

A diffuse light box in combination with differently shaped masks were used to produce a set of 6 simple and more complex highlight shapes. In the box we placed spherical stimuli that were painted in 6 degrees of glossiness. This resulted in a stimulus set of 6 highlight shapes and 6 gloss levels, a total of 36 stimuli. Observers were asked to rate glossiness looking at the real scene in the light box, but we also performed experiments with photographs of the stimuli displayed on a monitor. The figure below shows a subset of the stimuli with the six different masks that were tested.

stimuli selectie



2020 Jan Jaap R. van Assen, Shin’ya Nishida, & Roland W. Fleming. Visual perception of liquids: Insights from deep neural networks. PLoS Computational Biology 16(8): e1008018. doi:10.1371/journal.pcbi.1008018. [PDF]

2018 Jan Jaap R. van Assen, Pascal Barla, & Roland W. Fleming. Visual features in the perception of liquids. Current Biology, 28(3), 452-458, doi:10.1016/j.cub.2017.12.037. [PDF]

2017 Filipp Schmidt, Vivian C. Paulun, Jan Jaap R. van Assen, & Roland W. Fleming. Inferring the stiffness of unfamiliar objects from optical, shape, and motion cues. Journal of vision, 17(3), 18-18, doi:10.1167/17.3.18. [PDF]

2017 Vivian C. Paulun, Filipp Schmidt, Jan Jaap R. van Assen, & Roland W. Fleming. Shape, motion, and optical cues to stiffness of elastic objects. Journal of vision, 17(1), 20-20, doi:10.1167/17.1.20. [PDF]

2016 Jan Jaap R. van Assen, & Roland W. Fleming. Influence of optical material properties on the perception of liquids. Journal of vision, 16(15), 12-12, doi:10.1167/16.15.12. [PDF]

2016 Jan Jaap R. van Assen, Maarten W. A. Wijntjes, & Sylvia C. Pont. Highlight shapes and perception of gloss for real and photographed objects. Journal of Vision, 16(6):6, 1–14, doi:10.1167/16.6.6. [PDF]

Please contact me for a full CV at mail [at] janjaap [dot] info or by LinkedIn.


Since my arrival in Japan I switched to analog photography using a medium format camera (Mamiya 7). Below you will find a selection of my photos.

More work can be found on Behance. I recently switched to Behance and I will gradually add more work.