Welcome!

I am a post-doctoral researcher at the department of physics of Gothenburg University , in Sweden. My research is focused on active matter physics. More specifically, I study the optimal navigation strategies of microswimmers, such as Plankton in turbulent flows.

In my doctoral studies, I found mechanisms which enable microswimmers with weak steering abilities to travel faster than their propulsion speed by exploiting the background turbulent flow [1]. Furthermore, I found strategies that such swimmers can benefit, in order to avoid regions of the flow with high strain, which led to observation of emergent counter-current swimming behavior [2]. For a public press article you can check out here , and for detailed description of the results and methods, feel free to look into my thesis here .

To solve such problems, one needs to employ methods from fluid physics, dynamical systems, and statistical physics. Recently, machine learning algorithms, such as reinforcement learning [3], have shown great potential in addressing active matter physics problems [4,5], including the navigation challenges I work on. Therefore, I utilize various machine learning techniques to build mathematical models of smart swimmers and study the optimal behaviors these swimmers can adopt to achieve different survival goals.

 

You can read more about me on About me page and see my academic background and activities in CV . Feel free to contact me for any questions :-).

 

 

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[1] Navigation of micro-swimmers in steady flow: The importance of symmetries
Journal of Fluid Mechanics 932 (2022): A10
[2] Efficient survival strategy for zooplankton in turbulence
Physical Review Research 6 (2), L022034
[3] Reinforcement learning: An introduction MIT press, 2018
[4] Machine learning for active matter
Nature Machine Intelligence 2 (2), 94-103
[5] Flow navigation by smart microswimmers via reinforcement learning
Physical review letters 118 (15), 158004