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Department of Zoology



  • 2021- : Postdoctoral Research Associate, Marine Behavioural Ecology Group, Department of Zoology, University of Cambridge, UK
  • 2020-2021: Postdoctoral Forschungskredit Grantee, using accelerometers to study foraging strategies in the Kalahari meerkat, Population Ecology Group, Universität Zürich
  • 2020: PhD, methods to quantify animal behaviour and energetics using body-borne sensors, École polytechnique fédérale de Lausanne (Swiss Federal Institute of Technology; Laboratory of Movement Analysis and Measurement, in collaboration with the Population Ecology Group, Universität Zürich), Switzerland
  • 2015: Visiting Student Researcher, meshing algorithms to construct 3D tissue models from MR and CT images (co-developer of the software TiProScope), Stanford BioMotion Lab, Department of Mechanical Engineering, Stanford University, USA
  • 2015: MSc Bioengineering (minor in Biomedical Technologies), École polytechnique fédérale de Lausanne (Swiss Federal Institute of Technology), Switzerland
  • 2014: Research Intern, analysis of cartilage properties using MRI, Swiss BioMotion Lab, Centre hospitalier universitaire vaudois (Lausanne University Hospital), Switzerland
  • 2012-2013: Research Assistant, physics of hydrodynamic fluid focusing in microchannels for lab-on-a-chip diagnostic applications, Microfluidics Lab, Department of Mechanical Engineering, Indian Institute of Technology Bombay
  • 2012: BTech (Hons) Chemical Engineering, Indian Institute of Technology Bombay, India


Bioinspiration for efficient mobile sensor networks

How can mobile sensor networks be designed to be reliable and energy-efficient in noisy environments? This research aims to learn from the strategies fish - individually and in shoals - use to sense their environment and communicate between other members of the shoal when their sensory capacities are affected by noise.
I am currently building a data-driven, noise-dependent 'perception function' (see image below) that will quantify the effect of underwater caustics - shifting patterns of light and dark regions formed by light refracting through a wavy water surface - on the ability of fish to detect food.


In the later stages of the project, I will combine this perception function with social interaction rules to quantify and analyse fish shoals' communication networks and sensing strategies. The final step would be to integrate the lessons we learn from fish into design protocols to make human mobile sensor networks more robust.


Key publications: 

Chakravarty, P., Cozzi, G., Dejnabadi, H., Léziart, P. A., Manser, M., Ozgul, A., & Aminian, K. (2020). Seek and learn: Automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning. Methods in Ecology and Evolution, 11(12), 1639-1651.

Chakravarty, P., Maalberg, M., Cozzi, G., Ozgul, A., & Aminian, K. (2019). Behavioural compass: animal behaviour recognition using magnetometers. Movement ecology, 7(1), 1-13.

Chakravarty, P., Cozzi, G., Ozgul, A., & Aminian, K. (2019). A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods in Ecology and Evolution, 10(6), 802-814.

Other publications: 

Tripathi, S., Chakravarty, P., & Agrawal, A. (2014). On non-monotonic variation of hydrodynamically focused width in a rectangular microchannel. Current Science, 1260-1274.

Research Associate
Dr Pritish Chakravarty

Contact Details

F25 / F27
01223 (7)67130