Current Research

My research uses ideas and methods from physics, biology, and computer science to investigate the collective dynamics of honeybees.

  1. Collective Food Exchange/Distribution among Honeybees

    Division of labor, a hallmark of honeybee behavior, allows the assignment of different tasks to different individuals to improve the efficiency of the colony as a whole. An acute instance of a division of labor occurs as part of their feeding process, where some forager bees collect food and share via food regurgitation, essentially “charging” hivemates who do not have access to nearby energy sources. This process, termed trophallaxis, allows fast and efficient dissemination of nutrients and is crucial for the colony’s survival.  This behavior is not only an important feeding mechanism but also serves as a means for communication among hivemates, allowing them to distribute information about the quality of the new nectar sources or about food requirements of the brood nest. It is considered to be one of the most central features of eusociality in honeybees and is integral to their survival and growth as a colony.

    It amazes me how a group of bees manage to coordinate the complex task of food distribution with such high levels of efficiency and ensure the feeding of non-foragers and brood within the hive. In particular, think about a scenario where a bee just flies back to hive from a foraging trip and there are many mouths to feed! She faces this dilemma: should it feed another hivemate at the same spot on the honeycomb or move to feed another at a new place? This decision-making process that occurs as a result of food-exchange behavior causes a dramatic shift in the morphology of the collection of bees. Based on our series of laboratory experiments with fed/deprived honeybees we now know that initially, the individuals are distributed sparsely across the arena. After the fed bees are introduced, clusters appear. Eventually, the clusters dissipate.

    The main goal of this research is to discover the connections between the individual honeybee behavior and the collective food exchange dynamics that it produces within the hive. Furthermore, as social insects have always been a reliable source of inspiration for the design of artificial multi-agent systems, optimization algorithms, and robotics, I hope that as this research grows, our results can inspire useful solutions to problems in those fields.

2. Collective Comb Construction under Geometric Frustrations

As honeybees build their nests in pre-existing tree cavities, they must deal with dealing with the presence of geometric constraints, resulting in non-regular hexagons and topological defects in the comb. In this work, we study how bees adapt to their environment in order to regulate the comb structure. Specifically, we identify the irregularities in honeycomb structure in the presence of various geometric frustrations. We 3D- print experimental frames with a variety of constraints imposed on the imprinted foundations. The combs constructed by the bees show clear evidence of reoccurring patterns built by bees in response to specific geometric frustrations on these starter frames. Furthermore, using an experimental-modeling framework, we demonstrate that these patterns can be successfully modeled and replicated through a simulated annealing process, in which the minimized potential is a variation of the Lennard-Jones potential that only considers first-neighbor interactions according to a Delaunay triangulation. Our simulation results not only confirm the connection between honeycomb structures and other crystal systems such as graphene but also show that irregularities in the honeycomb structure can be explained as the result of local interactions between honeybees and their immediate surroundings, leading to emergent global order. Additionally, our computational model can be used to describe specific strategies that bees use to effectively solve each geometric mismatch problem while minimizing the cost of comb building.

Experimental data analysis pipeline using image processing techniques to automatically detect and characterize individual cells based on their shape and size.
The emergent patterns detected in our experimental data can be successfully replicated by a computational model dominated by local interactions.

For more details about this project please visit:

If any of these topics sound interesting to you or if you have any thoughts or ideas in similar research or just want to chat, I’d love to schedule a time and talk with you. So, either send me an email or message me on one of the social media links below.