Davide Zambrano | EPFL Graph Search

I am a senior postdoc in the Laboratory of Intelligent Systems of the Ecole polytechnique fédérale de Lausanne (EPFL). My research interests are in the fields of spiking neural networks, neuromorphic computing, deep learning and reinforcement learning. I received my M.Sc. degree (cum laude) in biomedical engineering and my Ph.D. degree in health technologies from the University of Pisa, Pisa, Italy, in 2008 and 2012, respectively. I worked as a Ph.D. and postdoc at The BioRobotics Institute of The Scuola Superiore Sant Anna, Pisa, Italy. From 2014 to 2018 I have worked as postdoc at the Machine Learning department of the Centrum Wiskunde and Informatica (CWI) in collaboration with Prof. Sander Bohte on spiking neural networks, biological plausible reinforcement learning models.

Autonomous Detection and Deterrence of Pigeons on Buildings by Drones

Dario Floreano, Dominik Natter, Fabrizio Schiano, Davide Zambrano

Pigeons may transmit diseases to humans and cause damages to buildings, monuments, and other infrastructure. Therefore, several control strategies have been developed, but they have been found to be either ineffective or harmful to animals and often depend on human operation. This study proposes a system capable of autonomously detecting and deterring pigeons on building roofs using a drone. The presence and position of pigeons were detected in real time by a neural network using images taken by a video camera located on the roof. Moreover, a drone was utilized to deter the animals. Field experiments were conducted in a real-world urban setting to assess the proposed system by comparing the number of animals and their stay durations for over five days against the 21-day- trial experiment without the drone. During the five days of experiments, the drone was automatically deployed 55 times and was significantly effective in reducing the number of birds and their stay durations without causing any harm to them. In conclusion, this study has proven the effectiveness of this system in deterring birds, and this approach can be seen as a fully autonomous alternative to the already existing methods.

2022

How to compete with robots by assessing job automation risks and resilient alternatives

Dario Floreano, Rafael Lalive, Nicola Nosengo, Antonio Paolillo, Fabrizio Schiano, William John Stewart, Davide Zambrano

The effects of robotics and artificial intelligence (AI) on the job market are matters of great social concern. Economists and technology experts are debating at what rate, and to what extent, technology could be used to replace humans in occupations, and what actions could mitigate the unemployment that would result. To this end, it is important to predict which jobs could be automated in the future and what workers could do to move to occupations at lower risk of automation. Here, we calculate the automation risk of almost 1000 existing occupations by quantitatively assessing to what extent robotics and AI abilities can replace human abilities required for those jobs. Furthermore, we introduce a method to find, for any occupation, alternatives that maximize the reduction in automation risk while minimizing the retraining effort. We apply the method to the U.S. workforce composition and show that it could substantially reduce the workers’ automation risk, while the associated retraining effort would be moderate. Governments could use the proposed method to evaluate the unemployment risk of their populations and to adjust educational policies. Robotics companies could use it as a tool to better understand market needs, and members of the public could use it to identify the easiest route to reposition themselves on the job market.

2022

Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention

Davide Zambrano

Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of precision (internal noise suppression) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neuron's input or on its outgoing connection. We show that modulating the connection is most effective in selectively enhancing information processing by redistributing spiking activity and by introducing additional task-relevant information, as shown by representational similarity analyses. Precision only produced minor attentional effects in performance. Our results, which mirror empirical findings, show that it is possible to adjudicate between attention mechanisms using more biologically realistic models and natural stimuli.

MIT PRESS2022