Seminar Details
2020-10-27 (14:00) : Welcome Seminar for new PhD students (#1)
At MS Teams
Organized by Mathematical Engineering
Section 1: Controlling COVID-19: Labor structure more important than lockdown policy
Speaker :
Isabella Loaiza
Abstract :
The government response to the COVID-19 pandemic in Colombia has been characterised by the closure of international borders and strict lockdown measures. In spite of a dictated nationwide lockdown, the implementation of this lockdown has been administered by municipal governments. Thus, lockdown measures vary across municipalities. Different measures allow residents to go out for essential shopping based on the last digit of their national ID card, their gender or a mix of both. This creates an opportunity to gain insight into the effects of different lockdown policies on urban mobility via this natural experiment.
We investigate the extent to which different policies have affected residents' mobility and compare pre and post lockdown disruption of commute patterns. While we find that residents of municipalities with more severe policies had, on average, greater reductions in trip frequency during lockdown, the effect of policy severity is only about half that of city size - residents of larger cities. We also find that policy severity had no bearing on the extent to which residents reduced their daily travel distance during lockdown - nor on the likelihood that residents abandoned their established commuting patterns. However, we do find that municipalities with higher GDP, larger populations, and higher labor formality have experienced disproportionately greater reductions in mobility and greater reductions in commuting patterns. Interestingly, this difference in mobility reduction across cities of varying size/wealth is more pronounced than the difference within cities based on neighborhood socioeconomic stratum. To validate our results derived from CDR data we use google mobility data and find strong correlation with our original results.
Section 2: Multisensory Integration in Dynamic Environments
Speaker :
Hoffmann, Anne
Abstract :
In my project I am investigating how the brain integrates information from different sensory modalities in a dynamic environment using a combination of reaching experiments and computational modeling. Human motor proficiency results from an intricate interaction between sensory and motor systems in the brain. The nervous system plans and controls movements using vision, proprioception, and tactile sensors. Being able to combine sensory information about the body and the environment in real-time is essential for executing fast, adaptable, and accurate movements in the presence of uncertainty and external disturbances. Today, most of our knowledge about multisensory integration is based on experiments in which the sensory input is constant over time. These static cue-combination tasks have shown that humans optimally integrate information from different sensory modalities to form a coherent percept of their body and the environment. However, when we move, the position of our body is constantly changing and sensory organs provide the brain with disparate and asynchronous information. How may the human brain resolve these computational challenges? To address this important question, I am conducting a series of psychophysical experiments on healthy adults in which they perform arm movements while encountering various visual and force perturbations. My empirical results will be interpreted in the context of Optimal Feedback Control theory. Developing a computational explanation of dynamic multisensory integration will have important implications for our fundamental understanding of the sensorimotor system, as well as for clinical applications. Indeed, most of our everyday behavior requires coordinated motor actions and deficits in multisensory integration may lead to biased or unreliable perception of body and environment, which ultimately impairs motor performance.
Section 3: Automatic Performance Estimation for Decentralized Optimization
Speaker :
Colla, Sébastien
Abstract :
Decentralized optimization consists in having interconnected computers optimizing a function f collaboratively, by iteratively updating estimates of the optimal solution. It is particularly relevant in machine learning applications where f depends on datasets too large to be stored on a single computer, or in sensor networks, multi-robot and micro-grid applications.
We take the view that progress in this promising field is hindered by the high complexity of analyzing these decentralized algorithms, due to the interplay between the effects of optimization and of the interconnection network; Most performance bounds are indeed complex and conservative, making it difficult to compare algorithms and understand the reasons limiting their performance.
I will create a mathematical formulation allowing to automatically compute the exact performance of any decentralized optimization algorithm and identify the bottleneck instances, providing insight on what limits the performance. This will allow for rapid exploration of ideas and iterative improvement and help develop a better understanding of the decentralized optimization algorithms. I intend to achieve this by extending to the decentralized case the Performance Estimation (PEP) approach. It relies on formulating the evaluation of an algorithm worst-case performance as an optimization problem itself, whose variables are the objective functions and initial conditions. Such problems are very complex, but have been shown to be solvable exactly. One of my main challenges will be to embed description of classes of interconnection networks in the PEP formulations.