By René Andrés García Franceschini
Some of work done here at MIT that is so outlandish, so “out there,” and, quite frankly, so bizarre, that it’s hard to imagine that work being done anywhere else. Coming back to MIT from leave, I knew that if I were to conduct research, I’d want to do so in a lab that devised projects that felt straight out of a sci-fi novel. While many labs have dipped their toes in these alien waters, the Senseable City Lab has been fully immersed in unorthodoxy for more than a decade.
The Senseable City Lab is an extremely multidisciplinary lab that focuses on the human interaction with digital interfaces within the context of an urban environment. Carlo Ratti, the director of the lab, takes pride in the application of technology to enhance the human experience, remarking that some of the lab’s top projects belong in Nature and the MoMA. Make no mistake, as artistic and nontraditional as their work may be, it is by no means lacking in its practical power to change the world. Some of it is perhaps changing your life right now: uberPOOL actually came out of a partnership between Uber and the lab, following a project that looked into savings obtained by sharing cabs.
When I’m not in the Senseable City Lab, I can be found doing data analysis for a 1.101 (Introduction to Civil and Environmental Engineering Design) project.
I started working at the lab this past semester as a UROP student. I work on a project called Underworlds, which emerged out of a conversation between Ratti and Eric Alm from Course 1. The idea is rather simple: we know that there is an enormous amount of information in our waste about our health, eating habits, drug use, etc. Thus, there is a vast reservoir of information about inhabitants of entire cities contained in sewers. If we can tap into that reservoir using a network of robots at different manholes around a city, we could revolutionize public health by providing targeted, almost immediate response to health concerns across a community.
I have been rather fortunate to work in different aspects of this multifaceted project. I initially worked on the robotics of the project. I wrote scripts that would calibrate sensors, collect data and log it in real time. What I’m currently working on, though, is what excites me the most as a Course 1 student on the systems track. The problem is as follows: processing data from sensors is expensive monetarily, computationally and timewise. It is thus imperative to make sure that the sampling times for our sensors are such that the data would be as informative as possible.
I am currently working on implementing algorithms that will select the best sampling times in a computationally efficient way. This is very akin to research done in network control in Course 1 by my advisor, Professor Saurabh Amin, and Professor Ali Jadbabaie. Network control also happens to be what excites me the most, and what I think is an important emerging branch of civil and environmental systems.
Although this is project is in a somewhat early stage (being able to prevent an outbreak is still very much in the future), this project could potentially change how we address widespread health concerns.
Projects like these are exactly why I chose to be Course 1: to tackle very large scale problems using only the most imaginative solutions.