During my Ph.D., I investigated analytically the tradeoff between forwarding-paradigms. Furthermore, I developed an analytical characterization of mobile networks based on the distribution of the size of connected components.
After the Ph.D., I joined the Paris Research group of Technicolor, a global provider of services and products for the communication, media and entertainment industries. We designed and evaluated a distributed inference algorithm to provide recommendations for media such as movies or TV shows. Thanks to the Netflix Prize, we had ample data to evaluate our algorithm with. As opposed to the objective of the Netflix Prize, however, our goal was to offer provable privacy guarantees to all users. To accomplish this, we relied on two core tools. First, belief propagation on Bayesian networks allowed us to distribute computations. Second, differential privacy provided the provable privacy guarantees we were looking for. Differential privacy was originally developed at Microsoft Research; but more recently, Apple has picked up differential privacy as a research focus.
My last stage in research was the College of Information and Computer Sciences of the University of Massachusetts (UMASS) in Amherst, MA, where we studied the mobility of people -- with a twist. Rather than just collecting GPS location data, we compared the trajectory of the logical location of the invidiual on the global Internet with their geographic location on planet earth.
Please refer to the publication list for further information.