PhD, ATER @ LIP, UCBL, ENS Lyon

I am a Research and teaching assistant at the University Claude Bernard Lyon 1, doing my research in the DANTE team of the LIP laboratory at ENS Lyon, under the supervision of Thomas Begin and in collaboration with Anthony Busson and Paulo Gonçalves.

My research interests include performance evaluation, modeling, and simulation of wireless and wired computer networks. Queuing and game theory. Modeling in health and environmental science and public decision making.

Recently, we developed the Science for Kids project (available in Macedonian only: Наука за деца). The project emerged as an answer to the lack of digital education resources in Macedonian and was further motivated by the urgency of bringing knowledge to kids during the Corona pandemic. Feel free to check it out and contact us at info@naukazadeca.mk for any information regarding the project, how to join the team, or ways to help us further develop and grow this idea.

marija.stojanova AT univ-lyon1.fr
marija.stojanova AT ens-lyon.fr

LIP, Laboratoire de l'Informatique du Parallélisme
46, Allée d'Italie
69364 Lyon
France

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You can download the full CV in English.
My research is focused on the modeling, simulation, and performance evaluation of computer networks, more specifically IEEE 802.11 networks, commonly known as WiFi. Most computer networks follow the TCP/IP protocol suite in which the network is divided into a series of independent layers that perform a given function. Over the past four years, I have been working on the performance evaluation of the Medium Access Control (MAC) layer. This is the part of the network that is just below the Network layer (that decides where the data should be sent) and just above the Physical layer (that defines for the bits of data are sent over the medium). The MAC layer provides the functions that decide which device gets to access the medium and start sending its bits of data.
We developed two types of models: constructive Markovian models and descriptive Graph Signal Processing (GSP) models. The Markovian models take a detailed look at the internal functioning of the network and they try to implement simplified versions of the network's mechanisms that allow us to study how the network behaves as a function of some input parameters. These models require a high level of expertise and intuition in the system being modeled, as we first need to have an in-depth knowledge of how the network behaves in order to develop a simple Markov chain that copies that behavior.
The GSP models take a completely different approach: the network is seen as a black box to which we provide an input and from which we recover an output. Though the development of such models does not necessarily require vast system expertise, we found that tweaking the models by incorporating certain known behaviors of the MAC layer allowed us to significantly increase their accuracy.
Conflict graph-based Markovian model to estimate throughput in unsaturated IEEE 802.11 networks, Marija Stojanova, Thomas Begin, Anthony Busson. IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 17, May 2017, Paris.

Conflict graph-based model for IEEE 802.11 networks: A Divide-and-Conquer approach, Marija Stojanova, Thomas Begin, Anthony Busson. Performance Evaluation, Elsevier, December 2018.

Modélisation des réseaux IEEE 802.11 : Diviser pour régner, Marija Stojanova, Thomas Begin, Anthony Busson. CoRes 2019, France.

Traitement du signal sur graphe pour modéliser les WLANs, Marija Stojanova, Thomas Begin, Paulo Gonçalves. Gretsi 2019, France.

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