Hi, welcome on my website!

My name is Sarah Wassermann and I am a 1st-year PhD student at Inria Paris, under the supervision of Dr. Renata Teixeira and Dr. Pedro Casas (AIT Austria). My main research topic is about Internet Quality of Experience.
Prior to that, I did my MSc. and BSc. studies at the University of Liège, specializing in computer systems and networks.
I have been interested in research since my 2nd year at university. This interest was strengthened by four research internships I performed in the last three years, in the specific field of Internet network measurements, and more precisely in active measurements for network performance analysis.
In my free time, I enjoy travelling, photography, reading, and video games. I am also interested in web and graphic design and therefore like to play around with Adobe Photoshop.

Curriculum Vitae

Learn about what I do

Research

My primary research interest lies in computer networks, and mostly in network traffic measurements.

During my PhD, my research focuses on Internet Quality of Experience (QoE). My goal will be to deliver algorithms, methods, and software systems to measure Internet QoE and diagnose the root cause of QoE impairments. As I personally see it, there is today an ever-growing need for better approaches to enhance the functioning of Internet-like networks and services that are commonly consumed by end-users, and this is why I am deeply interested in QoE. Indeed, today, we are accomplishing more and more tasks via the Internet and it is thus crucial that we can do so without being hindered by poor Internet performance. Questions like "Why does the video I am watching on YouTube keep stalling?", "Why is the website of my favourite newspaper so slow to load today?" or "Why is the audio quality of my Skype call so bad?" are unfortunately asked very (and even too) frequently.

During my MSc. studies, I conducted research in the field of network measurements under the supervision of Dr. Pedro Casas and Prof. Dr. Benoit Donnet. In particular, I worked on Internet path dynamics and performance, machine learning for networking, anycast in cellular networks, and malware detection in smartphones. My Master's thesis is entitled Anycast-based DNS in Mobile Networks and I carried out this project under the supervision of Prof. Fabiàn Bustamante and Prof. Dr. Benoit Donnet.

In the context of my research, I have developed two tools, namely DisNETPerf, an Internet paths performance analyzer, and NETPerfTrace, an Internet path tracking system. I also contributed to the well-known Paris Traceroute extension to the standard traceroute tool.

DisNETPerf

A distributed Internet paths performance analyzer developed in the context of my research internship at FTW Vienna in 2015. For more information about this work, please have a look at my papers or visit the GitHub repo of DisNETPerf.

NETPerfTrace

An Internet path tracking system. NETPerfTrace is a tool capable of forecasting path changes and path latency variations. For more information about this tool, please have a look at my papers or visit the GitHub repo of NETPerfTrace.

libparistraceroute

During my internship at LiP6 in Paris in 2014, I implemented a generic ping tool based on libparistraceroute which can handle IPv4, IPv6 and TCP, UDP, ICMP probes. I also extended the library itself. More information can be found on the project's GitHub repo of libparistraceroute.

Publications

In the context of my research, I have (co-)authored several papers

Journal papers:

Unveiling Network and Service Performance Degradation in the Wild with mPlane
P. Casas, P. Fiadino, S. Wassermann, S. Traverso, A. D'Alconzo, E. Tego, F. Matera, M. Mellia
in IEEE Communications Magazine, Network Testing Series, vol. 54 (3), pp. 71-79, 2016

Conference papers:

Improving QoE Prediction in Mobile Video through Machine Learning
P. Casas, S. Wassermann
in Proceedings of the 8th International Conference on Network of the Future (NoF), London, United Kingdom, 2017
Best Paper Award candidate

Workshop papers:

On the Analysis of Internet Paths with DisNETPerf, a Distributed Paths Performance Analyzer
S. Wassermann, P. Casas, B. Donnet, G. Leduc, M. Mellia
in Proceedings of IEEE WNM, Dubai, United Arab Emirates, 2016

NETPerfTrace - Predicting Internet Path Dynamics and Performance with Machine Learning
S. Wassermann, P. Casas, T. Cuvelier, B. Donnet
in Proceedings of ACM SIGCOMM 2017 Workshop on Big Data Analytics and Machine Learning for Data Communication (Big-DAMA), Los Angeles (CA), USA, 2017

Extended abstracts:

Towards DisNETPerf: a Distributed Internet Paths Performance Analyzer
S. Wassermann, P. Casas, B. Donnet
in Proceedings of the ACM CoNEXT Student Workshop, Heidelberg, Germany, 2015

Machine Learning based Prediction of Internet Path Dynamics
S.Wassermann, P. Casas, B. Donnet
in Proceedings of the ACM CoNEXT Student Workshop, Irvine (CA), USA, 2016

Demo sessions:

Reverse Traceroute with DisNETPerf, a Distributed Internet Paths Performance Analyzer
S. Wassermann, P. Casas
in Proceedings of the Demonstrations of the 41th Annual IEEE Conference on Local Computer Networks (LCN-Demos 2016), Dubai, United Arab Emirates, 2016

Posters:

Anycast on the Move – A First Look at Mobile Anycast Performance
S. Wassermann, John P. Rula, Fabiàn E. Bustamante
presented during the poster session at the ACM Internet Measurement Conference 2017, London, United Kingdom, 2017

BIGMOMAL – Big Data Analytics for Mobile Malware Detection
S.Wassermann, P. Casas
presented during the poster session at the ACM Internet Measurement Conference 2017, London, United Kingdom, 2017

Technical reports:

Predicting Internet Path Dynamics and Performance with Machine Learning
S. Wassermann, P. Casas, T. Cuvelier, B. Donnet
AIT-Big-DAMA Tech. Rep. A3215, 2017


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