Projects with students and positions

Supervised students and internd

At CRI and Bell labs France we had several students:

Hritika Kathuria (Bell labs, France, currently doing phd in Ecole Polytechnique, Paris)

Zahara Farook (cosupervised with Muki Haklay, Phd in LPI, University of Paris)

Gael Simon (currently doing Phd in Paris)

Mukhtar Urynbassarov (Bell labs France)

Aurélie Faure de Pebeyre (intern at CRI)

Main topics of students:time-series analysis, computational epidemiology, network dynamics, inference problems, data analysis

Internship on mobility analysis

There may be open positions for internships on the topic mobility analysis and visualisations.

Mobility research has become of new field in itself with the big community of researchers around the globe working on it using interdisciplinary approaches. To name but a few problems in this area: increasing of transport in urban areas, numerous problems with traffic, CO2 emissions in areas with big number of cars.

The general goal of our research is to bridge the gap between data science, transport engineering, including city transport analysis, sensing citizens and public transport network planning.

The problems, which we are going to focus on during the internship:

1. analysis of open data on mobility,

2. machine learning techniques applied to detect various transportational properties of mobile agents (moving people, mobile vehicles),

3. modeling of agents behaviour and anticipation of changes in the global scenario of transportation.

Internship in analysis of telecommunication networks

Please find here the description of the internship at Bell labs Nokia in Saclay area.

The main Keywords of the internship are: Machine learning; Causality (Root Cause) Analysis; Big data analytics; distributed systems, networks modeling

Ideal profile: Last year Master-level student (final project). Solid technical skills and background in at least some of the following

areas are required:

• Python or Matlab Programming skills

• Causal inference and modelling, Machine learning, Bayesian networks, Big Data analytics

• Analytics platforms, Spark and Spark streaming, Hadoop/MapReduce (additional)

Description

Bell Labs, the innovation engine of Nokia, where state-of-the-art software, hardware and services for any type of

network including the Internet of Things, 5G, are developed. Bell labs is looking for enthusiastic internship candidates

to join our research efforts on

1. Root Cause Analysis (RCA) in telecommunication networks and other related systems. Root cause analysis (RCA) is

a method of problem solving that tries to identify the root causes of faults or problems. RCA of complex dynamic

systems such as multi-tenant cloud infrastructures is challenged by high volumes of measurements and alarms to be

processed as well as by the noise of alarm generation processes. Often a single fault may produce multiple alarms, and a

given alarm can be caused by different faults. In addition, alarms on root causes do not necessary precede alarms in

consequences and not all of the faulty components generate alarms. This creates ambiguity in the interpretation of

alarms by the human operator.

2. Development of dynamical systems approach for causality and correlation methods validation using integrated

methods of machine learning and causality inference. Dynamical systems and data generating processes have been used

for testing and extraction of causality, correlation relations from complex systems such as climate, fluid dynamics and

many others [9][10]

Science communication coordinator

Together with Lecturers without borders we are happy to announce positions open thanks to funding from Botnar Foundation

See more at github https://github.com/lecturers-without-borders/

Teaching students and courses

We are am teaching the course “Big data and network science” as part of the Digital Masters if the CRI, along with Marc Santolini, Loic Saint Roch, Anirudh Krishnakumar, and Felix Schoeller. The course will take place on Wednesdays 9am-12pm beginning 16 October 2019 to 22 January 2020

Syllabus see github https://github.com/Big-data-course-CRI/materials_big_data_cri_2019

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