Teaching and internships

Students

Mentorship

Our work with students and collaborations.

Great gift to be working/co-supervising and mentoring with students from all around the globe for the past 6 years in France. Hritika Kathuria, Mukhtar Ubrassynov (Nokia Bell labs), Zahra Farook, Yasmin Nemotolahi (LPI, France). Some of this work has been officially published on https://github.com/Liyubov

Teaching and outreach

https://liubovkmatematike.wordpress.com/page/

Science Maths olympiads

https://liubovkmatematike.wordpress.com/page/problems-from-mathematical-olympiads/

Seasonal schools 2024/2025 season

Summer schools is something very specific, which is present only in Post Soviet space.

Seasonal schools were started in the Soviet Union, very known educator Makarenko has invested time in educating orphans.

The results of the integrative techniques of children have bitten all expectations allowing students to be better integrated into the society and having another experience of living in the protected and stimulating environment.

The seasonal school is the opportunity itself for making children to embrace the new learning environment, where they can be resting outside of their usual learning environment. Typically summer or winter schools are organised on the base of the summer camps, where children would live in the camps (with the tent), or in summer mansion schools. Lessons include everything from chess, genetics, lessons of how to build your own microscope, integrate complex functions, and everything which you do not normally study at school.
The school season in 2024/Tarusa region.

TEACHING Big data

New course on teaching big data with ID lab in CRI (LPI) France was organised in January 2022 and 2023, some projects are already online https://github.com/Big-data-course-CRI Some project examples are here https://github.com/Liyubov/memorial_data_analysis

We are teaching (online and offline) course on Big data, network science and visualisation. Some materials are available online here https://github.com/Big-data-course-CRI-in-2020 This course is project-driven, hence students are working on projects throughout the course.

TEACHING Network science

I 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

This course will provide an introduction to the field of big data, with a focus on network data and data for mental health. Topics will cover data project management, infrastructure of big data, data analysis and visualisation, and mental health data. The course will be divided into a big data and a network data parts.

Network part:

Why focus on network data? Over the past century, network studies have had significant impact in disciplines as varied as mathematics, sociology, physics, biology, computer science or quantitative geography, giving birth to Network Science as a field of itself. With the recent rise of social networks in the last decade, their use has now become widespread in the digital world. Here we will provide an introduction to the field of Network Science, from the theoretical foundations (generating, analysing, perturbing networks) to the practical hands-on part (analysis and visualisation of a real-world networks).

Network topics will cover:

a. How to construct networks from real data?

b. How to analyze networks? (centrality measures, community detection, statistical analyses etc.)

c. How to visualise networks?

d. Dynamics and spreading phenomena on networks (epidemics / information spreading, diffusion)

e. How do networks wirings change in time? (network robustness, temporal networks)

f. How to represent more complex network data? Multilayer, multiplex networks.

Students will select, analyse and present a network of their choice as part of a personal project for the course. They will also choose an advanced topic in network science & big data for which they will make a presentation in a reverse classroom setting. They will in particular contribute to a wikipedia page about that topic.

Data Efforts in Big Data for Mental Health part:

In this part, students will be presented with topics related to the infrastructure of ‘big data’. They will be introduced to barriers, current trends, types, protocols and importance of ‘big data’ collection in the sphere of mental health, specifically through the

(i) Healthy Brain Network project for 10000 children collecting and sharing neuroimaging & phenotypic data.

Students will also contribute to the development of :

(ii) A Linked Semantic Mental Health Database and scientific framework mapping signs, symptoms and behaviors to subjective and objective measures, projects and technologies (https://github.com/ChildMindInstitute/mhdb/wiki)

(iii) MindLogger Data Collection Platform & App to dramatically improve the convenience, consistency, efficiency, accuracy & analysis of widely distributed data efforts (https://mindlogger.org/)

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]

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