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/ 

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.

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/)