liubov tupikina
Short term fellow
Liubov's Bio

Currently from 2019 I am researcher at Bell labs working on machine learning methods development for trajectories analysis and causal discovery from time-series.

From 2018 I am short-term fellow at CRI, working on two different projects at CRI. First of all, I am working on analysis spreading processes in networks: epidemics spreading, mathematical foundation of information spreading, first-passage time network measures. We published several works on networks analysis (percolation of networks) and universal properties of trajectories. I also work on science-outreach scientists-schools networks "Lecturers without borders" www.scied.network which brings together scientists traveling around the globe to come to high-schools in remote areas, share knowledge and critical thinking.

Liubov is a mathematician and a theoretical physicist by training, with a PhD from Humbolt University. She studies mathematics in Moscow State University (MSU). 

She is interested in the complex networks and processes on them. She also develops the international scientists-schools network exploring potential of scientific networks.  

She works on the interface of physics, maths, data analysis and also is interested in the data visualisation methods.


My scientific projects are also listed on my project page .

Lecturers without borders
Connecting travelling scientists and schools
Quantifying innovation and innovators in Science
Mapping scientific trajectories to uncover the universal patterns behind paradigms
Analyzing heterogeneous spreading dynamics
Develop analytical and numerical tools for analysis of heterogeneous spreading
Network seminars at CRI
This event brings together network scientists to discuss open network problems
Mobility analysis
Analysis of mobility data for social good. Analysis of individual trajectories and global open data of human travels.
Collaborative learning from student forum and phone call data
Reconstruct student interaction network from online and phone call data to predict grades.