Big Data and their smaller varieties in network analysis

Is it true that any two people on Earth are only six handshakes away from each other and how does Facebook use this to suggest friends? According to Maxim Panov, Assistant Professor at Skoltech and an expert in machine learning, out of the millions of connections the world is laced with, we are fully aware of only a few, while others are too complex and barely obvious and can only be traced by analyzing Big Data. Surprisingly, such seemingly disparate things as protein-protein interactions and social connections, or the Internet and the brain’s neural networks have a lot in common. Join Professor Panov’s lecture to discover how network analysis can help pinpoint various connections, predict and prevent epidemics, assess the financial market stability and diagnose neurodegenerative diseases at an early stage.


Maxim Panov, Assistant Professor at the Skolkovo Institute of Science and Technology, supervises a group of students and conducts research at the crossroads of machine learning and mathematical statistics.

Panov worked as a research scientist at DATADVANCE where he was involved in developing a library of data analysis methods for engineering applications. The library is currently used by many companies around the world, including Airbus, Porsche, Mitsubishi, Toyota, and Limagrain.

The lecture will be given in Russian at Zaryadye Park Lecture Center on March 12, 2019 at 7 p.m.

Registration by link –

Contact information:
Skoltech Communications
+7 (495) 280 14 81

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