Maxim Khalilov
Maxim Khalilov

Maxim Khalilov is currently a head of R&D at Glovo, a Spanish on-demand courier service unicorn. Prior to that he was a director of applied artificial intelligence at Unbabel, a company disrupting the customer service market with machine translation and worked a product owner in data science at responsible for exploitation, collection and exploitation of digital content for hospitality market. Maxim is also a co-founder of a Natural Language Processing company, has a Ph.D. from Polytechnic University of Catalonia (Barcelona, 2009), an MBA from IE Business School (Madrid, 2016) and is the author of more than 30 scientific publications.

icons-action-calendar28 Apr 2020

The year 2020 is set to be the most difficult for most of us in decades. While the future of the translation industry after the COVID-19 pandemic depends on many factors, there is no doubt that its technologies are set to evolve radically. Indeed, in many ways, the machine translation (MT) journey is just beginning: the end of 2019 and the beginning of 2020 were full of fresh, eye-opening perspectives on tomorrow’s MT and other language technologies.

icons-action-calendar27 Nov 2018

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). The events that brought all of them together were: EMNLP 2018, one of the biggest conferences on Natural Language Processing in the world, and WMT 2018, which for many years has been one of the most reputable conferences in the field of machine translation (MT).

icons-action-calendar19 Sep 2016

The last significant breakthrough in the technology of statistical machine translation (SMT) was in 2005. That year, David Chiang published his famous paper on hierarchical translation models that allowed to significantly improve the quality of statistical MT between distant languages. Nowadays we are standing on the verge of an even more exciting moment in MT history: deep learning (DL) is taking MT towards much higher accuracy and finally brings human-like semantics to the translation process.