Mariia Pukalchik

Dr. Mariia Pukalchik received the specialist qualification in Analytical chemistry from the Vyatka State University in 2009, and PhD in Ecology in Lomonosov Moscow State University, Faculty of Soil Science in 2013. During her Ph.D. study, Mariia was working in an industry company as an advisor to a research group in ecology and mathematical modeling for solving ecological problems. Later, she worked in Czech University of Life Science, Faculty of Agrobiology, Food and Natural Resources as a postdoc fellow. From 2015 she was a Research scientist in Lomonosov Moscow State University, and since November 2017 Mariia has been a Research Scientist in Skolkovo Institute of Science and Technology (Skoltech). Mariia is interested in machine learning, statistics and their applications in Soil and Agricultural Sciences. Since 2010 she has received 3 grants from Russian Foundation in Basic Research as PI (travel grant; mol_a; mol_a_dk), and 3 grants as co-PI (mol_a; a), now her investigation is supported by Russian Science Foundation.


Now Dr. Mariia Pukalchik works in “Digital Agriculture” –  the new scientific field that uses data-intensive approaches to drive agricultural productivity with minimizing its environmental impact, and which have arisen from agro-technology and precision farming.  She focuses on the benefits and prospects of intelligent digital tools to advance connectivity in agriculture. 

Специалист в области Аналитической химии ( 2009 г, ВятГГУ) и кандидат биологических наук по специальности “Экология” ( 2013, МГУ им М.В. Ломоносова, факультет почвоведения). В 2015-м году выйграла грант Erasmus Mundus IAMONET на PostDoc в Чешском университете естественных наук, Прага. По окончании стажировки работала в должности старшего научного сотрудника МГУ им М.В. Ломоносова и в 2017 г перешла на работу в Сколтех , с 2018 года assistant professor.

Научная работа фокусируется в области прикладных технологий машинного обучения и ИИ в решении задач сельского хозяйства и охраны окружающей среды, а также в области аналитики данных.