Natalia Efremova

Natalia Efremova

Teradata Research Fellow in Marketing and AI


Saïd Business School
University of Oxford
Park End Street
Oxford
OX1 1HP

Profile

Dr Natalia Efremova's research focuses on applications of machine learning tools to marketing problems under the Oxford Future of Marketing Initiative.

Natalia has a background in both academia and industry research. Over the past 10 years, she has been working in the field of deep learning for image and video processing in the domains of biometrics and emotion recognition. Her work has been commended for successfully meeting challenges set for the global research community in these areas, and her research has been published in the top machine learning conferences (IEEE/FG, IJCNN, HRI, IEEE/ICME).

Previously, Natalia was an Associate Professor in the Department of Economical Mathematics and Statistics in Plekhanov’s Russian University of Economics, where she taught courses on neural networks, intelligent systems and decision-making to undergraduate and postgraduate students.

Natalia has an MBA from the University of Oxford and a PhD in Computer Science from the University of Kyoto, where she was a recipient of a Japanese Government (MEXT) Postgraduate Scholarship.

Publications

  • Boris Knyazev,
  • Roman Shvetsov,
  • Natalia Efremova,
  • Artem Kuharenko,
  • IEEE
Marketing

Cognitive Architectures for Optimal Remote Image Representation for Driving a Telepresence Robot

  • Journal article
  • Human-Robot Interaction
  • N Efremova,
  • A Kiselev
Marketing

An inferior temporal cortex model for object recognition and classification(opens in new window)

  • Journal article
  • Scientific and Technical Information Processing
  • NA Efremova,
  • T Inui
Marketing

Prediction of Soil Moisture Content Based On Satellite Data and
Sequence-to-Sequence Networks

  • Journal article
  • Natalia Efremova,
  • Dmitry Zausaev,
  • Gleb Antipov
Marketing

SMArtCast: Predicting soil moisture interpolations into the future using
Earth observation data in a deep learning framework

  • Journal article
  • ICLR 2020
  • Conrad James Foley,
  • Sagar Vaze,
  • Mohamed El Amine Seddiq,
  • Alexey Unagaev,
  • Natalia Efremova
Marketing
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