Profile
Roles:
- Member of the Centre for Globalisation Research (CGR)
Biography:
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. 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 venues (ICLR, NeurIPS, IEEE, IJCNN, HRI).
She holds a Ph.D. in Computer Science (Neural Networks for Computer Vision) from the Graduate School of Informatics, University of Kyoto, Japan. Previously, she worked as an Associate Professor in computer science in Plekhanov University of Economics, Russia in 2012-2016. She obtained her MBA degree from the University of Oxford, and she further worked as a Teradata Research Fellow in the University of Oxford, Said Business School. Natalia joined Queen Mary university in November 2021.
Teaching
Postgraduate:
- MSc in Business Analytics (Project Management course)
- MSc in Business Analytics (Group projects in business analytics)
- PhD program: BAAE - Elective modules Introduction to deep learning
- PhD program: BAAE - Elective modules -Introduction to geospatial analysis with Python
Research
Research Interests:
Natalia’s research focuses on Machine Learning (ML) and primarily deep neural networks for pursuing sustainable goals. This includes defining the areas where ML tools would be especially advantageous within targets related to climate change, gender equality, sustainable agriculture, water, and food scarcity, as well as monitoring progress towards market adoption of the new AI-based technologies for water-, agri- and waste- tech industries. Her work has two main goals:
a) Develop more reliable, transparent, and accountable models for sustainable land-use and climate change-related applications.
b) Empower economists, policy makers and researchers with intelligent ML-based tools to facilitate progress towards more sustainable future.
Research group membership:
Publications
Journal Articles
Efremova N., Seddik M., Erten E. (2022) Soil Moisture Estimation using Sentinel-1/2 Imagery Coupled with cycleGAN for Time-series Gap Filing. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022.
Efremova N., Inui T. (2014) An inferior temporal cortex model for object recognition and classification. Scientific and Technical Information Processing, vol.41 (6), pp.362-369.
Efremova N., Inui T. (2013) Visual cortex model for object recognition and classification. Artificial Intelligence and Decision Making, vol.1 pp. 55-62.
White papers and books
Efremova, N., Foley, J., Unagaev, A., Karimi, R., (2022) AI for sustainable agriculture and rangeland monitoring. in The Ethics of Artificial Intelligence for the Sustainable Development Goals (chapter), Springer Nature.
Thomaz, F., Efremova, N., Mazzi, F., Clark, G., Macdonald, E., Hadi, R., Bell, J., Stephen, A., (June 22, 2021) Ethics for AI in Business. (Available at SSRN).
Selected Refereed Conference Papers
Foley J., Unagaev A., Efremova N. (2021) Autonomous monitoring of environmental condition and overgrazing in East-African rangelands through remote sensing. Workshop on ML for developing world, NeurIPS 2021.
Efremova N., Erten E. (2021) Biophysical parameter estimation using earth observation data in a multi-sensor data fusion approach: CycleGAN. IEEE International Geoscience and Remote Sensing Symposium.
Vaze S., Foley C.J., Seddiq M.E.A., Unagaev A., Efremova N. (2020) Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks. AI for social good Harvard CRCS Workshop.
Foley C.J., Vaze S., Seddiq M.E.A., Unagaev A., Efremova N. (2020) SMArtCast: Predicting soil moisture interpolations into the future using earth observation data in a deep learning framework. ICLR 2020.
Efremova N., Hajimirza N., Bassett D. and Thomaz F. (2020) Understanding consumer attention on mobile devices. IEEE Int’l Conf. on Automatic Face and Gesture Recognition 2020.
Efremova N., Patkin M., Sokolov D. (2019) Face and Emotion Recognition with Neural Networks on Mobile Devices: Practical Implementation on Different Platforms. IEEE Int’l Conf. on Automatic Face and Gesture Recognition 2019.
Efremova N., West D., Zausaev D. (2019) AI-based evaluation of the SDGs: the case of crop detection with earth observation data. AI for Social Good Workshop, ICLR 2019.
Efremova N., Zausaev D., Antipov G. (2018) Multimodal analysis of satellite data for soil moisture content prediction with ensembles of neural networks. WiAI Workshop, NeurIPS 2018.
Knyazev B., Shvetsov R., Efremova N., Kuharenko A. (2018) Leveraging large face recognition data for emotion classification. 1st Workshop on Large-scale Emotion Recognition and Analysis. IEEE Int’l Conf. on Automatic Face and Gesture Recognition 2018.
Kiselev A., Scherlund M., Kristoffersson A., Efremova N., Loutfi A.(2015) Auditory Immersion with Stereo Sound in a Mobile Robotic Telepresence System: Motivation, Prototype, and Evaluation. In proc. Human-Robot Interaction 2015 Conf.
Efremova N., Kiselev A. (2014) Cognitive Architectures for Optimal Remote Image Representation for Driving a Telepresence Robot. Human-Robot Interaction 2014 Conf.
Tarasenko S., Efremova N. (2013) Neural Architecture for Complex Scene Recognition Based on Rank-order Features of IT Neurons. In proc. Int’l Conf. on Neural Networks.
Efremova N., Asakura N., Inui T., Abdikeev N. (2012) The visual cortex model for object recognition and classification. In proc. of the XIV All-Russian Scientific and Technical Conf. Neuroinformatics 2012.
Efremova N., Tarasenko S. (2012) A Brain-inspired Neural Architecture for Visual Scene Analysis. In proc. of Int’l Conf. on Application of Fuzzy Systems and Soft Computing, pp. 175-183.
Averkin N., Abdikeev N.M., Efremova N. (2012) Modelling of management processes in cognitive economics. In proc. of the 5th Int’l Conf. of Cognitive Science, pp. 193-195.
Efremova N., Asakura N., Inui T. (2012) Natural object recognition with the view-invariant neural network. In proc. of the 5th Int’l Conf. on Cognitive Science, pp. 802-804.
Efremova N., Asakura N., Inui T. (2011) Neural Network for View-Invariant Object Recognition and Classification. In proc. of the 21st Annual Conf. of the Japanese Neural Network Society.
Efremova N., Asakura N., Inui T., Abdikeev N. (2011) Inferotemporal network model for 3D object recognition. In proc. of IEEE Int’l Conf. on Complex Medical Engineering 2011, pp. 555-560.
Efremova N., Asakura N., Inui T. (2011) Neural Network model of Inferior Temporal Cortex for 3D Object Recognition and Classification. In proc. of the 9th Conf. of the Japanese Society for Cognitive Psychology.
Supervision
Dr Natalia Efremova is available for PhD supervisions and encourages applications from those interested in applications of AI and ML within targets related to climate change, gender equality, sustainable agriculture, water, and food scarcity.
Dr Efremova is currently second supervisor for 2 doctoral students staring in September 2022.
Public Engagement
Dr Natalia Efremova is a fellow of the Digital Environment Research Institute (DERI) .
Scholarly Contributions
- MSc program co-director (MSc in Environmental Analytics, 2023).