Ekaterina Kondrateva

Education:

01/11/2018–Present
PhD in Computational and Data Science and Engineering Data Science, Neuroscience, Computer Vision, Machine Learning, Signal Processing, Deep Learning

01/09/2016–20/06/2018
Biotechnology Skoltech: Skolkovo Institute for Science and Technology, Moscow (Russia) Molecular biology, Bioinformatics, Neuroscience

27/11/2017–05/12/2017
The 5th Human Brain Project School Obergurgl (Austria) https://education.humanbrainproject.eu/web/5th-school Image processing and Machine Learning in Neuroscience

01/07/2017–01/08/2017
Food Safety and Security courses Tel Aviv University, Tel-Aviv (Israel) https://foodsecurity.tau.ac.il/ Metabolomics: Advances, Applications, Food Security Policy and Economics

01/09/2012–01/09/2016
Bachelor of Information technologies in Biomedical Engineering Bauman Moscow State Technical University http://www.bmstu.ru/. Medical signals processing, Biophysics, Engineering, Informatics, Anatomy and physiology

Research interests:

Computational Neuroscience, Deep Learning, MRI analysis

Thesis title: 

Dimensionality reduction methods for biomarkers search on functional MRI data

Publications:

1. Pominova, M., Kuzina, A., Kondrateva, E., Sushchinskaya, S., Burnaev, E., Yarkin, V., & Sharaev, M. (2019, October). Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction. In Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction (pp. 158-166). Springer, Cham.

2. Sharaev, M., Artemov, A., Kondrateva, E., Sushchinskaya, S., Burnaev, E., Bernstein, A., … & Andreev, A. (2018, October). Mri-based diagnostics of depression concomitant with epilepsy: in search of the potential biomarkers. In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 555-564). IEEE. DOI: 10.1109/DSAA.2018.00071

3. Sharaev, M., Andreev, A., Artemov, A., Burnaev, E., Kondratyeva, E., Sushchinskaya, S., … & Bernstein, A. (2018, September). Pattern recognition pipeline for neuroimaging data. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 306-319). Springer, Cham. DOI: 10.1007/978-3-319-99978-4_24

4. Pominova, M., Artemov, A., Sharaev, M., Kondrateva, E., Bernstein, A., & Burnaev, E. (2018, November). Voxelwise 3d convolutional and recurrent neural networks for epilepsy and depression diagnostics from structural and functional MRI data. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 299-307). IEEE. DOI: 10.1109/ICDMW.2018.00050

5. Sharaev, M., Artemov, A., Kondrateva, E., Ivanov, S., Sushchinskaya, S., Bernstein, A., … & Burnaev, E. (2018, November). Learning connectivity patterns via graph kernels for fmri-based depression diagnostics. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 308-314). IEEE. DOI: 10.1109/ICDMW.2018.00051

Conferences:

1. Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge Workshop (ABCD-NPChallenge 2019), MICCAI2019, Shenzhen, https://sibis.sri.com/abcd-np-challenge/ (Oral Presentation)
2. Brain-Computer Interface: Science and Practise, BCI: Science&Practice 2019, Samara, http://bcisamara.com (Oral Presentation)
3. IEEE International Conference on Data Mining, ICDM 2018 Singapore, http://icdm2018.org/ (Oral Presentation)

4. The 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018 Tourin https://dsaa2018.isi.it/home (Oral Presentation)

5. 5th Human Brain Project School Conference, HBP School 2018, Obergurgl (Austria). Project: “‘Machine Learning and Pattern Recognition for the development of diagnostic and clinical prognostic prediction tools in epilepsy”, https://www.humanbrainproject.eu/(Poster) 

Competitions:

1. Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NPChallenge 2019) provided by NIH NDAR(https://sibis.sri.com/abcd-np-challenge/), 5th place on the leaderboard.

2. Predictive Analytics Competition (PAC2018) MRI depression prediction(https://www.photon-ai.com/pac ), 4th place in the leaderboard.

3. The Medical Image Computing and Computer-Assisted Intervention, MICCAI2019, Shenzhen, http://miccai2019.org (Oral Presentation)

4. Brain-Computer Interface: Science and Practise, BCI: Science&Practice 2019, Samara, http://bcisamara.com (Oral Presentation)

5. XI National Congress of Neurologists, IV National Congress of stroke association, Neurocongress 2019, Saint-Petersburg. Project: “Regression of post-stroke aphasia and associated non-speech syndromes caused by a course of restorative treatment including intensive speech therapy”, http://neurocongress2019.ru (Poster)

6. IEEE International Conference on Data Mining, ICDM 2018 Singapore, http://icdm2018.org/ (Oral Presentation)

7. The 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018 Tourin https://dsaa2018.isi.it/home (Oral Presentation)

8. 5th Human Brain Project School Conference, HBP School 2018, Obergugl (Austria). Project: “‘Machine Learning and Pattern Recognition for the development of diagnostic and clinical prognostic prediction tools in epilepsy”, https://www.humanbrainproject.eu/(Poster)

9. 7th Symposium on Conformal & Probabilistic Prediction with Applications (COPA 2018), Amserdam. Project: “A comparison of machine learning methods for epilepsy and depression classification based on structural MRI data”, http://www.clrc.rhul.ac.uk/copa2018/index.html (Poster)