I am currently a Postdoctoral Researcher at the
Centre for Computational Biology (CBIO)
at
Mines Paris PSL
and
Insitut Curie.
I work under the supervision of
Chloé-Agathe Azencott,
Pierre Baduel,
and
Vincent Colot
on statistical machine learning for the integration of transposable elements variability and epivariability in genotype-to-phenotype studies.
Previously, I have been a PhD Student in Machine Learning at
DaSciM team
at
École Polytechnique,
under the supervision of
Jesse Read.
My research interests include:
- Machine Learning in genetics & genomics
- Genotype-to-phenotype studies
- Multi-output prediction
- Missing value imputation
- High-dimensional data
My CV is available here.
I am currently a Postdoctoral Researcher at the Centre for Computational Biology (CBIO) at Mines Paris PSL and Insitut Curie. I work under the supervision of Chloé-Agathe Azencott, Pierre Baduel, and Vincent Colot on statistical machine learning for the integration of transposable elements variability and epivariability in genotype-to-phenotype studies. Previously, I have been a PhD Student in Machine Learning at DaSciM team at École Polytechnique, under the supervision of Jesse Read.
My research interests include:
My CV is available here.
Publications
- Antonenko E., Beigaitė R., Mechenich M., Read J., Žliobaitė I., Backward inference in probabilistic Regressor Chains with distributional constraints. Pre-print.
- Antonenko E., Carreño A., Read J., Autoreplicative Random Forests for missing value imputation. Pre-print.
- Antonenko E., Read J., Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets. ArXiv pre-print.
- Antonenko E., Multi-target learning and prediction: novel methods and applications. PhD Thesis.
- Antonenko E., Read J., Multi-Modal Ensembles of Regressor Chains for Multi-Output Prediction. Advances in Intelligent Data Analysis XXI - 21st International Symposium, IDA 2022.
- Ivanenko V., Antonenko E. , Gelfand M., Yager J., Ferrari F., Changes in segmentation and setation along the anterior/posterior axis of the homonomous trunk limbs of a remipede (Crustacea, Arthropoda). PeerJ, August 2016.
Talks and Posters
- July 25, 2023.
[ Poster , Video ]
Genotype Imputation with Multi-label Random Forests.
ISMB/ECCB 2023, MLCSB Cosi, Lyon (France).
- May 26, 2023.
[ Talk ]
Multi-output machine learning with applications to genomics.
Institut Imagine, Paris (France).
- March 10, 2023.
[ Talk ]
Autoreplicative Random Forests for missing value imputation.
DaSciM seminar, Ecole Polytechnique, Palaiseau (France).
- February 13, 2023.
[ Talk ]
Autoreplicative Random Forests for missing value imputation.
Group seminar, KU Leuven KULAK, Kortrijk (Belgium).
- November 21-22, 2022.
[ Poster ]
Genotype Imputation with Multi-label Random Forests.
MLCB 2022, virtual.
- September 19, 2022.
[ Talk , Best paper award]
Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets.
Multi-Label Learning Workshop, current trends and open challenges, ECML PKDD 2022, Grenoble (France).
- April 21, 2022.
[ Talk]
Multi-Modal Ensembles of Regressor Chains for Multi-Output Prediction.
IDA 2022, Rennes (France).
- November 26, 2021.
[ Talk ]
Chaining methods and their application to genomic data.
DaSciM seminar, Ecole Polytechnique, Palaiseau (France).
Teaching
- December, 2023: Teaching assistant. CSE204: Apprentissage artificiel (Machine Learning) at École des Mines.
- Spring, 2023: Teaching assistant. CSE204: Machine Learning at École Polytechnique.
- Spring, 2022: Teaching assistant. CSE204: Machine Learning at École Polytechnique.