Artem Moskalev
Hi, I am Artem 👋. I am a Research Scientist at Johnson&Johnson, where I work on reimagining drug discovery with AI. My research focuses on geometric deep learning and language models, with a keen interest in developing geometry-aware methods that efficiently learn from unlabeled data.
Previously, I did my PhD at AMLab / VIS Lab at the University of Amsterdam, supervised by Prof. Arnold Smeulders. My PhD focus was on group-equivariant neural networks. I received MSc degree from Skolkovo Institute of Science and Technology, where I worked on inverse problems and computational imaging under the supervision of Prof. Anh-Huy Phan.
I love history and learning about cultures. I like folk and metal music. In my free time, I enjoy playing chess and padel.
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Selected publications
HARMONY: A Multi-Representation Framework for RNA Property Prediction
ICLR: AI for Nucleic Acids, Oral, 2025
We introduce HARMONY, a neural network that dynamically integrates 1D, 2D, and 3D representations, and seamlessly adapts to diverse real-world scenarios. Our experiments demonstrate that HARMONY consistently outperforms existing baselines across multiple RNA property prediction tasks on established benchmarks, offering a robust and generalizable approach to RNA modeling.
HELM: Hierarchical Encoding for mRNA Language Modeling
ICLR, 2025
We introduce Hierarchical Encoding for mRNA Language Modeling (HELM), a novel pre-training strategy that incorporates codon-level hierarchical structure into language model training. HELM modulates the loss function based on codon synonymity, aligning the model's learning process with the biological reality of mRNA sequences.
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction
ICLR, 2025
We present the first systematic study of incorporating geometric context—beyond 1D sequences—into RNA property prediction. We reveal that geometry-aware models are more accurate while requiring less training data. At the same time, plain sequence-based models are the most robust to sequencing noise.
SE(3)-Hyena Operator for Scalable Equivariant Learning (Best Paper Award!!!)
ICML: Geometry-grounded Representation Learning and Generative Modeling, 2024
We introduce SE(3)-Hyena operator, a translation and rotation equivariant long-convolutional method to process global geometric context at scale with sub-quadratic complexity. Significantly more compute and memory efficient than transformers.
On genuine invariance learning without weight-tying
ICML: Topology, Algebra, and Geometry in Machine Learning, 2023
We study properties and limitations of invariance learned by neural networks from the data compared to the invariance achieved through equivariant weight-tying. We next address the problem of aligning data-driven invariance learning to the genuine invariance of weight-tying models.
LieGG: Studying Learned Lie Group Generators
NeurIPS, Spotlight, 2022
We present LieGG, a method to extract symmetries learned by neural networks and to evaluate the degree to which a network is invariant to these symmetries. With LieGG, one can explicitly retrieve learned invariances in a form of the generators of corresponding Lie-groups without any prior knowledge of the symmetries in the data.
Contrasting quadratic assignments for set-based representation learning
ECCV, 2022
We go beyond contrasting individual pairs of objects by focusing on contrasting objects as sets. We use combinatorial quadratic assignment theory and derive set-contrastive objective as a regularizer for contrastive learning methods.
DISCO: accurate Discrete Scale Convolution (Best Paper Award!!!)
BMVC, Oral, 2021
We develop a better class of discrete scale equivariant CNNs, which are more accurate and faster than all previous methods. As a result of accurate scale analysis, they allow for a biased scene geometry estimation almost for free.
Relational Prior for Multi-Object Tracking
ICCV: VIPriors, Oral, 2021
Tracking multiple objects individually differs from tracking groups of related objects. We propose a plug-in Relation Encoding Module which encodes relations between tracked objects to improve multi-object tracking.
Scale Equivariance Improves Siamese Tracking
WACV, 2021
In this paper, we develop the theory for scale-equivariant Siamese trackers. We also provide a simple recipe for how to make a wide range of existing trackers scale-equivariant to capture the natural variations of the target a priori.
Highlights
- 🌟Our paper HARMONY on multi-representational learning for mRNA has been selected as ORAL on AI4NA@ICLR2025.
- We presented HELM: mRNA languge model at MILA Valence Multiomics reading group [video].
- We presented our paper on Impact of Geometric Context for RNA Property Prediction at MILA Valence Multiomics reading group [video].
- Our HELM paper on hierarchical language models for RNA is accepted to ICLR2025.
- Our Beyond Sequence paper on RNA modeling is accepted to ICLR2025.
- 🌟Our SE3-Hyena operator work has been awarded with outstanding paper award at GRaM@ICML!
- Our paper on scalable equivariant learning has been accepted to GRaM@ICML.
- I joined Janssen R&D at Johnson & Johnson as a Research Scientist.
- I successfully defended my doctoral thesis.
- Our paper on data-driven invariance learning will be presented in TAG-ML@ICML.
- I gave a talk on extracting learned symmetries at Geometric Deep Learning Seminar [video].
- Our paper on Lie group symmetry learning is accepted to NeurIPS2022 as Spotlight.
- I am attending International Gran Canaria School on Deep Learning.
- Our paper on set-based representation learning is accepted to ECCV2022.
- I presented our paper on scale-equivariance on NCCV2022.
- 🌟Our paper "DISCO: accurate Discrete Scale Convolution" has won BMVC 2021 Best Paper Award.
- Ivan Sosnovik and I gave a talk on "Scale-Equivariant CNNs for Computer Vision" at Computer Vision Talks [video].
Interns and Students
- Mehdi Yazdani-Jahromi: HELM - Hierarchical Encoding for mRNA Language Modeling
- Junjie Xu: Beyond Sequence - Impact of Geometric Context for RNA Property Prediction
- Evgenia Ilia: Efficient Self-Supervised Learning for Real-world Tabular Data
- Harm Manders: Dense contrastive learning for microscopy cell segmentation
- Lotte Bottema: Deep sequence modeling for trajectory forecasting
- Nadia Isiboukaren: Space-Time-Slot correspondence as a contrastive random walk for video object segmentation
- Jorrit Ypenga: Domain-regularization for siamese object tracking
Teaching
- Statistics, Simulation and Optimization, University of Amsterdam, 2019 – 2022
- Introduction to Image Processing, Skolkovo Institute of Science and Technology, 2018
Reviewing
- ICLR 24/25, NeurIPS 22/23, ICML 21/22/23, CVPR 19/20, ICCV/ECCV 19/20