Ilia Semenkov

PhD, Research Scientist (AI/ML)

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Highlights

  • AI/ML researcher with 5+ years of experience spanning brain data (EEG/MEG/fMRI/ECoG), computer vision, and multimodal learning.
  • Drives technical execution across the full research pipeline, from model development and data analysis to reproducible pipelines and publications in Q1 journals and CORE A/A* venues.
  • Open-source contributions include releasing well-documented datasets and reusable code/tools to support reproducibility and follow-up work by others.
  • Co-organizes scientific initiatives (international challenges, conference organization, and summer-school teaching/program contributions).
  • Mentors students and junior researchers and provides technical leadership on model architecture and evaluation across multiple projects.

Directions

Brain and biosignal machine learning

End-to-end machine learning on EEG/MEG/fMRI/ECoG and related biosignals, including decoding, representation learning, and evaluation under real data constraints.

Foundation Models for EEG Safer Brain Surgery Speech Mapping BCI and Assistive Biosignal Control Compact Interpretable Neural Decoders Real-Time Brain Signal Tracking EEG-to-fMRI Digital Twin EEG/MEG Component Discovery Clinical MRI Abnormality Detection

Multimodal representation learning and alignment

Representation learning and alignment across modalities (language, audio, vision, brain signals), including controlled setups and embedding-level analysis.

LLM-Brain Alignment for Language Generating Images from fMRI Compact Interpretable Neural Decoders EEG-to-fMRI Digital Twin

Computer vision benchmarks and robust perception

Work spanning computational imaging and robust perception pipelines that remain stable under domain shift.

Camera Vision Benchmarks at Scale Robust Visual Localization Beyond RGB

Interpretable ML and decision-rule modeling

Interpretable models where learned decision structures are explicit, controllable, and robust for applied decision-making settings.

Interpretable Models with Human-Like Rules Compact Interpretable Neural Decoders Foundation Models for EEG EEG/MEG Component Discovery

Recent experience

Research Fellow - Artificial Intelligence Research Institute

Junior Research Fellow (2021-2024); Research Fellow (2025-Present)

  • Drive AI/ML methodology within research projects, from problem formulation and model design to baselines, evaluation protocols, and ablation strategy.
  • Build end-to-end research pipelines and run publication-grade experiments with strong emphasis on reproducibility, robustness, and analysis quality.
  • Co-author papers through the full research cycle, including implementation, additional experiments, error analysis, and revision rounds.
2021-Present Moscow, Russia
Research Fellow - HSE University
  • Translate scientific questions from interdisciplinary collaborators into concrete ML tasks, data formulations, and validation setups.
  • Provide technical ML leadership across collaborative projects through architecture reviews, experiment design, and quality control of results.
  • Supervise students and junior researchers across theses, internships, and project work, helping scope problems and review implementations and findings.
2023-Present Moscow, Russia
Scientific Advisor - ITMO University
  • Advise MSc research in AI/ML by shaping research questions, methodology, and evaluation plans.
  • Review experiments, results, and thesis narratives to bring projects to a coherent and technically sound final form.
2024-Present Moscow, Russia

Earlier experience

  • Research Intern - HSE University | 2020-2021 | Moscow, Russia
  • Teaching Assistant - HSE University | 2017-2021 | Moscow, Russia
  • Research Intern - Institute for Problems of Information Transmission | 2020-2021 | Moscow, Russia

Skills

Expertise Deep learning | Representation learning | Multimodal learning | Neuroimaging (EEG, MEG, fMRI, ECoG) | Interpretable ML | Computer vision
Tech Python | PyTorch | Git | Linux | Slurm
Languages English (fluent) | Russian (native)

Education

  • Ph.D. in Computer Science, HSE University (2021-2025)

    Thesis: "Neural network-based methods for decoding multimodal neuroimaging data"

  • M.S. in Computer Science, HSE University (2019-2021)
  • B.S. in Public Administration, HSE University (2015-2019)