PhD · Research Scientist (AI/ML)
brain and biosignal ML · multimodal representation learning · interpretable models · computer vision
I am a research scientist working on machine learning for brain data and biosignals, with additional experience in computer vision and multimodal representation learning.
Across projects, I build end-to-end research pipelines: from problem formulation and data processing to model development, evaluation, and publication-grade experimentation. I also mentor students and junior researchers and contribute to technical methodology, evaluation strategy, and experimental design in collaborative research.
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Brain & biosignal ML EEG, MEG, fMRI, ECoG, decoding, representation learning, and evaluation under real data constraints. |
Multimodal learning Alignment across brain signals, language, audio, and vision, including controlled experimental setups. |
Interpretable ML Neural and structured models with explicit, inspectable decision mechanisms and robust evaluation. |
| Repository | Description |
|---|---|
| EEGSimpleNet | Compact interpretable EEG classification network with tooling for analyzing learned spatial and temporal patterns. |
| LowLatencyEEGFiltering | Real-time EEG filtering and rhythm tracking for low-latency closed-loop settings. |
| Cube++ dataset tools and benchmarks | Reference code, download information, and evaluation scripts for the Cube++ illumination estimation dataset. |
| SIGNAL dataset tools for brain-LLM alignment | Dataset tooling, preprocessing, and analysis code for EEG language benchmark studies. |
| Stimulation-Free ECoG Speech Mapping | Preprocessing and machine-learning pipelines for stimulation-free speech mapping with clinically aligned evaluation workflows. |
Deep learning · Representation learning · Multimodal learning · Neuroimaging (EEG, MEG, fMRI, ECoG) · Interpretable ML · Computer vision
Python · PyTorch · Git · Linux · Slurm