Curriculum vitae
Yangdeyi Yang
Electrical and Electronic Engineering · Signal Processing · Machine Learning
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Profile
Electrical and Electronic Engineering MEngSc candidate with project experience in sensor machine learning, biomedical signal processing, multirate DSP and verification-oriented software. I pair clear experimental boundaries with implementation that others can inspect.
Research experience
Machine Learning Research Intern · Tyndall National Institute
2026, starting in May · CRFID sensing under reader-geometry shift.
- Built preprocessing, grouped partitions and source-only evaluation for chipless RFID measurements.
- Compared models and diagnostics under an unseen reader distance-and-angle combination.
- Reported near-chance transfer performance clearly, distinguishing measured evidence from untested explanations.
Selected engineering projects
Patient-specific EEG seizure detection · UCC EE6019
- Implemented CHB-MIT ingestion, filtering, spectral and synchrony features, fold-local feature selection, Random Forest classification and event scoring.
- Reported 53/55 detected seizure events and 0.3066 pooled false alarms/hour across a 10-subject retrospective study.
Detection pipeline and result tables
EEG multirate DSP · UCC EE6041
- Designed 500 Hz → 32 Hz conversion with an 8/125 ratio and a 4,145-tap Kaiser FIR.
- Validated direct/polyphase equivalence and preservation of the 0.5–12.5 Hz analysis band.
DSP implementation and benchmark
Eight-channel EEG simulator and verification proof of concept
- Built Python desktop and browser consoles with waveform playback, channel controls, fault injection and virtual 16-bit DAC mapping.
- Defined the proposed physical path and retained the boundary between software verification and unmeasured hardware output.
Education
MEngSc, Electrical and Electronic Engineering · University College Cork
2025–2026
BE, Electrical and Electronic Engineering · University College Cork
2020–2024
Technical skills
Python, NumPy, SciPy, pandas, scikit-learn, PyTorch; EEG filtering and feature extraction, multirate FIR processing, grouped experimental design, domain generalisation, error analysis; Git, automated tests and CI.