I'm a Neuroscience PhD candidate at the University of Washington working at the intersection of neuroscience and machine learning. I have 5+ years of experience building biosignal processing and ML decoding pipelines for electrophysiology and multimodal wearable data.
- Neural decoding & brain-computer interfaces
- Probabilistic models of perception and decision-making
- Real-time biosignal processing (EEG, EOG, EMG, ECG etc.)
- Bayesian inference & latent state modeling
- Languages: Python (scikit-learn, PyTorch, SciPy, Pandas) · MATLAB · R · C
- ML & Modeling: LSTM, LightGBM, HMMs, deep learning, Bayesian inference, unsupervised clustering, PCA, demixed PCA
- Signal Processing: Feature extraction, FFT, wavelet analysis, filtering, artifact removal, real-time monitoring
- Real-time Sleep Stage Estimation — 5-class sleep stage classifier + apnea prediction (10s ahead) from multimodal wearable (E4) & PSG signals; 74% macro F1, 0.70 AUC-PR with LightGBM
- Prior encoding in NHP superior colliculus — neural correlates of Bayesian priors in primate decision-making; presented at Neuroscience 2025 and Simian Collective
- RF mapping — receptive field mapping protocols in MATLAB
- Hong, M. Y., et al. (2025) Neuroscience 2025, Poster / Simian Collective, Poster & Talk
- Hamilos, A. E., ..., Hong, Y., et al. (2021) eLife
- PhD, Neuroscience — University of Washington (2021–Present)
- BS, Biology — University of Chinese Academy of Sciences (2016–2020)