CV
Summary
Ph.D. candidate in Computational Science developing statistical learning methods and scientific software for EEG/BCI systems under session drift. Built an open-source R/Python software ecosystem spanning EEG whitening, feature extraction, domain adaptation, cross-session benchmarking, drift diagnostics, and covariance-aware simulation, with complementary research in reliable deployment under distribution shift.
Technical Skills
- Programming: R (package development, Rcpp), Python (NumPy, scikit-learn, PyTorch), SQL (MySQL).
- Statistics & Machine Learning: Multivariate analysis, tensor methods, dimensionality reduction, regularized regression, mixed-effects models, domain adaptation, transfer learning, time-series modeling, EEG signal processing, nested cross-validation.
- Engineering & Infrastructure: Docker, Git, GitHub Actions, Linux, AWS, HPC/Slurm.
Education
- Ph.D. in Computational Science (Data Analytics Track), University of Massachusetts Boston, Expected Summer 2026
- M.S. in Analytics and Modelling, Valparaiso University, May 2018
- B.Eng. in Electrical and Electronics Engineering, Chongqing University, China, Jun 2014
Selected Open-Source Software
EEG representation and preprocessing
- eegwhiten — whitening and covariance-alignment tools for EEG and multichannel features, with reusable fit/apply models, diagnostics, and cross-session recentering.
- BCIFeatR — train/test-safe EEG feature extraction across CSP/FBCSP, tangent-space covariance features, bandpower, Hjorth, ATM, MVAR, and MSVAR features.
Domain adaptation and cross-session benchmarking
- DA4BCI — R/Python domain-adaptation toolkit for EEG/BCI, including SA, TCA, CORAL, Riemannian transport, optimal transport, shift metrics, and visualization utilities.
- CrossDA — Python experiment runner for MAP, DWP, MMP, and BDP cross-session transfer pipelines, producing summary/detail/role outputs for downstream analysis.
Diagnostics, dashboards, and audit tools
- MSDA-Bench — Streamlit dashboard for comparing multi-source DA strategies, configuration sensitivity, source-session roles, target difficulty, and runtime tradeoffs.
- ShiftDx — drift diagnostics dashboard for fixed-reference MI-EEG monitoring, adapt-vs-retrain decisions, DA method sweeps, and online drift detection.
- TensorEEG — R/Python simulation and covariance-aware augmentation audit toolkit for synthetic EEG tensors, SPD augmentation, fidelity metrics, and manifest replay.
- ShiftLens — static browser visualizer for explaining domain-adaptation geometry with 2-D source-target animations and live metrics.
Professional Experience
Doctoral Researcher, Data Science and Algorithm Development | Sep 2019 – Present University of Massachusetts Boston
Method Development and Deployment Under Shift
- Developed proxy tuning for label-scarce deployment, enabling automatic hyperparameter selection when target-domain labels are unavailable.
- Built a confidence-gated source-selection procedure using bootstrap confidence intervals; in simulation, reduced negative-transfer failures from 20.2% under random source selection to 0%.
Tensor Methods, Diagnostics, and Validation
- Developed TMCCA, a tensor-based method for integrating heterogeneous multi-view datasets, and released the method as the tensorMCCA R package.
- Designed a drift-feature-performance decomposition framework to distinguish input drift from feature-extraction failure as causes of performance degradation.
- Used mixed-effects models to quantify environmental effects on predictive accuracy and inform retraining policy.
- Built evaluation pipelines spanning accuracy, latency, and compute cost across classical and deep-learning classifiers.
- Ran simulation studies across noise and sample-size regimes to evaluate robustness and scalability of feature-matching algorithms.
Data Engineer, IoT Analytics | Jul 2014 – Aug 2016 China Mobile IoT Company Limited, Chongqing, China
- Managed GB-scale time-series sensor data in MySQL and improved heavy aggregation queries through indexing and partitioning, keeping reporting workflows within SLA limits.
- Built monitoring dashboards and automated visual reports for network-health tracking and anomaly detection.
Selected Research Outputs
Honors
- Silver Medal (Rank 98/2767, top 4%), Kaggle HMS – Harmful Brain Activity Classification, 2024
- Doctoral Fellowship, University of Massachusetts Boston (full tuition and stipend), 2019 – Present
Manuscripts
- Shen, Y. et al. Drift-Feature-Performance Decomposition via Structured Geometric Modeling. Under review. 2025
- Shen, Y. et al. Decision-Oriented BCI: Confidence-Gated Adaptation. Under review. 2025
- Degras, D. & Shen, Y. Scalable Feature Matching Across Large Data Collections. 2021
Talks
- Invited Talk: Benchmarking Classification Pipelines, MIND Seminar, Inria, France. Jun 2025
- Poster: Cross-Session BCI Transfer: Global vs. Selective Pooling, ENAR 2026, Indianapolis. Mar 2026