CV

Download my CV (PDF) · Updated September 2026

I am a machine learning researcher and consulting CTO working on AI evaluation, Bayesian statistics and biomedical ML. My research spans benchmark validity, evaluation reporting, uncertainty quantification and participatory AI governance.

Experience

StickFlux Labs — Consulting CTO

Research and consulting on AI evaluation and reproducible ML. Recent work includes social-impact evaluation reporting, benchmark saturation, Bayesian audits of public evaluation archives and multilingual mechanistic interpretability. Engineering experience includes training, hyperparameter-tuning and versioning pipelines for multivariate time-series classification.

University of Chicago — Researcher Data Scientist

Biomedical data science and machine learning, including frailty prediction from accelerometry and clinical covariates, drug–drug interaction prediction, and Bayesian analysis of population health data.

Education

• PhD, Chemistry (Biomedical Data Science), University of Chicago, 2023. Dissertation.
• BA, Chemistry (Distinction) and Mathematics, Music minor, cum laude, Colby College, 2016.
• Visiting student, St Edmund Hall, University of Oxford, 2015. Tutorials in Biochemistry and Mathematics.

Technical skills

• Programming and data: Python, R, C/C++, Julia, Scala, Java, Bash, Perl, SQL and Spark.
• Statistics: Bayesian modelling, uncertainty quantification and calibration with Stan, PyMC and NumPyro.
• Machine learning: PyTorch, JAX/Flax, TensorFlow/Keras, scikit-learn, Transformers, PyTorch Geometric and gradient boosting.
• MLOps and cloud: Weights & Biases, Neptune, Lightning, Airflow, Docker, GCP and AWS.
• Languages: English, Chinese (Mandarin and Cantonese), German and French.

Publications

See the publication list for papers, workshop contributions and preprints, including two ICML 2026 papers on social-impact evaluation reporting and benchmark saturation, two ACM FAccT 2026 papers on participatory governance, and the Evaluation Cards preprint.

Service and recognition

• Core organizer, Queer in AI, 2023–present, including workshop organization and participatory AI-governance research.
• Co-author through Organizers of Queer in AI of Queer In AI: A Case Study in Community-Led Participatory AI, ACM FAccT 2023 Best Paper Award.
• Reviewing includes ICLR, NeurIPS, ACM FAccT, AIES and PLOS Computational Biology.