Open to opportunities

Yiting Li

@yitingli

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Data scientist with Ph.D. in Physics specializing in predictive modeling and Bayesian inference.

United States

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What I'm looking for

I am looking for a Data Scientist role that leverages Bayesian and ML modeling, cloud deployment, and scalable ETL; I value collaborative, research-driven teams focused on impactful, production-ready solutions.

I am a data scientist with a Ph.D. in Physics and graduate training in artificial intelligence, focused on transforming complex, high-dimensional data into actionable insights.

I have built learning models and scalable ETL pipelines, deployed solutions on cloud platforms (AWS, GCP), and accelerated large-scale simulations with GPU parallelization to improve runtime and reproducibility.

My work spans hierarchical Bayesian modeling, probabilistic inference, and time-series analysis, with open-source contributions and peer-reviewed publications demonstrating impact and adoption.

I seek to apply rigorous statistical methods, production-ready ML engineering, and domain-aware modeling to drive strategic decision-making in a data-driven organization.

Experience

Work history, roles, and key accomplishments

Current

Postdoctoral Research Fellow

Current

University of Michigan

Sep 2023 - Present (2 years)

Designed hierarchical Bayesian pipelines and probabilistic models for noisy multi-source time-series, and optimized GPU-parallelized Bayesian simulations to reduce runtime 5–10x while improving uncertainty quantification.

Python MultiNest PyTorch CUDA Time Series Analysis

ML/DS Intern

Pear Care

Jun 2025 - Aug 2025 (2 months)

Developed ML algorithms with PySpark and SQL to predict doctor rankings and engineered scalable AWS ETL pipelines, improving predictive accuracy on heterogeneous healthcare data and enabling real-time monitoring.

Pyspark SQL AWS EC2 S3 Lambda ETL Hugging Face Data Engineering

Graduate Researcher

UC Santa Barbara

Sep 2018 - Aug 2023 (4 years 11 months)

Released an open-source Python/Cython package for probabilistic inference and applied Bayesian time-series and spectral analysis to recover weak signals from noisy high-frequency data, producing peer-reviewed publications.

Python Cython Bayesian Time Series Spectral Analysis Unit Testing Nonlinear Optimization (Bézier Fitting)Scientific Computing

Research Assistant

Penn State University

Jan 2014 - Jan 2018 (4 years)

Developed ML methods for signal-noise separation in LIGO data and contributed to NASA/NSF spectrograph projects, while building IoT backends and low-latency computer vision systems for real-world monitoring.

Machine Learning Signal Processing C TensorFlow PyTorch OpenCV AWS

Education

Degrees, certifications, and relevant coursework

University of Texas, Austin

Master of Science, Artificial Intelligence

Pursuing a Master of Science in Artificial Intelligence with graduate-level training in AI and predictive modeling.

University of California, Santa Barbara

Doctor of Philosophy, Physics

2018 - 2023

Activities and societies: Developed open-source probabilistic inference package; presented research at conferences.

Completed a Ph.D. in Physics focusing on probabilistic inference, time-series analysis, and high-dimensional model fitting with published research outputs.