I am a data scientist, quantitative researcher, and Assistant Professor of Kinesiology at DePauw University. I have more than seven years of experience designing experimental and observational studies, building statistical and machine learning models, and translating complex findings into practical decisions.
My work combines data science, applied machine learning, Bayesian statistics, causal inference, behavioral science, and neurophysiological research. I am especially interested in developing reproducible analytical tools for healthcare, biopharma, clinical research, and human performance.
- 🎓 M.S. in Analytics, Computational Data Analytics track, from Georgia Tech
- 🧠 Ph.D. in Kinesiology with a concentration in Motor Behavior
- 👨🏾🏫 Assistant Professor of Kinesiology and Director of the Motor Control Lab at DePauw University
- 💊 Data Science Intern with the Chinese American Biopharmaceutical Society
- 📊 Experienced in statistical modeling, predictive analytics, experimental design, and longitudinal data analysis
- 🔬 Research interests include Parkinson's disease, motor control, clinical analytics, and neurophysiological data
- 🌱 Currently expanding my work in applied AI, deep learning, and biopharmaceutical data science
- 📍 Based in Greencastle, Indiana
- Classification, regression, and clustering
- Convolutional neural networks
- Transformers
- Recurrent neural networks and LSTMs
- Multilayer perceptrons
- XGBoost
- Logistic regression
- Threshold optimization
- Model evaluation and interpretability
- PyTorch
- Scikit-learn
- PyMC
- Pandas
- NumPy
- Matplotlib
- NetworkX
- Bayesian inference
- Causal inference
- Multilevel and mixed-effects modeling
- Longitudinal data analysis
- Repeated-measures analysis
- Hypothesis testing
- Missing-data handling
- Experimental and observational study design
- Statistical analysis planning
- Data visualization and stakeholder reporting
OpenTrial is a Bayesian clinical trial design engine that transforms structured trial inputs into cited and reproducible study-design reports.
The platform integrates evidence from ClinicalTrials.gov, PubMed, openFDA, and DailyMed to support:
- Evidence-derived Bayesian priors with source-level provenance
- Sample-size and statistical-power grids
- Prior-sensitivity analysis
- Predictive probability-of-success criteria
- Simulation-calibrated group-sequential designs
- Adaptive trial designs aligned with FDA guidance
Technologies: Python, Streamlit, Bayesian inference, simulation, clinical data APIs
Developed a deep-learning pipeline for classifying cardiac signal quality using the PTB-XL benchmark dataset.
The project includes:
- A CNN-Transformer hybrid ensemble
- L1-regularized logistic regression, MLP, and CNN baseline models
- Stratified cross-validation
- Youden-index threshold optimization
- Saliency-map analysis
- Per-lead importance analysis
- End-to-end preprocessing, training, evaluation, and interpretability
Technologies: Python, PyTorch, deep learning, signal processing, model interpretability
View ECG Signal Quality Detection
I currently lead the Motor Control Lab at DePauw University, where I design and analyze studies involving motor, behavioral, and clinical outcomes.
My current work includes:
- Developing Bayesian and mixed-effects models for Parkinson's disease research
- Modeling longitudinal treatment outcomes and symptom trajectories
- Building reproducible analysis pipelines for behavioral and neurophysiological datasets
- Teaching sports analytics, biomechanics, predictive analytics, and experimental design
- Developing AI and machine learning tools for biopharmaceutical research
- Communicating technical findings to clinicians, researchers, students, and other stakeholders
M.S. in Analytics, Computational Track
Coursework included deep learning, machine learning, and computational data analytics.
Ph.D. in Kinesiology, Motor Behavior
Dissertation: Visuomotor Rotation Adaptation and Workspace Manipulation: A Behavioral and Cognitive Emphasis
M.S. in Kinesiology
Thesis: Cognitive Load Assessment in Computer-Based Video Training
B.S. in Psychology
My research has appeared in areas including motor behavior, Parkinson's disease, postural control, clinical technology, and remote healthcare delivery.
Selected publications and presentations include:
- Bilateral Transfer of a Visuomotor Task in Different Workspace Configurations
- Subtyping Parkinson's Disease by Symptom Dominance Reveals Group Differences in Postural Sway
- Does Wild Blueberry Supplementation Improve Motor Symptoms and Performance in People with Parkinson's Disease?
- Remote Mentoring Using Telemedicine Platforms
I am interested in opportunities and collaborations involving:
- Applied machine learning
- Healthcare and biopharmaceutical analytics
- Bayesian modeling
- Clinical trial design
- Predictive analytics
- Behavioral and neurophysiological data
- Responsible and interpretable AI
- Data science education
- Reproducible research
