Data Scientist | Expert in Physiological Signal Analysis and Clinical Data Integration| Biomedical Researcher
20+ years of experience leading data-driven projects in clinical research and wearable technology. Proven expertise in applying advanced mathematical and statistical techniques to extract actionable insights from large-scale biomedical and physiological datasets. Strong collaborator across multidisciplinary teams, including clinicians and engineers.
Core Skills
Data Science & Analytics
Time-Series Analysis (Linear & Non-linear techniques applied to various biomedical signals), Predictive Modeling, Model Evaluation, Signal Processing, Machine Learning, Feature Engineering, Data Visualization
Programming & Tools
Programming Languages: MATLAB, R, Python
Tools & Libraries: Modeling, Machine Learning, and Data Visualization packages in R and Python, SPSS, JMP, MPlus.
Platforms: Windows Server, High-Performance Computing Systems
Domain Expertise
Biomedical Signal Analysis, Wearable Sensor Data, Neonatal & Cardiovascular Physiology, Gait & Balance, Neurological Disorders, Aging, Research Methodology, IRB Compliance
Leadership & Communication
Project Development & Management, Grant & Manuscript Writing, Mentoring Students, Teaching (Statistics & Health Innovation), Cross-functional Team Leadership
Work Experience
Physiological Data Scientist
Ann & Robert H. Lurie Children's Hospital of Chicago, IL
Apr 2021 – Present
- Analyzed large-scale longitudinal cardiorespiratory data and applied a linear mixed effects model to characterize autonomic nervous system maturation in extremely preterm infants.
- Applied highly comparative time series analysis to predict respiratory outcomes.
- Investigated heart rate and breathing patterns in people with Congenital Central Hypoventilation Syndrome.
- Validated Hexoskin wearable system against gold standard system.
- Co-author on multiple grants, peer-reviewed publications, and conference abstracts.
Assistant Professor
Arizona State University, Phoenix, AZ
Sep 2013 – Mar 2021
- Developed machine learning models (SVM, Neural Networks) and utilized novel features such as Attractor Shape Distribution and the Largest Lyapunov Exponent for classifying balance control and predicting walking patterns.
- Led NIH-funded study to develop a real-time feedback system using IMU sensors for Parkinson’s disease patients.
- Led ASU & Mayo Clinic-funded study to develop a treadmill-based real-time feedback system.
- Applied Association and Agreement analysis using Correlation coefficients (Pearson, Spearman, Lin) and Bland-Altman plots to compare walking patterns obtained from the real-time feedback system with a gold standard.
- Performed Description and Inferential Statistical Methods (ANOVA) to quantify improvements in walking.
- Performed classification of daily activities data from wearables using frequency analysis and thresholding methods.
- Applied Detrended Fluctuation Analysis to detect long-range temporal correlations at multiple time scales to characterize the balance control.
- Taught graduate-level courses in Statistics, Research Methodology, Evidence-Based Practice, Health Technologies.
Research Program Manager
St. Joseph's Hospital and Medical Center, Phoenix, AZ
Jul 2011 – Aug 2013
- Designed posturography protocols for clinical and reimbursement use.
- Predicted shunt surgery outcomes for hydrocephalus patients.
- Set up a gait lab and integrated wearable and platform-based data collection systems.
Research Assistant Professor
Arizona State University, Tempe, AZ
May 2005 – Jun 2011
- Led NIH-funded project to improve walking, balance, and cardiovascular health using a 12-week polewalking exercise regimen in people with Parkinson’s disease.
- Led Paralyzed Veterans of America – funded study to improve cardiovascular health in people with Spinal Cord Injury using electrical stimulation of leg muscles.
- Used RM-ANOVA and post-hoc comparisons to quantify improvements due to exercise programs.
Research Scientist
Arizona State University, Tempe, AZ
Oct 2001 – Apr 2005
- Conducted nonlinear time series analysis of EEG to identify seizure foci and predict epileptic seizure events.
- Developed algorithms to measure information transfer between different brain sites.
Publications & Grants
- Published extensively in data analytics, physiological signal analysis, and clinical research.
- Secured funding from NIH, foundations, and internal university grants as Principal Investigator.
Teaching & Mentorship
- Taught doctoral-level courses in Statistics & Research Methodology and graduate-level courses in Evidence-Based Practice and Health Technologies.
- Mentored numerous graduate and undergraduate students in data analytics, biomedical engineering, and health sciences.
