Narayanan Krishnamurthi

I am a data scientist and researcher.

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.

Education

Doctorate in Applied Mathematics/Biomedical Signal Analysis

Indian Institute of Technology, Madras — Chennai, India

Jun 1993 – Apr 1999