Work experience
Data Science & Analytics Professional | 7+ Years of Experience Driving Insights, Process Optimization, and Innovation
Data Science & Analytics Professional | 7+ Years of Experience Driving Insights, Process Optimization, and Innovation
WVU Medicine is the clinical and academic health system associated with West Virginia University, serving as the state’s largest healthcare provider and a leader in patient care, education, and research. Headquartered in Morgantown, WV, it includes a network of hospitals, clinics, and specialty centers across the region, dedicated to advancing health through innovation, compassionate care, and cutting-edge medical research. WVU Medicine plays a vital role in training the next generation of healthcare professionals and translating scientific discoveries into real-world improvements in patient outcomes and community health.
Role: Research Analyst, Department of Medicine
Tenure: October, 2024 - Present
Responsibilities:
Lead and manage multi-domain research initiatives, including critical care, pulmonary, and sleep medicine, integrating clinical expertise with advanced data analytics using Python, R, SQL, EPIC, and REDCap.
Design and implement predictive and geospatial models for CDC and NIH funded projects, including machine learning frameworks to assess vaccine uptake and rural health disparities.
Conduct comprehensive EHR-based analyses to uncover key clinical patterns, disease predictors, and outcome disparities, supporting peer-reviewed abstracts, posters, and national conference presentations.
Collaborate across disciplines with clinicians, data scientists, and IT specialists to optimize clinical workflows, quality improvement (QI) initiatives, and research study protocols.
Mentor fellows, residents, and research staff in data analytics, study design, and reproducible workflows, fostering a collaborative, data-driven research environment.
Current Projects:
Title: Machine Learning–Driven Predictive Modeling for Vaccine Receptivity: Optimizing Resource Allocation in Rural Appalachia (CDC Project)
Duration: Oct 2024 - Present
• Developed and implemented advanced machine learning and deep learning models using Python, TensorFlow, and Scikit-learn to predict vaccine uptake across rural Appalachian populations, leveraging demographic, socioeconomic, and health behavior data.
• Collaborated with CDC and WVU Medicine researchers to analyze vaccine receptivity trends, identify high-risk geographic clusters, and support data-driven resource allocation and public health outreach strategies.
• Enhanced model performance through iterative validation, achieving over 89% prediction accuracy, and provided actionable insights to inform policy-level vaccination strategies and community health interventions.
Title: INSPIRE Study: Data Analysis of Upper Airway Stimulation Therapy Outcomes in Obstructive Sleep Apnea
Duration: Aug 2025 - Present
• Performed statistical analysis of pre and post-operative sleep study data from the INSPIRE Upper Airway Stimulation project to evaluate treatment outcomes for Obstructive Sleep Apnea (OSA) patients.
• Identified correlations between facial structure and body position and how these influence therapeutic response, contributing to improved patient selection criteria and postoperative evaluation strategies.
Title: Comparative Outcomes of Mechanical Thrombectomy and Anticoagulation Therapy in Pulmonary Embolism Patients
Duration: Sep 2025 - Present
• Conducted a retrospective data analysis of 191 patients diagnosed with acute Pulmonary Embolism (PE), comparing outcomes between mechanical thrombectomy (MT) and anticoagulation (AC) treatment groups.
• Performed statistical tests on clinical, demographic, and biomarker data (e.g., Troponin, B-type natriuretic peptide, Charlson Comorbidity Index), identifying a significant difference in Troponin levels (p = 0.0003) between groups.
Title: Right Heart Catheterization Data Analysis in Group 1 Pulmonary Hypertension Patients
Duration: Oct 2025 - Present
• Conducted a comprehensive analysis of Right Heart Catheterization (RHC) data from 61 Group 1 Pulmonary Hypertension patients, evaluating demographic, comorbidity, and hemodynamic parameters.
• Performed Kaplan-Meier survival analysis and Cox proportional hazards model based on functional class (I–IV), identifying a statistically significant association with patient survival (p = 0.0016).
Title: Randomized Controlled Trial for Early Detection of Sleep Apnea in Acute Stroke Patients
Duration: Oct 2024 - Present
• Designed a randomized controlled trial comparing an intervention group (in-hospital sleep consultation and structured follow-up) versus standard care to improve timely completion of sleep studies post-stroke.
• Aimed to set a new standard in stroke care by enabling early diagnosis and treatment of obstructive sleep apnea, thereby enhancing recovery and reducing long-term health impacts.
Title: Bariatric Surgery & Prediabetes: A 5-Year Propensity Score-Matched Analysis (NIH Project)
Duration: Oct 2024 - Present
• Compared long-term metabolic outcomes in prediabetic patients undergoing bariatric surgery versus non-surgical controls by
analyzing 3 and 5 year A1c values to assess progression, regression, and resolution of prediabetes.
• Utilized propensity score matching to control for confounders, delivering robust insights into how bariatric intervention influences
the incidence of sustained prediabetes and diabetes development.
Completed Projects:
Duration: Oct 2024 - May 2025
• Retrospectively analyzed a REDCap database of 2,728 patients using level III unattended polysomnography, identifying that 40% of screened patients had moderate to severe sleep apnea.
• Analyzed associations between sleep apnea severity and key comorbidities such as hypertension, COPD, diabetes, and congestive heart failure to uncover population-level health disparities.
• Findings contributed to a published abstract at the SLEEP 2024 Conference, supporting data-driven initiatives to enhance inpatient sleep medicine programs in rural hospitals.
Duration: Oct 2024 - Nov 2025
• Conducted a retrospective cohort study of 573 (rural Appalachian population) patients using Kaplan–Meier curves and Cox regression, to evaluate long-term survival and hospital readmission outcomes, adjusting for age, gender, BMI, and comorbidities.
• Identified a “survival paradox”: patients with both OSA and PH demonstrated better long-term survival compared to those with PH alone (HR=1.3, p=0.002).
• Discovered that BMI was inversely associated with hospital readmission rates, suggesting an “obesity paradox” protective effect.
• Supported development of hospital-level sleep medicine quality improvement initiatives and contributed to a peer-reviewed abstract publication and national poster presentation (APSS).
Duration: Jan 2025 - June 2025
• Conducted comprehensive statistical and geospatial analyses of pulmonary embolism patients to assess mortality, readmission, and demographic disparities using Python (Pandas, Seaborn, GeoPandas, Matplotlib, Contextily).
• Integrated geospatial mapping to visualize regional healthcare patterns and rural–urban disparities, highlighting variations in ICU mortality and readmission rates across West Virginia.
• Designed reproducible analysis workflows and advanced visualizations to communicate findings effectively to clinical teams and support evidence-based decision-making.
• Merged electronic health record (EHR) data with geographical indicators (RUCA codes) to identify underserved areas and inform public health resource allocation.
Title: Geographic and Clinical Determinants of Interstitial Lung Disease Severity and Referral Patterns in Rural Appalachia
Duration: Dec 2024 - July 2025
• Analyzed 312 ILD clinic patients (2021–2024) to evaluate referral patterns, geographic access, and pulmonary function outcomes in rural Appalachia.
• Found that greater travel distance (>60 miles) correlated with worse lung function (FEV1, TLC, DLCO) and longer delays from symptom onset to specialty care.
• Conducted geospatial and statistical analysis using Python and Excel, identifying key access barriers and trends in disease severity and referral behavior.
Title: Implementation Science–Driven Quality Improvement to Increase Vaccine Uptake in Pulmonary Medicine Clinics
Duration: Dec 2024 - Aug 2025
• Participated in a multi-institutional American Thoracic Society (ATS) Vaccine Initiative aimed at improving adult vaccination rates (COVID-19, influenza, pneumococcal) in subspecialty pulmonary medicine settings.
• Applied implementation science frameworks - including the Consolidated Framework for Implementation Research (CFIR) and Expert Recommendations for Implementing Change (ERIC) - to identify barriers, facilitators, and context-specific strategies for program deployment.
• Conducted a clinic process workflow analysis using stakeholder focus groups and LucidChart-based mapping to identify intervention touchpoints across patient and provider engagement steps.
• Collaborated with a multidisciplinary team (physicians, IT developers, administrators, public health researchers) to align QI design with implementation science methods for long-term sustainability.
WVU is a renowned public land-grant university located in Morgantown, West Virginia. Established in 1867, WVU is committed to providing world-class education, fostering innovation, and advancing research. With a diverse student body and strong research initiatives, WVU plays a significant role in shaping the future of various industries.
Role: Data Analyst, Graduate Research Assistant
Tenure: January, 2022 - August, 2024
Responsibilities:
Conducted healthcare data analysis and research by using electronic health record data (> 20M data from N3C) to investigate the impact of COVID-19 on mental health and predicting the emergence of new psychiatric illness by Machine Learning modeling.
Used R, SQL, Python, MS PowerBI, and Tableau to analyze large and complex datasets to identify trends and draw actionable insights.
Communicated and presented research findings and insights effectively to different multidisciplinary teams (e.g. CTR N3C).
Completed Projects:
Duration: Jan 2022 – Aug 2024
• Developed and validated predictive models using the N3C dataset (over 19M patient records) with advanced survival analysis and machine learning techniques to identify new-onset psychiatric disorders following COVID-19 infection.
• Optimized model performance through rigorous feature selection and statistical validation (CMH, Likelihood Ratio, Wald, and Log-rank tests), enabling early detection and targeted intervention strategies for high-risk patients.
Duration: Jan 2023 - May 2023
• Collaborated with a multidisciplinary team to design and implement two innovative products - bionic aircraft bracket and load bearing bridge.
• Applied comprehensive statistical analysis to assess the products' performance and identify areas for improvement using R, SQL, SAS, Python, MS PowerBI, Autodesk AutoCAD, Netfabb, Fusion 360 and different MS tools like MS Word, Excel and PowerPoint.
Duration: Aug 2022 - Dec 2022
• Developed an energy consumption model and employed data analysis techniques to investigate the impact of process parameters on energy consumption.
• Utilized various data analysis and automation tools, including R, SAS, SQL, Python, MS Excel, and MS PowerPoint for data cleaning, manipulation, visualization, and model building.
Duration: Aug 2022 - Dec 2022
• Conducted data analysis to optimize the use of CAD (Computer-Aided Design), Multi-Axis CAM (Computer-Aided Manufacturing) and Generative Design.
• Collaborated closely with interdisciplinary team members to ensure the project's alignment with industry standards.
Stylex Collection is a leading Bangladeshi company specializing in the design, manufacturing, and export of premium garments and textile products. Established to serve the global demand for high-quality fashion, Stylex has expanded its operations internationally, with exports contributing significantly to its yearly revenue of over $50 million.
Role: Senior Supply Chain Analyst
Tenure: April, 2018 - November, 2021
Responsibilities:
Achieved a 10% reduction in supply chain costs by implementing data-driven solutions for inventory management, resulting in the optimization of stock levels and a decrease in both stockouts and overstock situations.
Improved supply chain efficiency by 15% through regression analysis, ANOVA, multilevel models, and root cause analysis. This involved streamlining processes and reducing lead times to address supply chain inefficiencies.
Led cross-functional collaboration with production teams to gather and structure extensive manufacturing data. Proficiently utilized tools like R, SAS, SQL, Python, MS Excel, Tableau, and Power BI to create comprehensive reports and visually compelling presentations, showcasing advanced data management, organization, and visualization skills.
Asmara International Ltd. is a global fashion supply chain company, specializing in product design, development, and sourcing for the apparel industry. Headquartered in Hong Kong, Asmara operates across 13 countries, working with some of the world's leading brands and retailers. With a commitment to sustainability and innovation, the company generates over $1 billion in annual revenue.
Role: Quality Assurance Engineer and Technician
Tenure: March, 2017 - March, 2018
Responsibilities:
Collected and analyzed statistical quality data from four factories, identifying and rectifying over 100 areas for improvement, resulting in a 25% increase in product quality and compliance with industry standards.
Collaborated with operating staff to interpret and implement Lean Six Sigma quality control standards and acted as a catalyst for change and improvement, resulting in a 20% reduction in defects and a 15% increase in production efficiency.
Successfully secured 95% sample approvals from buyers and reduced the lead time by 5 days, demonstrating effective project management and time management skills.