DATA ANALYST | GEOSPATIAL DATA SCIENTIST | NETWORK ANALYSIS SPECIALIST | PUBLISHED RESEARCHER
Analytical and detail-oriented data science graduate student with 1.5+ years of experience in data analysis, visualization, and machine learning. Skilled in Python, Power BI, and SQL, with a strong track record of developing data-driven solutions, automating workflows, and driving operational efficiency.
Passionate about leveraging advanced technologies like Generative AI and digital twins to transform manufacturing processes and optimize decision-making.
Work Authorization: F-1 student with 12-month OPT + 24-month STEM OPT eligibility (up to 3 years of work authorization)
Modeled 35 years of UN Comtrade data (1988-2022) as small-world supply chain networks (238 countries, 16,000+ links). Confirmed 8/9 hypotheses, including 80% fragmentation from China hub removal and Asia-Pacific regional clustering with C = 0.91.
Data-driven analysis of Linux CPU scheduling (CFS) by collecting real process metrics with top, ps, and pidstat on Ubuntu. Compared real scheduler behavior and CPU utilization patterns against textbook algorithms using Python-based analytics and visualizations.
Designed and implemented a fully normalized relational database for a university/company scenario, modeling entities such as students/employees, departments, courses/projects, and enrollments/works_on. Built ER diagrams, enforced integrity constraints, and wrote complex SQL queries, views, and stored procedures for realistic reporting and analytics use cases.
Applied clustering and time-series forecasting to global earthquake data, identifying seismic hotspots and achieving ~85% accuracy on high-risk regions.
Built a browser-based drawing tool and automated regression tests using Selenium IDE to validate functionality and performance.
Analyzed recent cyber-attacks using data mining methods, quantifying business impact and proposing mitigation strategies.
Modeled global supply chains as geospatially embedded small-world networks, integrating network science and spatial analysis. Key findings: pronounced small-world structure, strong regional clustering, and asymmetric resilience under targeted disruptions. Manuscript and code repository will be linked after submission.
Applied clustering algorithms and time-series forecasting to 30+ years of earthquake data, achieving 85% accuracy in high-risk zone identification across 100+ geographic regions.
Earthquake Data Heatmap Analysis: Interactive visualization of global earthquake patterns using machine learning models for seismic hotspot identification and risk assessment.
Phone: Will be availed on request
285 Old Westport Rd
Dartmouth, MA, 02747