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Dr.Niaz Murshed Chowdhury
                                         PhD. MS. MSS

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I am an economist, data scientist, and public health researcher with over eight years of experience designing and deploying advanced analytics, economic models, and machine learning solutions to solve real-world challenges. My interdisciplinary background bridges public health, environmental and resource economics, and data engineering, transforming complex datasets into actionable insights for policy, operations, and research.

Currently, I serve as a Lead Researcher at the California Department of Public Health, where I lead the modernization of legacy systems, develop dynamic dashboards, and automate large-scale data pipelines. I work across platforms including SAS Viya, SQL Server, Oracle, PostgreSQL, Python, PowerShell, Databricks, and Power BI. I also specialize in data visualization using tools like Tableau and Power BI, creating intuitive dashboards that support regulatory reporting, policy analysis, and performance monitoring, including EMTALA compliance systems.

I hold a PhD in Econometrics and Quantitative Economics, with my research specialization in  Environmental and Resource Economics,  Fire Science, and Macroeconomics, supported by three master’s degrees in Economics, Econometrics, and Quantitative Economics. My academic research has explored topics such as land-use dynamics in the U.S. Prairie Pothole Region, climate impacts on ecosystems, wildlife management, macroeconomic development in Bangladesh, and foreign reserve and inflation dynamics.

My analytics expertise includes regression modeling (linear, logistic, LASSO), time series forecasting, Bayesian inference, survival analysis, and spatial econometrics. I’ve applied these tools to public health systems, wildfire modeling, patient risk stratification, and economic trend forecasting. In addition, I have hands-on experience with machine learning, deep learning, NLP, and reinforcement learning using Python, R, SAS, STATA, and SPSS.

My work has been cited more than 431 times by national and international institutions, including the USDA, USGS (EROS), International Center for Climate and Global Change Research, and the American Association for the Advancement of Science. My findings have been directly implemented in U.S. government decision-making processes.

Beyond technical execution, I bring strong leadership and cross-functional collaboration skills. I build scalable solutions, streamline data flows, and foster data-informed cultures. My goal is to use data science and economics to support effective governance, evidence-based decision-making, and systems that benefit both people and the environment.

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Ph.D. in Econometrics & Quantitative Economics | University of Nevada, Reno, USA | 2017–2023

Dissertation: Essays in Economics: Analyzing Homeowners’ Willingness-to-Pay for Wildfire Risk Mitigation, and the Overlapping Generations (OLG) Model of Interest Rate Dynamics

M.S. in Econometrics & Quantitative Economics | (En Route with Ph.D.) | University of Nevada, Reno, USA
M.S. in Economics | South Dakota State University, USA | 2014–2016

Thesis: Grassland to Cropland Conversion Decision in the Dakotas: 2015 Producer Survey and Analysis

M.S.S. in Economics | University of Chittagong, Bangladesh | 2013–2014

Research: Determinants of Foreign Exchange Reserve in Bangladesh

B.S.S. in Economics | University of Chittagong, Bangladesh | 2008–2013

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  • Over 10 years of experience in Economics and Public Health Research, specializing in Environmental & Resource Economics, Macroeconomics, and Economic Growth.

  • Proven ability to design and implement advanced statistical models, including linear/logistic regression, time series forecasting, Bayesian analysis, and survival models.

  • Skilled in applying machine learning, natural language processing (NLP), deep learning, and reinforcement learning to solve complex, real-world problems.

  • Expert in data analytics, ETL/ELT processes, and automation of large-scale data pipelines using SQL, Databricks, and modern data tools.

  • Extensive hands-on experience with SAS Viya, SAS EG, SAS Macro, STATA, R, Python, Pandas, Numpy, Scipy, and SPSS.

  • Strong proficiency in dashboard design, data visualization, and reporting using Power BI, Tableau, and custom BI solutions.

  • Demonstrated leadership in modernizing legacy systems, troubleshooting data transmission issues, and ensuring accurate, timely statewide reporting.

  • Broad experience conducting empirical research, survey design, and analysis of large, complex datasets for policy development and stakeholder reporting.

  • Excellent communication and collaboration skills; adept at presenting technical findings to diverse audiences and working cross-functionally with modeling and policy teams.

  • Highly adaptable and committed to process improvement, innovation, and delivering actionable, data-driven solutions that inform strategic decisions.

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I was employed as a Graduate Research Assistant at the University of Nevada, Reno, under the supervision of Dr. Michael H. Taylor and Dr. Mark Pingle in the Department of Economics and Cooperative Extension until the completion of my Ph.D. in December 2023. During my earlier studies at South Dakota State University, I also served as a Graduate Research Assistant at the South Dakota Agricultural Experiment Station under the guidance of Dr. Larry Janssen. My master’s research was supported by funding from the U.S. Department of Agriculture and the National Institute of Food and Agriculture (NIFA).

Currently, I am employed as a Lead Research Data Specialist (Research Data Specialist III) at the California Department of Public Health (CDPH). In this role, I lead the design and development of statewide data pipelines, automate regulatory reporting, build and maintain dashboards, and support policy evaluation efforts, particularly those related to EMTALA compliance. My responsibilities span across platforms such as Python, SAS Viya, SQL Server, Databricks, Tableau, Power BI, Oracle, and PostgreSQL, with an added focus on automation using PowerShell and advanced analytics.

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