CV

Current Position

Waiting to join University of West Florida starting Fall, 2024.

Research Interests

Spatiotemporal modeling, Exposure models, Environmental risk mapping, Low-rank methodologies, Data reduction, Random neural networks, Computational statistics, Bayesian analysis.

Education

• 2018–2023. Ph.D. in Statistics, University of Missouri - Columbia.
  • Advisor: Dr. Christopher K. Wikle
  • Thesis topic: Methodologies for low-rank analysis and regionalization for multi-scale spatial datasets
• 2014–2016. Master of Statistics (M. Stat.), Indian Statistical Institute, Kolkata.
• 2011–2014. Bachelor of Statistics (B. Stat.), Indian Statistical Institute, Kolkata.

Work Experience

• Jul 2023 – Jul 2024. Postdoctoral fellow, National Institute of Environmental Health Sciences, Durham, NC. Working on spatiotemporal exposure modeling and supervised environmental risk modeling.

• Aug 2018 – Jul 2019. Intern , Missouri Department of Conservation, Columbia, MO. Served as part of my research assistantship. Worked on species distribution models, small area estimation, and bootstrapping.

• Jun 2016 – Dec 2017. Data Scientist, Deloitte, Hyderabad, India. Addressed statistical queries in various business-related sectors and implement them in user-friendly tools for third-party use. Worked on real-world data from the insurance, banking, medical, and retail sectors.

• May 2015 – Jul 2015. Intern, General Electric, Bangalore, India. Implemented fast heuristic time series algorithms.

• May 2014 – Jul 2014. Intern , FinIQ , Pune, India. Visual Basic-based automation of financial algorithms in Excel.

Research Projects

Accepted Papers

• Schliep, E. M., Wikle, C. K., Daw, R. (2023). Correcting for informative sampling in spatial covariance estimation and kriging predictions. Journal of Geographical Systems , 1 – 27.
• Daw, R. , Wikle, C. K. (2022). Supervised spatial regionalization using the Karhunen-Loève Expansion and minimum spanning tree. Journal of Data Science 20 no. 4. 566 – 584 , DOI 10.6339/22-JDS.
• Daw, R. , Wikle, C. K (2022). REDS: Random Ensemble Deep Spatial prediction. Environmetrics, e2780, 1180 – 4009 , DOI 10.1002/env..

• Daw, R. , Simpson, M., Wikle, C. K., Holan, S. H., Bradley, J. R. (2022). An overview of univariate and multivariate Karhunen Loève expansions in Statistics. Journal of the Indian Society for Probability and Statistics , 1 – 42.

• Chakraborty, S., Menifield, C. E., Daw, R. (2022). Impact of Stand Your Ground, Background Checks, and Conceal and Carry Laws on homicide rates in the US. Journal of Public Management and Social Policy.
• Yeasmin, F., Daw, R. , Chakraborty, B., Gupta, A., Bhattacharya, S., Chakraborty, B. (2021). A new growth rate measure in identifying extended Gompertz growth curve and development of goodness-of-fit test. Calcutta Statistical Association Bulletin , Volume 73, 127 – 145.

Submitted Papers

• Daw, R. , Wikle, C.K., Bradley, J. R., Holan S. H. (2023). MVCAGE: A criterion for multivariate regionalization of spatial data.

Ongoing Papers

• Daw, R. , Messier P. K. (2024+). SBoost: A spatially-aware boosting model for global high- resolutional imputation of NO₂.
• Daw, R. , Messier P. K. (2024+). A supervised dimension reduction methodology for geospatial conditional risk modeling.

Conferences

Invited Talks

• 2021 JSM, (Joint Statistical Meetings). Application of coresets in Spatial Modeling.

Contributed Talks

• 2021 IISA Conference, (International Indian Statistical Association). Application of coresets in Spatial Modeling.
• 2020 JSM, (Joint Statistical Meetings). Uncertainty Quantification and Inference for Spatiotemporal Forecasting via Echo State Mixture Density Networks with Relevance Propagation.

Poster Presentations:

• 2024, Theory and Foundations, Florida State University. SBoost: A spatially-aware Boosting Model for Large Spatial Prediction.
• 2024 Science Day, NIEHS (National Institute of Environmental Health Sciences). SBoost: A spatially- aware Boosting Model for Large Spatial Prediction.
• 2022, Applied Statistics Symposium , ICSA (International Chinese Statistical Association). REDS: Random Ensemble Deep Spatial prediction.
• 2019, Deep Learning Program Opening Workshop, SAMSI (Statistical and Applied Mathematical Sciences Institute). Deep Neural Network in Cusp Catastrophe Model.
• 2018 Innovations in Design, Analysis, and Dissemination , Frontiers in Biostatistical Methods. Analysis on the Effect of the Handgun Laws on Crime Rates.

Teaching Experience

• Spring 2020: Graduate Instructor, Stat 3500, Probability and Statistics - II.
  • Topics : ANOVA, Linear and Logistic Regression, Nonparametric Statistics, Introduction to R.
  • Responsibilities : Instruction and evaluation.
  • Mode : Both online and classroom.
  • Special Mention : Data Analysis project as Final examination.
• Fall 2019: Graduate Instructor, Stat 3500, Probability and Statistics - II.
  • Topics : ANOVA, Linear and Logistic Regression, Nonparametric Statistics, Introduction to R.
  • Responsibilities : Instruction and evaluation.
  • Mode : Classroom.
• Spring 2018: Teaching Assistant.
  • Evaluation of examination and homework of various graduate and undergraduate Statistics courses.

Service & Membership

Peer Review Reviewer for Spatial Statistics , Signal Processing.

Professional Membership

Member of American Statistical Association.

Technical Expertise

• Computing: Matlab, Python, SAS, R.
• Programming: C, C++, LaTex.
• Visualization: Tableau, HTML, JavaScript, JavaScript-D3.
• Cloud: Lewiscluster, Nautilus, Google Colab.

Academic Awards

• 2011–2016: Received the INSPIRE scholarship administered by the Department of Science and Technology of the Government of India.
• 2010: Cleared the Regional Mathematical Olympiad and qualified for the national level.