- PhD, Civil Engineering (Transportation), 2015–2019, Iowa State University, USA. CGPA: 3.81/4, Minor: Statistics.
Topic: Evaluation of Safety using driver’s speed and lane merge scenario in Work Zones using SHRP2 NDS data. Advisor: Dr. Shauna Hallmark
- M.S, Civil Engineering (Transportation), 2013-2015, Iowa State University, USA. CGPA: 3.96/4
Topic: Safety evaluation of roadway segments provided with Safety Edge in Iowa. Advisor: Dr. Shauna Hallmark
- B. Tech, Civil Engineering, 2006-2010, Meghnad Saha Institute of Technology, West Bengal, India
RELEVANT COURSES (at ISU)
TRANSPORTATION ENGINEERING/GIS
- C E 551- Urban Transportation Planning and Modelling:Urban transportation planning context and process. Project planning and programming. Congestion, mitigation, and air quality issues. Transportation data sources. Travel demand and network modeling. Use of popular travel demand software and applications of geographic information systems.
- CE 553-Traffic Engineering: Driver, pedestrian, and vehicular characteristics. Traffic characteristics; highway capacity; traffic studies and analyses. Principles of traffic control for improved highway traffic service. Application of appropriate computing software and tools.
- CRP 551- Introduction to Geographic Information Systems: Introduction to geographic information systems, including discussions of GIS hardware, software, data structures, data acquisition, data conversion, data presentation, analytical techniques, and implementation procedures. Laboratory emphasizes practical applications and uses of GIS.
- CE 556- Transportation Data Analysis: Analysis of transportation data, identification of data sources and limitations. Static and dynamic data elements such as infrastructure characteristics, flow and operations-related data elements. Spatial and temporal extents data for planning, design, operations, and management of transportation systems. Summarizing, analyzing, modeling, and interpreting data. Use of information technologies for highways, transit, and aviation systems.
- CE 558- Transportation System Development and Management: Study of designated problems in traffic engineering, transportation planning, and development. Forecasting and evaluation of social, economic, and environmental impacts of proposed solutions; considerations of alternatives. Formulation of recommendations and publication of a report. Presentation of recommendations in the host community.
- CE 552- Traffic Safety, Operations, and Maintenance: Engineering aspects of highway traffic safety. Reduction of crash incidence and severity through highway design and traffic control. Accident analysis. Safety in highway design, maintenance, and operation.
- STAT 551- Time Series Analysis: Concepts of trend and dependence in time series data; stationarity and basic model structures for dealing with temporal dependence; moving average and autoregressive error structures; analysis in the time domain and the frequency domain; parameter estimation, prediction and forecasting; identification of appropriate model structure for actual data and model assessment techniques. Possible extended topics include dynamic models and linear filters.
- STAT 406- Spatial Data Analysis: The analysis of spatial data; geo-statistical methods, mapping and spatial prediction; methods for areal data; models and methods for spatial point processes. Emphasis on application and practical use of spatial statistical analysis. Use of R and R packages for spatial data analysis.
- STAT 579- Introduction to Statistical Computing: An introduction to the logic of programming, numerical algorithms, and graphics. The R statistical programming environment will be used to demonstrate how data can be stored, manipulated, plotted, and analyzed using both built-in functions and user extensions. Concepts of modularization, looping, vectorization, conditional execution, and function construction will be emphasized.
- STAT 401/587- Statistical Methods For Researchers: A first course in statistics for graduate students from the applied sciences. Principles of data analysis and scientific inference, including estimation, hypothesis testing, and the construction of interval estimates. Statistical concepts and models, including group comparison, blocking, and linear regression. Different sections are designed for students in various disciplines, and additional methods covered may depend on the target audience. Topics covered may include basic experimental designs and analysis of variance for those designs, analysis of categorical data, logistic and log-linear regression, likelihood-based inference, and the use of simulation.
- STAT 407- Methods of Multivariate Analysis: Techniques for displaying and analyzing multivariate data including plotting high-dimensional data using interactive graphics, comparing group mean vectors using Hotelling's T2, multivariate analysis of variance, reducing variable dimension with principal components, grouping/classifying observations with cluster analysis and discriminant analysis. Imputation of missing multivariate observations.
- Stat 447/588- Statistical Theory for Research: Provides an introduction to the theoretical basis of fundamental statistical methods for graduate students in the applied sciences. Probability and probability distributions, moments and moment generating functions, conditional expectation, and transformation of random variables. Estimation based on loss functions, maximum likelihood, and properties of estimators. Sampling distributions, exact and asymptotic results, and the development of intervals. Principles of Bayesian analysis, inference from posterior distributions, and optimal prediction. Uses simulation to verify and extend theoretical results.
- R package for Statistical Analysis: Visualizing complex data, managing large data sets, integrating, manipulating and analyzing data, statistical modelling (count data, random effects, hierarchical Bayesian, ordered logit; Time series analysis, spatial data analysis, PCA, factor analysis and cluster analysis.
- JMP, SPSS, NLOGIT5: statistical modelling (count data, random effects, ordered logit).
- Advanced Excel, Tableau, ArcGIS 10.2.2, Microsoft Office.
- ITSM: Interactive Time Series Modelling Software.