Journal Scope
BRJMS Journal Scope
The Boston Research Journal of Mathematics & Statistics welcomes original research across pure and applied mathematics, probability and stochastic processes, statistics, discrete mathematics and computational data science. Browse the research scopes and topics covered within each subject area.
Pure Mathematics
Exploration of fundamental mathematical concepts and structures, including algebra, analysis, geometry, topology, and number theory, for intrinsic understanding.
- Abstract Algebra (Group, Ring, and Field Theory)
- Real and Complex Analysis
- Differential Geometry
- Algebraic Topology
- Number Theory
- Set Theory and Mathematical Logic
- Functional Analysis
- Category Theory
- Measure Theory and Integration
- Representation Theory
- Algebraic Geometry
Probability Theory & Stochastic Processes
The study of randomness and uncertainty, encompassing the mathematical models for random events, variables, and dynamic systems evolving over time.
- Classical and Axiomatic Probability Theory
- Stochastic Differential Equations
- Markov Chains and Markov Processes
- Renewal Processes and Poisson Processes
- Brownian Motion and Random Walks
- Martingale Theory
- Queuing Systems and Applications
- Ergodic Theory and Stationary Processes
- Branching Processes
- Applications in Finance, Insurance, and Engineering
- Random Fields and Spatial Processes
Discrete Mathematics & Combinatorics
Study of finite or countable mathematical structures such as graphs, networks, codes, and arrangements, with applications in computer science and operations research.
- Graph Theory and Network Analysis
- Enumerative and Algebraic Combinatorics
- Combinatorial Designs and Configurations
- Finite Geometry and Projective Spaces
- Discrete Structures in Computer Science
- Coding Theory and Error-Correcting Codes
- Cryptography and Cryptographic Protocols
- Lattice Theory and Boolean Algebras
- Theory of Computation and Complexity
- Permutation and Partition Theory
- Applications in Algorithms and Software Systems
Applied Mathematics
Development and utilization of mathematical methods and models to solve problems in science, engineering, finance, industry, and other practical domains.
- Mathematical Modeling in Physical and Life Sciences
- Numerical Methods and Simulation Techniques
- Optimization and Control Theory
- Differential Equations (ODEs and PDEs)
- Applied Linear Algebra
- Computational Fluid Dynamics
- Mathematical Physics
- Mathematical Biology and Epidemiology
- Financial Mathematics and Risk Analysis
- Inverse Problems and Imaging
- Industrial and Engineering Mathematics
Statistical Theory & Methodology
Foundations and techniques for data collection, analysis, interpretation, and inference, including experimental design, hypothesis testing, and model estimation.
- Statistical Inference and Estimation Theory
- Hypothesis Testing Procedures
- Regression Analysis (Linear and Nonlinear)
- Multivariate Statistical Analysis
- Bayesian Statistics
- Time Series Analysis and Forecasting
- Experimental and Survey Design
- Resampling Techniques (Bootstrap, Jackknife)
- Nonparametric and Semiparametric Methods
- Statistical Learning and Model Selection
- Applications in Health, Social Sciences, and Engineering
Computational Mathematics & Data Science
Application of computational techniques to mathematical problems and the extraction of knowledge from data, including numerical analysis, optimization, and statistical learning.
- Numerical Linear Algebra
- Scientific Computing and High-Performance Algorithms
- Computational Optimization
- Numerical Solutions of Differential Equations
- Data Assimilation and Uncertainty Quantification
- Statistical and Machine Learning Algorithms
- Big Data Analytics and Scalable Computing
- Computational Statistics
- Information Theory and Signal Processing
- Deep Learning and Neural Networks
- Visualization and Computational Geometry