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
- Abstract algebra and group theory
- Ring theory and field theory
- Linear and multilinear algebra
- Real analysis
- Complex analysis
- Functional analysis and operator theory
- Harmonic analysis
- General and algebraic topology
- Mathematical logic and set theory
- Lie groups and Lie algebras
- Ordinary and partial differential equations
- Dynamical systems and ergodic theory
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
- Probability distributions and limit theorems
- Brownian motion and diffusion processes
- Stochastic calculus
- Random walks
- Queueing theory
- Point processes
- Large deviations theory
- Extreme value theory
- Random matrix theory
- Percolation and random graphs
- Stochastic optimization and stochastic control
- Monte Carlo and simulation methods
- Renewal theory and reliability
- Applications in finance, insurance, and risk
- Interacting particle systems
- Introduction To Stochastic Processes
- Statistics and Probability
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
- Graph theory and its applications
- Enumerative combinatorics
- Algebraic combinatorics
- Extremal combinatorics
- Combinatorial designs
- Coding theory
- Combinatorial aspects of cryptography
- Ramsey theory
- Matroid theory
- Discrete and combinatorial geometry
- Combinatorial optimization
- Network theory and complex networks
- Lattice theory and partially ordered sets
- Boolean functions and discrete structures
- Discrete algorithms and computational complexity
- Random graphs and probabilistic combinatorics
- Additive combinatorics
- Generating functions and recurrence relations
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
- Mathematical modelling and simulation
- Numerical analysis and scientific computing
- Applied partial differential equations
- Optimization and operations research
- Control theory and applications
- Fluid dynamics and continuum mechanics
- Financial and actuarial mathematics
- Approximation theory
- Inverse problems
- Dynamical systems and chaos
- Game theory and decision sciences
- Wave propagation and acoustics
- Asymptotic and perturbation methods
- Integral equations and transform methods
- Calculus of variations
- Matrix Methods
- Newton Laws
- Law of gravitation
- Friction
- Rigid body equilibrium
- Centre of Mass, Centroids
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
- Hypothesis testing
- Bayesian methods and computation
- Nonparametric and semiparametric statistics
- Regression analysis and linear models
- Multivariate analysis
- Design of experiments
- Survey sampling and official statistics
- Time series analysis
- Survival analysis
- Longitudinal and panel data analysis
- Categorical data analysis
- Robust statistics
- Resampling and bootstrap methods
- High-dimensional statistics
- Spatial and spatio-temporal statistics
- Model selection and validation
- Missing data and causal inference
- Biostatistics and clinical trials
- Statistical quality control
- Introduction To Statistical Hypothesis Testing
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
- Numerical methods for differential equations
- Finite element and finite difference methods
- Scientific computing and high-performance computing
- Optimization algorithms and computational optimization
- Machine learning theory and algorithms
- Statistical learning and predictive modelling
- Mathematical foundations of artificial intelligence
- Data mining and knowledge discovery
- Big data analytics
- Monte Carlo methods and stochastic simulation
- Approximation and interpolation algorithms
- Symbolic computation and computer algebra
- Inverse problems and computational imaging
- Uncertainty quantification
- Network and graph analytics
- Data visualization and exploratory data analysis
- Signal and image processing mathematics
Mathematics & Statistics
General mathematical and statistical research spanning algebra, analysis, numerical methods and mathematical finance, including work that crosses the boundaries of the other subject areas.
- Algebra
- Calculus Of Several Real Variables
- Discrete Mathematics
- Introduction To Galois Theory
- Mathematical Finance
- Numerical Analysis
- Matrix Analysis With Applications
- Transform Techniques For Engineers
- Algebra and Number Theory
- Applied Mathematics
- Computation
- Differential Equations
- Linear Algebra
- Mathematical Analysis
- Mathematical Logic
- Probability and Statistics
- Topology and Geometry
- Vectors