# Dr. Andrees Chattha

## Associate Professor in Data Science

Dr. Andrees read mathematics and applied mathematics at the University of London, attaining a doctoral degree in applied mathematics at King’s College London. He held a number of teaching and research positions at various universities in various countries, gaining an international experience of teaching mathematics, statistics, computation and data science at undergraduate and graduate level. At present he is teaching data science and mathematical courses at Forward.

## Educational background

- PhD in Applied Mathematics [Statistical Signal Processing], King’s College, University of London.
- MSc in Statistics and Operational Research, Birkbeck College, University of London.
- MMath in Computational Mathematics with Modeling, Brunel University.
- BSc Honours in Mathematics, Queen Mary College, University of London.

## Research applied mathematics

#### Bayesian numerical signal processing algorithms

The main focus of my research is on the development of statistically based novel algorithms and methodologies for extracting information from distributed random signals and data. This involves the construction of mathematical and statistical models in the area of signal processing and creation of distributed algorithms and methods for parameter and signal estimation.

## Research interest

- Distributed filtering of nonlinear stochastic signals.
- Bayesian Methods for Machine learning and stochastic optimization.
- Distributed estimation and data fusion algorithms.
- Statistical and Monte Carlo based signal processing methods.
- Application of stochastic signal processing and time series analysis.
- Sampling and signal reconstruction algorithms
- Pedagogical research in the teaching of Mathematics/Programming to Engineers and Computer Scientists.

## Research output

- More than 20 publications in the area of distributed stochastic filtering algorithms in signal processing journals and IEEE signal Processing Conferences.
- Book chapters on particle filtering algorithms and Bayesian Computation.
- Supervised eight MSc theses in the area of distributed estimation and consensus algorithms.
- Supervised ten BSc theses in the area of control systems, signal processing and applied mathematics.
- Several technical reports and scientific seminars on fitting surfaces and nonlinear curves to scattered data.

## Teaching & research experience

- Associate Professor of Mathematics, Statistics and Data Science, Forward College, Paris.

- Associate Professor of Mathematics and Statistics, China Agriculture University(CAU/Oklahoma), Beijing
- Associate Professor of Engineering Mathematics, Sino-British College of [USST/Liverpool], Shanghai.

- Assistant Professor of Mathematics and Statistics, Kean University (Wenzhou/New Jersey)
- Assistant Professor of Mathematics and Statistics, University of Essex.
- Teaching and Research Fellow, King’s College London.
- Lecturer of Mathematics, Christ’s College London.
- Lecturer of Mathematics, West London College.
- Lecturer of Mathematics, Greenwich University.

## Mathematical & Statistics courses taught

- Mathematical analysis – Real Analysis, Complex Analysis, Harmonics Analysis, Fourier Analysis, signal analysis, Measure Theory and Integration. Application of analysis in engineering, physics, mechanics, finance…
- Linear/Abstract Algebra Symbolic Computation with Maple –Vector Spaces, Vector Calculus, Matrix Computation, Hilbert and Functional Analysis, Quadratic Forms and Geometry, Algebraic Structures: Group/Ring and Field Theory. Visualization and animation of algebraic objects Maple and symbolic algebraic computing using Maple. Application in other areas.
- Numerical Mathematics, Programming in Java and C, Python, R, Matlab – Numerical schemes for differential and partial differential equations, Numerical methods for integral equations, Numerical linear algebra and Optimization, Dynamical Systems, Methods of Operational Research. Approximation methods for fitting curves and surfaces to numerical scattered data.
- Deterministic and Stochastic Modelling/Simulation – Electronics, Communication and Control systems, Signals and Networks. Realization and Computer Models
- Probability and Statistics – Mathematical and Statistical Theory of Signals – Theory of stochastic processes, Numerical methods for stochastic differential equations, Stochastic filtering theory, Statistical modelling of time series data and parameters estimation, data classification and data clustering algorithms, Stochastic geometry and random graphs. Application in signal processing and mathematical finance.
- Discrete mathematics Number theory – Combinatorics and graph theory. Recursive algorithms and computation. Methods for solving difference equations. Logic and Boolean algebra. Digital logic circuit analysis. Coding theory. Elementary and computational number theory. Cryptography.
- Further Stochastics – Queuing and network theory, Markov Process and application to the dynamics of population, Stochastic calculus and Brownian motion (modelling options), Classification and selection of models. Renormalization methods for studying the stochastic networks.

## Current teaching

- ST2195: Programming for Data Science (R and Python )
- ST2133: Advanced statistics: distribution theory
- ST2134: Advanced statistics: statistical inference
- ST2187: Business analytics, applied modelling and prediction

## Professional membership

- Member of the Institute of Mathematics and its Application (IMA).
- Member of the Society of Industrial and Applied Mathematics (SIAM).
- Member of the British Computer Society
- Member of the IEEE Signal Processing Society.