Welcome to the Department of Mathematics

The Department of Mathematics is one four  Departments of the School of Science in the college of Science and technology (CST)

MISSION AND VISION

Vision:

The vision of the Department is to become a high level centre for quality teaching and research in mathematics and statistics that has an international reputation, providing graduates with a strong and broad background in mathematics and statistics and the experience needed to work successfully in private and public sectors in Rwanda and in the region.

Mission:

Transmit mathematics knowledge and application from one generation to another, conduct scientific research, and make mathematics more marketable.

Objectives (to include):

 

- Develop interdisciplinary, problem-based academic programmes aligned with Rwanda's development needs.
- Integrate IT-based resources from around the world.
- Ensure students have the leadership, entrepreneurship and management skills needed to create employment.
- Prepare students for service to their communities and country through applied service learning programmes nationally and internationally.
- Create applied, evidence-driven, research centres focused on problem solving, aligned with Rwanda's development needs.
- Develop continuous education programs for upgrading skills and knowledge.

 

DEPARTMENT STRUCTURE

The Department of Mathematics has two undergraduate programs, MSc in Applied Mathematics and PhD program

 

  1. UNDERGRADUATE PROGRAMS
    1. Applied Mathematics
      1. Programme Rationale and Aims

Rationale

According to the Government’s 2020 Vision, Rwanda would like to be a modern nation, able to generate and disseminate technological knowledge and innovations in its socio-economic development. In order to achieve this goal, there is a need to build a critical mass of scientists and technicians both quantitatively and qualitatively to meet the needs of the nation. The UR School of Sciences has a mission to offer a comprehensive education in fundamental and applied sciences, to conduct applied research and provide services to the community. 

Today, there is an increasing demand in business, industry and the public sector for graduates with skills in quantitative analysis. One reason for this demand is the need for greater and deeper understanding of quantitative methods and management science in today's organizations, where information technology is providing ever-increasing amounts of data to be processed, interpreted and exploited. The program in Applied Mathematics will offer courses for catering this demand and will enhance the career prospects of those who successfully complete it. All topics will emphasize the application rather than theory and most make extensive use of computer software. Furthermore, the laureates of this program will be able to pursue postgraduates studies (MSc) in Mathematics and Statistics

 

Educational Aims

 

The programme goals are: (i) To develop skills in Applied Mathematics in order to meet the demand by industry in Rwanda and other neighbourhood and abroad countries. (ii) To develop research skills for study anticipating a MSC in Mathematical Sciences or  in  Applied mathematics (iii) To contribute to the development of highly knowledgeable and skilled researchers in mathematics in Rwanda (iv) To produce independent researchers who are broadly educated in mathematics for productive research and who can apply their knowledge in modern industry.  (v) To develop skills in entrepreneurship and leadership for the use of mathematical methods for problem solving in any area of the real life Topics in the programme will include problem structuring approaches, elements of applied mathematics, some statistic courses, applied multivariate calculus, numerical methods, prediction selected topics of operations research, some selected topics of actuarial mathematics and actuarial sciences. Students will get a working knowledge of software package like Mat lab, Mathematica, Stata and R as well as software used in applications outside academia.  Students will exit the programme with, a Bachelor Degree with honours according to the UR framework and regulations for undergraduate degrees  

 

  1. Programme structure

 

Year 1: English for Specific purpose, ICT skills, Calculus 1, General Physics 1 ( Mechanics), Linear algebra 1, Analytical geometry 1, Calculus 2,  Analytical geometry, linear algebra 2, analytical geometry 2, ordinary differential equations, general physics 2 ( Electricity and Magnetism), Complex analysis, special functions

Year 2:  General topology and its applications, probability and Statistics, Numerical analysis and Programming, measure theory and integration, functional analysis, Partial differential equations, inferential statistics, Operations research and optimization, mathematical modeling, stochastic Process and time series, Nonlinear analysis, Fluid dynamics, Multivariate analysis

Year three: Optimal control, Differential geometry, Dynamical systems, mathematical biology, research methodology for mathematicians, Econometrics, Entrepreneurship development, Financial Mathematics, Internship, final year project

 

 

 

  1.  MATHEMATICS- STATISTICS
    1. PROGRAMME BACKGROUND, RATIONALE AND INDICATIVE CONTENTS                                        

Rationale

The UR School of Sciences has a mission to offer a comprehensive education in fundamental and applied sciences, to conduct applied research and provide services to the community.

With the 2017 Rationalization, The Department of Mathematics has been requested to update its actual program and to include new more attractive programs. The present is the updated option of Statistics within the former applied mathematics program.

Today’s operations systems provide big data that needed to be analyzed and exploited. For this, there is a high demand for qualified staff in this area. One reason for this demand is the need for greater and deeper understanding of quantitative methods and management science in today's organizations, where information technology is providing ever-increasing amounts of data to be processed, analyzed, interpreted and exploited. The program of Mathematics- Statistics will offer courses for catering this demand and will enhance the career prospects of those who successfully complete it. All topics will emphasize the application rather than theory and most make extensive use of computer software. Furthermore, the laureates of this program will be able to pursue postgraduates studies (MSc) in Statistical sciences

Educational Aims

The programme goals are:

  1. To develop skills in statistics in order to meet the demand by industry in Rwanda and other neighborhood and abroad countries.
  2. To develop research skills for study anticipating a MSC in statistics  or Data Sciences
  3. To contribute to the development of highly knowledgeable and skilled researchers in Statistics and related topics in Rwanda
  4. To produce independent researchers who are broadly educated in statistics for productive research and who can apply their knowledge in modern industry.
  5. To develop skills in entrepreneurship and leadership for the use of mathematical methods for problem solving in any area of the real life

Programme content:

Topics in the programme will include problem structuring approaches, elements of applied mathematics, some statistic courses, applied multivariate calculus, numerical methods, prediction selected topics of operations research, some selected topics of actuarial mathematics and actuarial sciences. Students will get a working knowledge of software package like Mat lab, Mathematica, Stata and R as well as software used in applications outside academia.

Students will exit the programme with, a Bachelor Degree with honors according to the UR framework and regulations for undergraduate degrees

1.2.2. Programme Structure:

Year 1: English for Specific purpose, ICT skills, Calculus 1, Introduction to Statistics , Probability 1, Matrix algebra 1, Analytical geometry 1, Calculus 2,  Introduction to Statistical Computing, Demographic statistics, Survey methodology, analytical geometry 2, ordinary differential equations, Statistical quality control,  Complex analysis

Year 2:  English for Academic purpose, probability theory 2, Numerical analysis and Programming, Markov Chain and Queuing models, functional analysis, Partial differential equations, inferential statistics, Selected topics of Operations research , mathematical modeling, stochastic Process and time series, Stochastic Process, Regression Analysis, Time series analysis and Forecasting, Biostatistics,  Design and Analysis of experiments

Year three: Multivariate statistical Methods , Actuarial and Financial Mathematics, Research methodology, Econometrics, Production of national statistics, survival Analysis, Decision theory, Entrepreneurship Development, Industrial Attachment, Final year Project

 

1.2.3. LEARNING STRATEGY

The competence based learning (CBL) approach is followed as a teaching strategy. This approach is oriented to problem-solving method. The student is the centre of learning process. For each module, lecturer will choose problems to be solved after a short introduction of the method to be used and then, by application students will discover the reality. The role of the lecturer here is “The guide”; the use of software package: MATLAB, Maple, Mathematica, and R will be used  in applications

 

1.2.4. ASSESSMENT STRATEGY

The taught modules are evaluated through assignments and examinations. Assignments consist of computer based exercises and continuous assessment test. Each course lecturer serves as an internal examiner. An external Examiner is appointed to moderate question papers, students’ scripts and reports. The Programme is completed with a final year project. The assessment of final year projects is based on UR regulations.

 

  1. POSTGRADUATE PROGRAMMS
    1. MASTER PROGRAMME IN APPLIED MATHEMATICS                                                                            

              The programme goals are:

  1. To develop skills in numerical computing and modeling, data handling and data analysis in demand by industry in Rwanda and in the region
  2. To develop research skills for study towards a PhD in Mathematical sciences in the area of Scientific computing, inverse problems, image processing, financial mathematics, numerical analysis, dynamical systems, Mathematical statistics, Probability, Actuarial Sciences
  3. To develop capabilities to conduct independent research and development in the subject related to Mathematical statistics 
  4. To produce independent researchers who are broadly educated in mathematical statistics for productive research and who can apply their knowledge in modern industry and/or lecturing.
  5. To  initiate research partnership with national and international institutions/industries

 

 

  1. Programme structure

 

S.N

MODULE CODE

MODULE NAME

CREDITS

 

SEMESTER  ONE

1

MAT 6161

MEASURE THEORY AND PROBABILITY

20

2

MAT 6162

APPLIED FUNCTIONAL ANALYSIS

20

3

MAT 6163

DIFFERENTIAL EQUATIONS AND DYNAMICAL SYSTEMS

20

 

SEMESTER TWO

4

MAT 6261

NUMERICAL LINEAR ALGEBRA

20

5

MAT 6262

PARTIAL DIFFERENTIAL EQUATIONS

20

5

MAT 6263

STOCHASTIC PROCESS

10

7

MAT 6264

RESEARCH METHODOLOGY (Including Seminars)

10

 

SEMESTER THREE

 

OPTION: MATHEMATICAL MODELING AND SCIENTIFIC COMPUTING

8

MAT 6361

MODELLING WITH DIFFERENTIAL EQUATIONS

15

9

MAT 6362

NUMERICAL SOLUTIONS OF ODEs AND PDEs

15

10

MAT 6363

OPTIMIZATION AND APPLICATIONS

15

11

MAT 6364

BIOMATHEMATICS 

15

 

OPTION: STATISTICAL MODELING AND ACTUARIAL SCIENCES

8’

MAT 6365

SAMPLING TECHNIQUES, EXPERIMENTAL DESIGN AND STATISTICAL MODELS

20

9’

MAT 6366

MATHEMATICS FOR INSURANCE AND FINANCE

15

10’

MAT 6367

TIME SERIES

15

11’

MAT 6368

PRINCIPLES OF REMOTE SENSING AND GIS

10

 

 

 

 

 

SEMESTER FOUR

12

MAT 6461

INTERNSHIP

20

13

MAT 6462

MASTER’S THESIS

40

 

2.3.LEARNING AND TEACHING STRATEGY

The problem-oriented part of the Common Core may vary in structure from course to course to include regular weekly class sessions, to include short intensive workshop weeks, or continuous "clinic" investigations. Whatever the structure, each student will, over a period of one year, be exposed to a range of relevant problems in Applied mathematics and scientific computing

 

  1. ASSESSMENT STRATEGY

The taught modules are evaluated through assignments and examinations. Assignments consist of computer based exercises and continuous assessment test. Each course lecturer serves as an internal examiner. An external Examiner is appointed to moderate question papers, students’ scripts and reports. The Programme is completed with a dissertation, lasting at least eight months, which is concerned with Statistics for decision making, policy formulation and complex data set by using statistical methods. An external Examiner is appointed to assess the dissertation according to the UR framework and regulations

 

 

 

  1.  STUDENT PROFILE

 

The programme is designed to students who completed undergraduate studies with at least upper second class in all disciplines with background in mathematics.

Admission into the Programme shall follow the existing UR framework and regulations

 

  1. PROGRAMS UNDER PROCESS OF APROVAL

 

  1. Master of Science in Mathematical Statistics
  2. PhD Programme in Mathematics with three tracks: Pure Mathematics, Applied

mathematics and Statistics

  1. RESEARCH

There are three active research groups in the following areas:

  1. Pure Mathematics: Lie Groups and their Applications, Topology and Measure theory
  2. Applied Mathematics: Mathematical modeling, Inverse Problems, Optimization, Symmetry of Differential Equations and their Applications, Numerical Analysis, Biomathematics, Stochastic processes and Applications, Actuarial mathematics
  3. Statistics: Regression Modelling, Estimation Theory, Statistical Inference Theory, and advanced Linear and bilinear Modelling Analysis

Research Grants:

  1. Mathematical modeling of cardio vascular and respiratory system for patients and sportsmen in Rwanda
  2. EAUMP Research grant 2018-2020

 

  1. STAFF PROFILE

Research group 1. Pure Mathematics

Dr. Shumbusho Michel

Dr. Kurujyibwami Celestin

Dr. Nyagahakwa Venuste

Dr. Rusagara Innocent

Mr. Umutabazi Vincent

Mr. Ruganzu Fidele

Mr. Mugisho Jerome

 

 

 

 

Research group 2: Applied Mathematics:

 

 

Mathematical modeling, Biomathematics, Optimization, Inverse problems, differential equations and their Applications, Numerical Analysis, Stochastic processes and Applications

 

 

  1. Prof. Ntaganda JM
  2. Dr Banzi Wellars
  3. Dr Minani Froduald
  4. Dr Ndanguza Denis
  5. Dr Niyobuhungiro Japhet
  6. Dr Kurujyibwami Celestin
  7. Dr Mpinganzima Lydie
  8. Mr. Nsabimana Jean Paul
  9. Mr Muhirwa Jean Pierre
  10. Mrs Mukeshimana Solange
  11. Mr Maniraguha Jean de Dieu
  12. Mr Ndayambaje Felix

 

  1. Financial and Insurance mathematics
  2. Dr Ndengo Marcel
  3. Mr Muhinyuza Stanislas  (PhD std)
  4. Mr Murara Jean Paul     (PhD std)
  5. Mr Hakizimana JMV    (PhD std)

 

  1. STATISTICS: Regression Modeling, Estimation Theory, Statistical Inference Theory, and advanced Linear and bilinear Modelling Analysis
  2. Dr Nzabanita Joseph
  3. Dr Ngaruye Innocent
  4. Mrs Byukusenge Beatrice (PhD std)
  5. Mrs Umunoza Gasana Emelyne  (PhD std)
  6. Mrs Uwamariya Denyse (PhD std)
  7. Mr Mugemangango Cyprien  (PhD std)
  8. Mr Habyarimana Cassien
  9. Mr Nkurunziza Alexandre
  10. Mr Niyigena Jean de Dieu
  11. Mrs Dushimirimana Justine

 

  1. Mathematical Education
  2. Dr Gahamanyi Marcel
  3. Mr Mugisho Jerome

 

 

 

 

 

RESEARCH

There are three active research groups in the following areas:

  1. Pure Mathematics: Lie Groups and their Applications, Topology and Measure theory
  2. Applied Mathematics: Mathematical modeling, Inverse Problems, Optimization, Symmetry of Differential Equations and their Applications, Numerical Analysis, Biomathematics, Actuarial sciences
  3. Statistics: Linear Models ( Bilinear and trilinear models)

Research Grants:

  1. Mathematical modeling of cardio vascular and respiratory system for patients and sportsmen in Rwanda
  2. EAUMP agreement 2018-2020

 

REGIONAL AND INTERNATIONAL COLLABORATION:

  1. Eastern Africa Universities Mathematics Programme (EAUMP). Participating departments are:

Department of Mathematics of University of Rwanda, Makerere University, University of Dar es Salaam, University of Zambia and the School of Mathematics of the University of Nairobi. The overall purpose of this network is to strengthen pure mathematics in the region through postgraduate training and research.

Through this network, annual summer school is organized with the support of ISP and ICTP, international conferences are organized.

  1. Applied Mathematics and Statistics subprogram under Sida bilateral Program: The Department of Mathematics is collaborating with the Department of Mathematics of Linkoping University and Stockholm University in implementation of the subprogram of applied mathematics and Statistics. Through this program, 6 Staff have been trained in PhD programme and graduated on the Sandwich model. 5 staff are still under training in this mode.

With the support of this program the Masters Programme in Applied mathematics is operational since 2008. More than 47 students graduated from this Programme and a big number is serving the University of Rwanda and others public and private institutions in Rwanda

 

  1. East Africa centre for Mathematical Research (EACMaR). The Department of Mathematics at UR, MAK and UDSM in Collaboration with Swedish partners and IUCEA establish a regional centre for mathematical research. The purpose of this centre is to promote regional collaboration, mobility of students and staff, benchmarking and regional research groups.
  2. Others regional and International Collaboration:

CIMO/HEI-ICI, ISP, CIMPA, LMS, DAAD, EMS, AMS, AIMS, EAIFR-ICTP

 

 

RECENT PUBLICATIONS

  1. Ntaganda Jean Marie, Perturbation-Iteration Method for Solving Mathematical Model of Blood Partial Pressures in Human Cardiovascular-Respiratory System during Physical Activity, International Journal of Scientific and Innovative Mathematical Research (IJSIMR), Academics'Research center (ARC), PP 1-8, ISSN 2347-307X (Print) & ISSN 2347-3142 (Online), 2017
  2. L. N. Nkamba, J. M. Ntaganda, H. Abboubakar, J. C. Kamgang, Lorenzo Castelli, Global Stability of a SVEIR Epidemic Model: Application to Poliomyelitis Transmission Dynamics, Open Journal of Modelling and Simulation, Scientific Research Publishing, pp 98-112, ISSN Online: 2327-4026, ISSN Print: 2327-4018, 2017
  3. Innocent Ngaruye, Joseph Nzabanita, Dietrich von Rosen &Martin Singull, Small Area Estimation under a Multivariate Linear Model for Repeated measures Data, Communication in Statistics-Theory and Methods, Vol. 103 No.2, 2016
  4. Innocent Ngaruye, Dietrich von Rosen &Martin Singull, Crop yield estimation at district level for agricultural seasons 2014 in Rwanda, African Journal of Applied Statistics in Statistics, Vol 2, pp. 69-90, 2016
  5. Joseph Nzabanita, Dietrich von Rosen, Martin Singull, Extended GMANOVA Model with a Linearly Structured Covariance Matrix, Mathematical Methods of Statistics, Vol 24 No. 4, 2015
  6. Joseph Nzabanita, Dietrich von Rosen, Martin Singull, Bilinear regression model with Kronecker and linear structures for the covariance matrix, Africa Statistika, Vol. 10 No. 2, 2015
  7. CassienHabyarimana, Martin Singull, Joseph Nzabanita, Extended GMANOVA Model with a Linearly Structured Covariance Matrix, Mathematical Methods of Statistics, Vol. 24 No.4, 2015
  8. Joseph Nzabanita, Dietrich von Rosen, Martin Singull, Estimation of Parameters in the Growth Curve Model with a Linearly Structured Covariance Matrix: A Simulation Study, International Journal of Scientific Engineering and Technology, Vol. 6 No.1, 2017
  9. Vitalij A. Chatyrko and VenusteNyagahakwa, Vitali selectors in topological groups and related semigroups of sets, Questions and answers in General Topology, Vol. 33, pp. 93-102, 2015
  10. Berntsson, Fredrik, Munyeshyaka Jean Marie Vianney, Ndahayo, Fidele, and Nyalihama, Yves, Simulation of a Shieleded Thermocouple, Rwanda Journal, Vol. 27 Series C, pp. 3-11, 2012
  11. Ndanguza D. 2013 Numerical and Nonlinear Analysis: Applications to Real Life Problems, LAP LAMBERT Academic Publishing (May 31, 2013), ISBN-10: 3659396648, ISBN-13: 978-3659396649

 

  1. Ndanguza D. Haario H. 2012. Analysis of SDEs applied to SEIR epidemic models, Conference paper, Bahir Dar Ethiopia. http://inverse-problems.org/presentation_bahir_dar.pdf
  2. Bonus Ntagengerwa, Felix Hagenimana, Denis Ndanguza, JojakMung’atu, Modeling Drivers of Fertility in Rwanda, International Journal of Mathematics and Physical Sciences Research, Vol. 5, No.1, pp. 30-40, 2017

 

  1. J. Niyobuhungiro, Subdifferentiability of infimal convolution on Banach couples, Functiones et approximation, Vo. 52, pp. 311-326, 2015
  2. Marie Emmanuel Ntigura, Ndahayo, Fidele, and Berntsson, Fredrik, Air Pollution Tracking Using PDEs, Rwanda Journal, Vol. 27, Series C, pp. 63-69, 2012
  3. Herberthson, Magnus, Minani Froduald, Nzabanita Joseph, and Turesson, BengtOve, Static Equilibrium configurations of charged Metallic Bodies, Rwanda Journal, Vol 27, Series C, 2012
  4. Fredrik B, Vladimir Kozlov, MpinganzimaLydie, An accelerated alternating procedure for the Cauchy problem for the Helmholtz equation, Computers and Mathematics with Applications, Vol. 68 (1-2), pp. 44-60, 2014
  5. Nzabanita Joseph, Estimation of parameters in the extended growth curve model with a linearly structured covariance matrix, Acta et CommentationnesUniversitatisTartuensis de Mathematica, Vol. 16, 1-20, 2012
  6. W. Banzi,Virunga Mountain Gorilla Population Dynamics: Age structured model,  East Africa Journal of Science and Technology, Vol 3(1), pp. 265-284, http://eajournal.unilak.ac.rw/index.php/publications/eajst-volume-3-issue-1
  7. Banzi Wellars: Modeling the competition from Virunga Mountain Gorilla with Golden Monkey in the Volcanoe National Park. East Africa Journal of Science and Technology. Vol (5)  Issue 2. PP 50-66 ISSN:2227-1902 (online) http://eajournal.unilak.ac.rw/index.php/publications/eajst-volume-5-issue-2
  8. Banzi Wellars. Human Population Dynamics in Great lakes Countries up to 2050. Kabarak International Conference proceedings.. Pp 151-161, Nakuru 2011
  9. Banzi Wellars, Newton Kantorovich method to solve nonlinear reaction diffusion Systems.  3rd EAUMP conference proceedings. Pp 72-89.  Kampala 2017
  10. Jean Marie NTAGANDA, Benjamin MAMPASSI, 2012 Modelling glucose and insulin in diabetic human during physical activity,ICM2012 Proceeding, Tome1331-344,Fourth International Conference on Mathematical Sciences, ICM2012

 

  1. Jean Marie NTAGANDA, 2012, Modelling blood and pulmonary pressures for solving a performance optimal control problem for sportsmen, International Scholarly Research Network, ISRN Applied Mathematics, Volume 2012, 1-16  Article ID 470143, http://dx.doi.org/10.5402/2012/470143

 

  1. Jean Marie Ntaganda, Benjamin Mampassi, 2012 CARDIOGUI: An Interface Guide to Simulate Cardiovascular Respiratory System during Physical Activity, Applied Mathematics, Vol. 3,2000-2006, http://dx.doi.org/10.4236/am.2012.312275

 

  1. Jean Marie Ntaganda, Benjamin Mampassi, 2012 CARDIOGUI: An Interface Guide to Simulate Cardiovascular Respiratory System during Physical Activity, Applied Mathematics, Vol. 3,2000-2006, http://dx.doi.org/10.4236/am.2012.312275

 

  1. Jean Marie Ntaganda, Benjamin Mampassi March, 2013 An Optimal Control Problem for Hypoxemic Hypoxia Tissue-Blood Carbon Dioxide Exchange during Physical Activity,Open Journal of Applied Sciences  Vol. 3. N°156-61 http://dx.doi.org/10.4236/ojapps.2013.31009

 

  1. Jean Marie Ntaganda  October, 2013 Matlab Design for Solving an Orthostatic Stress Optimal Control Problem of Cardiovascular-Respiratory System, International Journal of Scientific and Innovative Mathematical Research (IJSIMR)Volume 1, Issue 2 103-116 ISSN 2347-307X (Print) & ISSN 2347-3142 (Online) http://www.arcjournals.org/pdfs/ijsimr/v1-i2/V1-I2-6.pdf

 

  1. Jean Marie Ntaganda  November, 2013 Fuzzy Logic Strategy for Solving an Optimal Control Problem of Glucose and Insulin in Diabetic Human, Open Journal of Applied Sciences Vol. 3 421-429, http://dx.doi.org/10.4236/ojapps.2013.37052

 

  1. Jean Marie Ntaganda, December, 2013 MATLAB Design for Solving a Mathematical Model of Insulin Dynamic International Journal of Scientific and Innovative Mathematical Research (IJSIMR), Volume 1, Issue 3, 225-233ISSN 2347-307X (Print) & ISSN 2347-3142 (Online) http://www.arcjournals.org/pdfs/ijsimr/v1-i3/ijsimr-v1-i3-7.pdf

 

  1. J. M. Ntaganda, Hopf Bifurcation of a Two Delay Mathematical Model of Glucose and Insulin during Physical Activity, OJAppS, Vol. 4 No 2, pp. 43-55, 2014

 

  1. Marcel Gahamanyi, Jean Marie Ntaganda, MahamatSalehDaoussa, Gaggar, Purturbation-iteration method for solving mathematical model of glucose and insulin in diabetic human during physical activity, Journal of Applied Sciences, Vol. 6 No 12, pp. 826-838, 2016

 

  1. Gilbert MUGENZI, Dr. Joseph K. Mung’atu, Dr. Marcel Ndengo,Modeling the Determinants Influencing Labour Force Participation in Rwanda 2013/14, Using Logistic Regression Approach,  International Journal of Mathematics and Physical Sciences Research ISSN 2348-5736 (Online)Vol. 5, Issue 1, pp: (41-54), 2017

 

  1. Innocent Ngaruye, Joseph Nzabanita, Dietrich von Rosen &Martin Singull, Small Area Estimation under a Multivariate Linear Model for Repeated Measures Data, Communications in Statistics - Theory and Methods ,103(2):1-12, 2016
  2. Cassien Habyarimana, Martin Singull, Joseph Nzabanita, Estimation of Parameters in the Growth Curve Model with a Linearly Structured Covariance Matrix: A Simulation Study", International Journal of Scientific Engineering and Technology, 6(1): 45-49, 2017
  3. Innocent Ngaruye, Dietrich von Rosen & Martin Singull, Crop yield estimation at district level for agricultural seasons 2014 in Rwanda, African Journal of Applied Statistics ,3(1): 69-90, 2016
  4. Minani Froduald, Hausdorff contninuous viscosity solutions of Hamilton-Jacobi equations, Sprink link,  Book series LNCS Vol. 5910, Large scale scientific computing, 2009,  pp 231-238
  5. C.Tuyizere, J.K Mung’atu, D.Ndanguza, (2017), Rainfall Forecasting in Gasabo District Using markov Chain Properties. International Journal of Scientific Engineering and Technology. Vol. 6 Issue 4 ISSN 2277-1581  pp 128-131   SCI indexing
  6. Nsengiyumva Emmanuel, Josephk. Mung’atu, Denis Ndanguza (2017), Queuing models, Application to Kibagabaga hospital services. International Journal of Mathematics and Physical Research. Vol. 5 Issue 2. Research Publis Journal.  ISSN 2348-5736 pp 31-39   CiteSeer, citeulike,Academia.edu, Google
  7. Ndanguza  Denis, Mutarutinya Vedaste (2017), A model perception on independence of PhD students in promoting the research. Rwanda Journal of Education. Volume 4, Issue 1. Rwanda journal
  8. Ndanguza Denis, Isambi S, Mbalawata, Heikki Haario, Jean M. Tchuenche (2017) Analysis of bias in an ebola epidemic model by extended kalman filter approach. Mathematics and computers in simulation. Vol 142 Elsevier. Science Direct pp 113-129
  9. Cyemezo H, Mung’atu J, Ndanguza Denis (2017), System Delivery Service: A case study of Banque Populaire du Rwanda Kimironko Branch. International Journal Of Scientific Engineering and Technology. Vol 6 issue 10  pp 331-334
  10. Muhirwa Jean Pierre, Ndanuza Denis (2017), Effect of Random Noise, Quasi random Noise and systematic Random noise on Unknown Continuous Stirred Tank reactor. Applied Mathematical Sciences. Vol 11 Issue 62 HIKARI Ltd pp 3051-3071
  11. Bonus Ntagengerwa, Felix Hagenimana, Denis Ndanguza,  Joseph K Mung’atu, (2017), Modelling Drivers of fertility in Rwanda. International Journal of Mathematics and Physical Sciences Research Vol 5 Issue 1. Research Publishing journals 30-40 ISSN 2348-5736