Raazesh Sainudiin
Senior Lecturer
Department of Mathematics and Statistics
University of Canterbury
Private Bag 4800
Christchurch 8041, NEW ZEALAND
Room 326, Rutherford Building (Room 724, Erskine Building)
Telephone: +64 3 364 2987 extn 7682 (ext 7691)
Fax: +64 3 364 2587
Email: this address
Office Hours: Email for appointment
Home Address, Phone: 2 Clonbern Place, Upper Riccarton, Christchurch 8041, +64 03 343-4459
Raazesh Sainudiin coordinates the Laboratory for Mathematical Statistical Experiments, is a member of the Biomathematics Research Centre, and is a senior lecturer at the Mathematics and Statistics Department of University of Canterbury, Christchurch, New Zealand.
Research Interests
Statistical inference of stochastic processes embedded within stochastically evolving networks. Examples include statistical decision problems in population genetics, phylogenetics, ecological genetics, and set-valued statistics.
Read current research proposals for details, or view my applied Mathematical and Statistical Genetics publications listed by PubMed and collaboration network from the biomed experts research profile. A MathsReach video interview on "Making Sense out of Chaos" and a Biosketch.
Current Projects
Ongoing projects are grouped into the following two seemingly unrelated areas. |
Mathematical and Statistical Genetics
- Algebraic statistics of combinatorial stochastic processes in population genetics
- Multi-resolution lumped n-coalescents and ancestral recombination graphs in population genetics
- Constructions of spatial coagulation-fragmentation processes
- Approximate sufficiency via n-coalescent experiment graphs in population genetics
- LCE: A C++ class library for lumped coalescent experiments in population genetics
Set-valued Statistical Algorithms
- Extending integer, real and interval arithmetic to multi-dimensional metric data structures
- Exact trans-dimensional samplers
- Non-parametric set-valued statistics for classifiers of spherically homeomorphic 3D shapes
- Non-parametric high-dimensional density estimators and set-valued plug-in estimators for massive data
- Rigorous prameter estimation in non-linear systems with machine-representable sub-sigma algebras -- a collaboration between Laboratory for Mathematical Statistical Experiments and Computer-Aided Proofs in Analysis, a research group in the Department of Mathematics, Uppsala University, Sweden.
- MRS: A C++ class library for statistical set processing in computational statistics
Last modified on Tuesday, 03-Jan-2012 20:15:02 NZDT and served on Friday, 10-Feb-2012 11:54:10 NZDT.