Students may want to do their Thesis or Major Research Project on an industrial research topic. Listed below are some industrial research topics and associated researchers. Interested students should contact the professors to see if their background and abilities are suitable for the project.
Students will have access to Bloomberg terminals and could take their certification courses. For more details see Bloomberg Education
Risk Assessment and Modelling for a Fund of Hedge Funds (FHF)
The project proposes the development, implementation and testing of various state of the art
stochastic models for the returns of a FHF. One can model a FHF portfolio as a combination of
financial securities or as a security itself. In any of these instances, one is confronted with
alternative modeling opportunities that require the application of stochastic and statistical
tools needed to set up the models as well as to assess their predictive power. The project
requires stochastic covariance modeling, with a focus on multivariate GARCH models,
estimation of continuous and discrete time models as well as advances in numerical analysis,
probability and statistics.
Professors Sebastian Ferrando presently works with Sigma Analysis &
Management Ltd. Modeling their FHF portfolio
(https://www.sigmanalysis.com/)
Stochastic Modelling and Simulation for Systems Biology
One of the great challenges of the post-genomic era is to investigate an organism as a whole, an interacting system of genes, proteins and biochemical reactions. This is the approach of Systems Biology and it employs mathematical and computational models as essential tools of investigation. Applications include understanding disease and designing improved treatment. One research area focuses on stochastic modelling and simulation of key biological processes. Noise, which arises due to random molecular interactions, may be significant when low molecular populations are present in the system, as is the case of gene regulatory networks. Professor Silvana Ilie collaborates with biotechnology industrial companies on research projects funded by MITACS. Examples of projects include the development of mathematical models and simulation strategies for the aptamer selection process, with applications to the identification of commercially important agricultural targets and the wheat sequence variation prediction by peptide mass spectra.
Modelling and Searching Complex Networks in the Big Data Era
In the big data era, data is considered as the new fossil fuel. Every
human-technology interaction, or sensor network, generates new data
points that can be viewed, based on the type of interaction, as a
self-organizing network. In these networks (for example, the Facebook
on-line social network) nodes not only contain some useful information
(such as user's profile, photos, tags) but are also internally connected
to other nodes (relations based on friendship, similar user's behaviour,
age, geographic location). Such networks are large-scale,
self-organizing, decentralized, and evolve dynamically over time.
Understanding the principles driving the organization and behaviour of
complex networks as well as algorithms based on these networks is
crucial for a broad range of fields, including information and social
sciences, economics, biology, and neuroscience.
Professor Pawel Pralat works with industry partners on various applied projects
related to both modelling and searching of complex networks. For more
details, see:
http://www.math.ryerson.ca/~pralat/applications.html
See also Professor’s Pralat interview:
