# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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## Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Bayesian Nonparametric Tensor Completion

Data Science, University of Alberta, 2017

In this project, we propose a Bayesian nonparametric method to estimate missing data in tensors. The proposed method uses a Tucker-1 factorization to learn a smaller core tensor and a factor matrix via Gibbs sampling. Unlike most existing Tucker factorization algorithms, the tensor rank is estimated automatically. This is done by employing an Indian Buffet Process prior over the latent matrix, where the estimated number of non-zero columns corresponds to the tensor rank. The missing data is then estimated based on the latent structure learned from the factorization. As a result, the method provides a form of Bayesian model averaging over estimated ranks, allowing it to avoid overfitting. As an illustration, a num- ber of datasets are artificially corrupted at various levels. Relative error is used to assess completion performance and our result is compared to parallel matrix factorization as a baseline. The empirical results show that our method does not beat the current state of the art, but provides pertinent information that other methods do not. That is, statistical quantities, like mean standard error, are readily available due to the Bayesian estimation framework of our method. Lastly, we identify some future avenues for research that may improve performance.

## Epidemiology

Part of my PhD projects., University of Alberta, 2017

I have done substantial research on indirectly and directly transmitted infectious diseases. I formu- lated and analyzed a cholera transmission model, which explicitly includes the dynamics of bacte- riophage and bacteria, and also contains an indirect infection term which accounts for a minimum infectious dose of the pathogen V. cholerae. At the moment, I am working on a multi-scale model for Guppy-Gyrodactylus interactions. This is a collaborative research venture involving G. Fussmann’s lab (biology at McGill university) and my supervisor. In this first phase of the project we are formulating a guppy-gyrodactylus interaction model with distributed delay. The model consists of five coupled nonlinear differential equations tracing the rates of change of guppy population, guppy immune response and gyrodactylus population. The guppy total population is divided into three sub-groups: susceptible, infected, and recovered guppies. This project at the interface of math- ematics and experimental epidemiology attempts to provide an experimental test bed for spatial disease and host-parasite dynamics. The proposed research project provides a unique opportunity to establish an ethically acceptable and cost-effective explorative tool for general infectious disease dynamics. In the next phase of the project, we will modify it to a mathematical model that contains two nested meta-population levels. At the basal level, guppies serve as patches for parasites, which disperse from fish to fish; the higher level consists of fish groups in different patches that are linked through dispersal of parasitized or non-parasitized guppies

Part of my PhD projects., University of Alberta, 2017

I have formulated and investigated a stoichiometric organic matter decomposition model in a chemostat culture that incorporates the dynamics of grazers. I performed complete local and global stability analyses of the system and determined the criteria for the uniform persistence and extinction of the species and chemicals. Moreover, I determined the optimal value of grazers that maximizes degradation of organic matter. Based on the parameters under the control of the ex- perimenter, I performed one- and two-parameter bifurcation analyses. Furthermore, I determined the switching time of bacterial growth rate from carbon dependent to nitrogen dependent and vice versa for different continuous cultures. I determined the sensitivity of the degradation rate with respect to the model parameters. I also discussed numerically how the dilution rate affects the decomposition percentage as well as decomposition speed. In collaboration with M. Lewis (math & biological sciences), T. Siddique (renewable resources), J. Foght (biological sciences), Hao Wang (math) and industrial partners, we have modified this model to a greenhouse gas (GHG) generation model for oil sands settling basins and end pit lakes. First, I fitted the model to naphtha hydrocarbons in tailings from Syncrude’s Mildred lake settling basin to determine optimal parameter values. Next, I extended it to a GHG generation model and then validated the model using measured methane data from Syncrude’s mature fine tailings (MFT). This model is cross validated using the hydrocarbons and methane data from Shell Albian’s MFT.

## Phytoplankton Project

Postdoctoral project, Supervised by Professor Simon Levin, 2017

I am formulating a model for the dynamics of phytoplankton in a stratified lake. Stratification separates the water with a horizontal plane called thermocline into two zones: epilimnion and hypolimnion. The epilimnion is the upper zone which is warm (lighter) and well mixed. The hypolimnion is the bottom colder zone. It is usually dark, and relatively undisturbed. Most deep lakes on Earth are stratified. Phytoplankton, also called algae, grow via photosynthesis in use of nutrients such as phosphorus and nitrogen from water and energy from sunlight. Because the hypolimnion is not well mixed, the change in the phytoplankton density and nutrient concentration in it depends on time and depth in the water column. Phytoplankton can be moved from their position by tur- bulent mixing (diffusion) or by sinking (advection). The change in the phytoplankton density in the epilimnion is independent of the depth (since it is well mixed over night). Light absorption in a lake follows Lambert-Beer’s Law. This novel model is the hybrid of highly interconnected nonlinear partial and ordinary differential equations. I will later extend the model to a bacteria-phytoplankton interaction model in a stratified lake.

## Regulation of the Human plasma glycemia by means of glucose measurements and subcutaneous insulin administration

Published in In the proceedings of the 3rd international congress on intelligent control and automation science, 2013

Recommended citation: Pierdomenica Pepe, Palumbo Pasquale, Jude D. Kong, Saseendran K. Sreeedhar & Panunzi Simona. (2013). "Regulation of the Human plasma glycemia by means of glucose measurements and subcutaneous insulin administration." in the proceedings of the 3rd international congress on intelligent control and automation science. 46(20), 524-529. https://doi.org/10.3182/20130902-3-CN-3020.00036

## Stability and sensitivity analysis of the iSIR model for indirectly transmitted infectious diseases with immunological threshold

Published in SIAM J. Appl. Math, 2014

Recommended citation: Jude D. Kong, William Davis, Xiong Li, and Hao Wang. (2014). "Stability and sensitivity analysis of the iSIR model for indirectly transmitted infectious diseases with immunological threshold." SIAM J. Appl. Math 1. Vol. 74: 1418-1441. http://www.judekong.ca/files/iSIR_Analysis_SIAP.pdf

## A Useful Tool for the Artificial Pancreas. In: Delitala M., Ajmone Marsan G. (eds) Managing Complexity, Reducing Perplexity.

Published in Springer Science & Business Media B.V., 2014

Recommended citation: Jude D. Kong, Sreedhar S. Kumar, Pasquale Palumbo. (2014). "A Useful Tool for the Artificial Pancreas. In: Delitala M., Ajmone Marsan G. (eds) Managing Complexity, Reducing Perplexity." Springer Science & Business Media B.V.. p109-117. https://link.springer.com/book/10.1007/978-3-319-03759-2

## Dynamics of a cholera transmission model with immunological threshold and natural phage control in reservoir

Published in Bulletin of Mathematical Biology, 2014

Recommended citation: Jude D. Kong, William Davis, and Hao Wang. (2014). "Dynamics of a cholera transmission model with immunological threshold and natural phage control in reservoir." Bulletin of Mathematical Biology. Vol. 76: 2025-2051. http://www.judekong.ca/files/CholeraPhageBMB2014.pdf

## The inverse method for a childhood infectious disease model with its application to pre-vaccination and post-vaccination measles data

Published in Bulletin of Mathematical Biology, 2015

Recommended citation: Jude D. Kong, Chaochao Jin, and Hao Wang. (2015). "The inverse method for a childhood infectious disease model with its application to pre-vaccination and post-vaccination measles data" Bulletin of Mathematical Biology. Vol. 77: 2231-2263 https://link.springer.com/article/10.1007/s11538-015-0121-5?wt_mc=internal.event.1.SEM.ArticleAuthorAssignedToIssue

## A stoichiometric organic matter decomposition model in a chemostat culture

Published in Journal of Mathematical Biology, 2017

Recommended citation: Kong, Jude D., Paul Salceanu, and Hao Wang. (2017). "A stoichiometric organic matter decomposition model in a chemostat culture." Journal of Mathematical Biology. 1-36. https://link.springer.com/article/10.1007/s00285-017-1152-3

## Matlab code for parameter estimation and model validation in the paper: Second-generation stoichiometric mathematical model to predict methane emissions from oil sands tailings

Published in URL: http://www.judekong.ca, 2019

Recommended citation: Jude D. Kong, Hao Wang, Tariq Siddique, Julia Foght, Kathleen Semple, Zvonko Burkus, and Mark A. Lewis. (2019). "Second-generation stoichiometric mathematical model to predict methane emissions from oil sands tailings" http://www.judekong.ca/files/Methane_prediction_code.m

## Bayesian Nonparametric Tensor Completion

Published in URL: http://www.judekong.ca, 2019

Recommended citation: Jude D. Kong, Amir Forouzandeh , Alex Lewandowski, Mehran Mahmoudi, and Gian Matharu. (2019). "Bayesian Nonparametric Tensor Completion" http://www.judekong.ca/files/TNBNP.pdf

## Second-generation stoichiometric mathematical model to predict methane emissions from oil sands tailings

Published in Science of the Total Environment, 2019

Recommended citation: Jude D. Kong, Hao Wang, Tariq Siddique, Julia Foght, Kathleen Semple, Zvonko Burkus and Mark Lewis. (2019). "Second-generation stoichiometric mathematical model to predict methane emissions from oil sands tailings." Sci. Total Environ. 1. https://doi.org/10.1016/j.scitotenv.2019.133645 https://www.sciencedirect.com/science/article/pii/S0048969719335715?dgcid=author

## Modeling Microbial Dynamics: Effects on Environmental and Human Health

Published:

In this talk, I will present several nonlinear models for microbial dynamics vis-a-vis human and environ- mental health that I formulated and the results I obtained from analyzing them. Firstly, I will present a stoichiometric organic matter decomposition model in a chemostat culture that incorporates the dynam- ics of grazers. This mechanistic biodegradation model leads to reliable and suggestive ecological insights in the preservation and restoration of our fragile ecosystems. Using the model, I answer the following research questions: (i) What mechanisms allow microbes and resources to persist uniformly or go ex- tinct? (ii) How do grazing and dead microbial residues affect decomposition? Secondly, I will talk about a greenhouse gas biogenesis model which I formulated and fitted to oil sands tailings hydrocarbons data to estimate model parameters. This model can be used to (i) predict the volume of greenhouse gasses emitted at any given time in an oil sands tailing pond and an end pit lake, (ii) calculate the time required to produce a given volume of cumulative greenhouse gases from them and (iii) estimate how long it will take for an oil sands tailing pond and an end pit lake to stop emitting greenhouse gases. Lastly, I will present some directly and indirectly transmitted infectious disease models that I designed. The questions I answered using these models include: (i) Why are there irregularities in seasonal patterns of outbreaks amongst different countries? (ii) How can we estimate the transmission function of an infectious disease from a given incidence or prevalence data set? (iii) How can we control the period and intensity of pathogenic disease outbreaks?

## Teaching assistant (TA)

Undergraduate courses, University of Alberta, Mathematical and Statistical Sciences", 2017

# Maths 201 (Ordinary Differential Equations)

In Winter, Spring & Summer 2014, Winter, Spring & Summer 2015, Winter, Summer & Fall 2014, Winter, Spring & Summer 2014, Winter, Spring & Summer 2016 terms, I TA Maths 201 (Ordinary Differential Equations) Labs, set quizzes for these labs and graded them. Equally graded the weekly assignments for students in this lab.

## Tutor

Undergraduate Courses, Athabasca University, Mathematics, 2017

Ordinary and Partial Differential Equations courses (Math 376 and Math 476) =====

I have been tutoring Ordinary and Partial Differential Equations courses at Athabasca University since Fall 2015. Check the University website for details.

## Teaching Statement

My teaching philosophy, University of Alberta, Mathematical and Statistical Sciences, 2017

My love for teaching dates back to my Primary School days. When I realized that Mathematics was a nightmare for most of my classmates, I decided to study Mathematics on a daily basis, with the aim of understanding more than what was done in class; so as to be the only person that could solve challenging problems given to us by our primary school teacher. Hence, I had the opportunity to go to the board to solve problems for my classmates, which is something I very much cherish. I equally had many pupils bringing gifts for me everyday since they wanted to be my my friends. I used to feel excited whenever I was given the opportunity to solve intricate problems for my classmates. The profoundity of the ineffable feeling of triumph and the urge to remain the best made me interested in teaching and end me the nakename “Prof” right from Primary School .