At the 48th convocation of USJ, two Masters in Applied Statistics degrees were awarded to Mr. Ishara Madushanka and Mr. Mahendran Niroshan. Congratulations to you both!! This is indeed a proud moment for the department.
At the 48th convocation of USJ, the first-ever PhD graduate from the Department, Dr. Manjula Perera was conferred. Her primary supervisor was Dr. Ravindra Lokupitiya of the Department of Statistics, USJ. Her PhD thesis title was “Estimation of global scale carbon fluxes using Maximum Likelihood ensemble filter“. She was co-supervised by the following academics:
- Prof. A. Scott Denning, Professor, Department of Atmospheric Science, Colorado State
University, Fort Collins, CO 80523-1371 USA. - Prof. E. Y. K. Lokupitiya, Professor in Environmental Science, Department of Zoology
and Environment Sciences, University of Colombo, Colombo, Sri Lanka. - Dr. Prabir Kumar Patra, Principal Scientist, Research Institute for Global Change,
JAMSTEC, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, 236-0001, Japan. - Prof. R. G. N. Meegama, Professor in Computer Science, Department of Computer
Science, Faculty of Applied Sciences, University of Sri Jayewardenepura, Gangodawila,
Nugegoda, Sri Lanka.
Dr. Perera would like to thank the following institutes for the grants she received for her PhD studies.
- National Research Council, Sri Lanka (NRC-036)
- Asia-Pacific Network for Global Change Research (APN); grant#ARCP2011-11NMY-
Patra/Canadell).
Her thesis abstract is as follows:
More advanced data assimilation methods based on statistical and mathematical knowledge are needed to cater to the increased amount of CO2 measurements collected in various platforms. In addition to existing flasks, continuous and aircraft data, CO2 measurements obtained by passenger aircrafts and satellites increase the observation network and provide more constraints on surface carbon flux estimation. This thesis mainly focuses on estimating the surface carbon sources and sinks using CONTRAIL aircraft observations, in addition to the existing in-situ measurements using the ensemble based data assimilation method called Maximum Likelihood Ensemble Filter (MLEF) coupled with Parameterized Chemistry Transport model (PCTM). A pseudodata experiment was carried out by adding CONTRAIL measurements to the observation vector using MLEF coupled with PCTM model, which was driven by GEOS-4 (Goddard Earth Observation System, version 4) weather data for the model validation. Next, MLEF code was developed to conduct the real data experiment to identify the capability on estimating surface carbon fluxes for the period 2009-2011. PCTM model was driven by Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) weather data. Solving separate multiplicative biases added for photosynthesis, respiration, and air-sea gas exchange fluxes the estimated fluxes were obtained. Hourly land fluxes, Gross Primary Production (GPP) and respiration obtained from Simple Biosphere-version 3 (SiB3) model, Takahashi ocean fluxes and Brenkert fossil fuel emissions were used.
Pseudodata experiment results showed a considerable uncertainty reduction for Asian region and more than 50% reduction for North American and European regions. According to the results of the real data experiment, North America showed about 60-80% uncertainty reduction while the Asian and European regions showed moderate results with 50-60% uncertainty reduction. Most other land and oceanic regions showed less than 30% uncertainty reduction. The results were mainly compared with the results of well-known CarbonTracker (CT2017) which is a CO2 measurement and modeling system developed by NOAA (National Oceanic and Atmospheric Administration). The spatial distribution of estimated mean annual fluxes over North America, Australia and Tropical Asia showed good agreement with the CarbonTracker results when aggregated into large regions. The biases were poorly constrained in the regions where the CO2 observations are not sufficiently dense such as South America and Africa. Long-term averaged fluxes were compared with several other inversion studies and showed similar results for the Boreal North America, Temperate North America and Australia. The results reveal the capability of MLEF method to assimilate large CO2 observation vectors with high performance parallel computing environment with less cost and less time. The impact of satellite observations with MLEF needs to be investigated further and this study forms the basis of the future work in this area.
Keywords: Data assimilation, Maximum Likelihood Ensemble Filter, CONTRAIL aircraft observations, Carbon sources and sinks, CO2 modeling
A financial aid program organized by the “Statistics Society” in affiliation with the Department of Statistics, Faculty of Applied Sciences, University of Sri Jayewardenepura for first and second-year undergraduates who study statistics as a subject with financial difficulties.
Objectives
- To help undergraduates financially.
- Reduce the number of university dropouts due to financial related problems.
Evaluation Criteria
- Sorting the applicants by thoroughly evaluating the details collected through the google form application.
- Selected applicants will be interviewed by the panel of lecturers (mainly the degree of the financial burden will be considered in the selection).
- Continuous (discipline/attendance/academic performance) monitoring of the 15 selected scholarship holders.
(Identity of the student will not be revealed to the sponsor without consent)
How to apply
If you are a student who requires financial support through the Helping Hands scholarship program, please apply by filling the google form using the following link. You will be asked to upload a signed request letter written by you justifying why you should be selected and a letter from the Grama Niladhari of your GN division confirming your family’s financial status.
Link to the google form: https://forms.gle/SAy9V3uyFSzEPB4D8
Fill the google form on or before 22nd of November 2022.
Implemented by Statistics society in affiliation with the Department of statistics, University of Sri Jayewardenepura.
A three day workshop was jointly conducted by the Department of Statistics and Department of Computer Science on 3rd, 4th and 10th September 2022. The workshop was organized as an outcome of the project “Curriculum Development in Data Science and Artificial Intelligence DS&AI” which is an Erasmus + KA2 Project co-funded by the Erusmus+ program of the European Union.
The course emphasized on machine learning techniques and applications for finding interesting patterns / information from large amount of data. Participants were able to learn to design, implement, and evaluate intelligent systems incorporating models learned from large data. The target audience was the Business leaders, mid to senior managers, data specialists, consultants, and business professionals. The curriculum for the workshop was developed with the partnership of European experts aiming to up skill the professionals of ICT industry in Sri Lanka. Dr. Ravimal Bandara, Department of Computer Science, was the resource person of the workshop and it was coordinated by Dr. Chitraka Wickramarachchi, Department of Statistics.
At the end of the workshop, a certificate of participation was awarded to the participant. Further, those who successfully participated the workshop were eligible to sit for the examination conducted by Leiden University Netherlands, one of our partners from the European Union, and obtain a certificate if passed.
There were 39 participants for the workshop and the overall feedback was positive.