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X-WR-CALDESC:Events for Institute of Statistical Research and Training
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DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20250113T140000
DTEND;TZID=UTC:20250113T170000
DTSTAMP:20260424T051509
CREATED:20250107T065038Z
LAST-MODIFIED:20250107T065038Z
UID:7360-1736776800-1736787600@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminars on Monday\, January 13\, 2025
DESCRIPTION:Seminar 1\nTitle: Opportunities and Challenges in Single-Cell RNA-Seq: Revealing Biomarkers and Regulatory Networks in Early Brain Development \nVenue and time: ISRT\, 2:00 pm \nSpeaker: Dr. Md. Alamin\, Assistant Professor\, Department of Mathematics & Physics\, School of Engineering and Physical Sciences\, North South University \nAbstract: \nUnraveling the molecular mechanisms of early neuronal development is critical to understanding the genetic and regulatory factors driving human brain formation. Leveraging single-cell RNA sequencing (scRNA-seq)\, we profiled the transcriptional dynamics during the differentiation of human embryonic stem cells (hESCs) into neurons at two key time points: Day 26 (D26) and Day 54 (D54). Our analysis uncovered 539 differentially expressed genes (DEGs)\, revealing that up-regulated DEGs are involved in neurogenesis\, while down-regulated DEGs play roles in synapse regulation. Reactome pathway analysis highlighted significant contributions of down-regulated DEGs to synaptic protein interactions. Furthermore\, we identified 20 critical transcription factors and explored miRNA-DEG and TF-miRNA interactions\, advancing our understanding of gene regulatory networks during early brain development. These findings offer valuable insights into the genetic underpinnings of intelligence\, mental health\, and neurodevelopmental disorders. Moreover\, I will address the emerging challenges for statisticians and mathematicians in analyzing and interpreting high-dimensional single-cell data. These include handling data sparsity\, developing robust computational models\, and integrating multimodal datasets to uncover complex biological interactions. By bridging the gaps between biology\, statistics\, and mathematics\, we can push the boundaries of our understanding and foster innovation in neuroscience research. \nSeminar 2\nTitle: A reflection on the recent development of the subject area of Statistics and some specific issues of concern for further research \nVenue and time: ISRT\, 3:00 pm \nSpeaker: Dr. Moudud Alam\, Associate Professor in Microdata Analysis\, Dalarna University\, Sweden \nAbstract: \nThis talk is divided into two parts. In first part the recent development of the subject area of Statistics\, particularly the development of Data Science\, is discussed from the speaker’s experience along the way to develop a master’s and a PhD programme in Data Science\, and Data Analytics at a Swedish university. The media outcry and the popularity of the computing technology\, and artificial intelligence has pushed the Statistics community to rethink about its longstanding branding. Dedication of the 2024 Nobel prize in Physics and partly in Chemistry\, to the contribution in the development of artificial neural network\, and artificial intelligence can be considered as yet another dictation from the scientific community of the future direction of the filed. The contemporary labour market demands of the computing and soft skills is a non-negligible factor influencing the current trend of the subject. In this talk the experience of the speaker’s journey from Statistics to Data Science is discussed\, in connection with the related Swedish and European initiatives. Yet\, in this impassionate outrage it seems the scientific community is undermining (if not missing) the core assignment\, as all researchers concentrate too much to the practical applications driven by the contemporary problems\, mainly coming from the industry. In the second part\, the speaker draws attention\, using literature review and own research towards a number of statistical core issues that need special attention. In particular\, the limitation of ad hoc (such as cross validation) inferential procedure is exemplified using variable selection problem as an example\, from the literature. Using the speaker’s own research on service lifetime estimation of traffic signs in Sweden the speaker highlights the need for core statistical skills in dealing with unconventional data sources in the Data Science era. \n  \n 
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminars-on-monday-january-13-2025/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241223T140000
DTEND;TZID=UTC:20241223T150000
DTSTAMP:20260424T051509
CREATED:20241222T010115Z
LAST-MODIFIED:20241222T011925Z
UID:7338-1734962400-1734966000@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday\, December 23\, 2024
DESCRIPTION:Title: Postpartum family planning counselling during maternity care visits in Bangladesh and its effect on contraceptive initiation \nVenue and time: ISRT\, 2:00 pm \nSpeaker: Md. Moinuddin Hiader\, Associate Scientist\, icddr\,b \nThe speaker will initiate the talk by briefly discussing what he and his team expect from a fresh graduate as employers and what helps in career growth in public health research. He will conclude the talk by discussing 2-3 research ideas using publicly available data.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-december-23-2024/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241111T140000
DTEND;TZID=UTC:20241111T150000
DTSTAMP:20260424T051509
CREATED:20241104T043223Z
LAST-MODIFIED:20241104T043223Z
UID:7206-1731333600-1731337200@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday\, November 11\, 2024
DESCRIPTION:Title: Engineering Energy Efficiency among Residential Customers in Dhaka using Home Energy Reports (HERs) \nVenue and time: ISRT\, 2:00 pm \nSpeaker: Atonu Rabbani\, Ph.D\, Professor\, Department of Economics\, University of Dhaka \nAbstract: \nIn this study\, we estimated the potential impacts of home energy reports (HERs) on energy efficient behaviours among residential customers of Dhaka\, the capital of Bangladesh. We partnered with one of the two major retail power distribution companies. Using administrative consumption data\, we developed HERs with social feedback or descriptive norms\, where customers received comparisons between their own consumption and the averages of their neighbours. Using a randomized control trial\, we compared the electric energy consumption of a group who received “placebo” reports based only on their own consumption and without any comparison groups. Our findings suggest that the energy consumption declined by about 5 percent. However\, these impacts were short-lived and there was also suggestive evidence of possible rebound effects. Consistent with prior findings\, we also found that the impacts were larger for consumers who had higher consumption based on pre-intervention energy consumption. The point estimates suggest that households who received feedbacks based on their expenditure exhibited a larger impact compared to the households who received quantity-based feedbacks. The preliminary analyses suggest that behavioural nudges can be effective in the short run and that more intense and continuous feedback may be necessary for longer-term effects.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-november-11-2024/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240918T100000
DTEND;TZID=UTC:20240918T113000
DTSTAMP:20260424T051509
CREATED:20240915T033016Z
LAST-MODIFIED:20240916T045507Z
UID:7018-1726653600-1726659000@isrt.ac.bd
SUMMARY:A talk by Awan Afiaz on 18 September 2024
DESCRIPTION:Title: Optimal Sandwich Variance Estimator in Penalized GEE for Nearly Separated Longitudinal Binary Data with Small Samples \nVenue and time: ISRT\, 10:00 am \nSpeaker: Awan Afiaz\, PhD candidate at the Department of Biostatistics\, University of Washington Seattle\, WA\, USA and ISRT alumnus \nAbstract: \nData separation arises in both independent and correlated binary data in biomedical studies and poses a substantial challenge that can lead to unreliable estimates and misleading inferences. This problem can occur due to a small sample size\, a rare exposure or event\, a very strong predictor or a linear combination of predictors\, high within-subject correlation (ICC)\, or any combination of these issues. Penalized generalized estimating equations (GEE) have been shown to be the superior approach for handling separation in binary longitudinal data\, along with bias-corrected sandwich variance estimators. Although the sandwich variance estimator is valid under misspecification of the working correlation structure in GEE\, it is downward biased by design for small samples and requires large samples for the asymptotic advantages to take effect. This has led to the development of several modified robust variance estimators for GEE for small samples\, which motivates finding the optimal sandwich estimator in the context of penalized GEE when there is near separation (sparsity) in the data. The current study proposed a bias-corrected sandwich variance estimator for penalized GEE and compared its performance with ten extant sandwich estimators for nearly separated data using a simulation study. To motivate the need for an optimal sandwich estimator in penalized GEE\, we demonstrated that the existing small-sample based estimators provided contradictory results when using dermatophyte-toe onychomycosis trial data. The proposed sandwich estimator does not require any additional assumptions beyond those already employed by the original sandwich estimator for GEE. We evaluated the proposed sandwich estimator by assessing the ratio of the average SEs and the empirical SD and by calculating the type-I error rates for Wald tests of the regression coefficients. Our simulation studies showed that the proposed estimator yielded nominal-level type-I error rates based on Wald tests of regression coefficients\, regardless of whether the working correlation model was correctly specified. Furthermore\, while existing approaches performed well when the number of subjects was high\, the proposed estimator achieved nominal type-I error rates with sample sizes as low as 10\, even in the most extreme scenarios. Even though all existing sandwich estimators performed better as the number of subjects increased\, exhibiting the usual asymptotic behavior of sandwich estimators\, no other estimator uniformly achieved optimal performance faster (with respect to the number of subjects and ICC) than our proposed estimator
URL:https://isrt.ac.bd/event/a-talk-by-awan-afiaz-on-18-september-2024/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240205T140000
DTEND;TZID=UTC:20240205T153000
DTSTAMP:20260424T051509
CREATED:20240204T022109Z
LAST-MODIFIED:20240204T022109Z
UID:6480-1707141600-1707147000@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday\, February 5\, 2024
DESCRIPTION:Two talks (20 minutes each) \nVenue and time: ISRT\, 2:00 pm \nTalk 1 \nTopic : Data Tracker Table \nSpeaker: Nur Mohammad\, 2nd year student\, ISRT \n  \nTalk 2 \nTopic : Introduction to Typst: a modern typesetting system \nSpeaker: Md. Aminul Islam Shazid\, MS student\, ISRT
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-february-5-2024/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240201T140000
DTEND;TZID=UTC:20240201T153000
DTSTAMP:20260424T051509
CREATED:20240129T030850Z
LAST-MODIFIED:20240129T031353Z
UID:6427-1706796000-1706801400@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Thursday\, February 1\, 2024
DESCRIPTION:Title: A Introduction to Power BI – Data visualization \nVenue and time: ISRT\, 2:00 pm \nSpeaker: Tawfique Abdur Razzaque\, Senior Data Analyst\, Business Intelligence\, Therap (BD) Ltd. \n  \nWhat is Power BI? \nMicrosoft Power BI is an interactive data visualization software product developed by Microsoft with a primary focus on business intelligence. It is part of the Microsoft Power Platform. Power BI is a collection of software services\, apps\, and connectors that work together to turn various sources of data into static and interactive data visualizations. [Wikipedia] \n.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-thursday-february-1-2024/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240122T140000
DTEND;TZID=UTC:20240122T153000
DTSTAMP:20260424T051509
CREATED:20240118T092012Z
LAST-MODIFIED:20240118T092012Z
UID:6415-1705932000-1705937400@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Thursday\, January 22\, 2024
DESCRIPTION:Title: Impact of Covid-19 on im(mobility) of informal sector workers in Dhaka\, Bangladesh \nVenue and time: ISRT\, 2:00 pm \nSpeaker: Md. Touhidul Alam\, PhD student at Lancaster Environment Centre\, Lancaster University\, UK \nAbstract: \nComparable to many fast-growing cities of the developing world\, Dhaka city contributes greatly to the national economy (36% of GDP and 32% of national employment)\, with much of the employments being in the informal sectors. During COVID-19 pandemic\, the poorest section of this informal workforce with little or no savings became the worst victim of economic turmoil and lockdown. To escape starvation and annihilation\, many had but to return to the very villages that they had left to join Dhaka’s ever-expanding informal workforce. Curiously\, following lifting of the COVID-19 restrictions\, many of the returnees did not come back to the city immediately. This trend has the potential to reduce the supply of informal workforce to the city\, although their absorption in the rural economy has many positives for the rural development – like the availability of new skills\, ideas and technology. Taken collectively\, COVID-19 might have opened-up new possibilities for poorer people to secure higher wellbeing for themselves\, as well as to unleash a new way of doing development. While studies focusing on loss of jobs\, food insecurity and coping mechanisms of informal workers in Bangladesh are emerging\, documentation of the (im)mobility of informal workers is required for better policymaking and advancing scientific knowledge.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-thursday-january-22-2024/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240111T140000
DTEND;TZID=UTC:20240111T153000
DTSTAMP:20260424T051509
CREATED:20240110T044931Z
LAST-MODIFIED:20240110T080942Z
UID:6407-1704981600-1704987000@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Thursday\, January 11\, 2024
DESCRIPTION:Title: Estimating Causal Effects of Socio-economic Factors on the Prevalence of Cesarean Section Delivery in Bangladesh \nVenue and time: ISRT\, 1:45 pm \nSpeaker: Dr  A. H. M. Mahbub Latif\, Professor\, ISRT\, DU \nAbstract: TBA \n 
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-thursday-january-11-2024/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240104T140000
DTEND;TZID=UTC:20240104T153000
DTSTAMP:20260424T051509
CREATED:20240102T094327Z
LAST-MODIFIED:20240102T094327Z
UID:6392-1704376800-1704382200@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Thursday\, January 4\, 2024
DESCRIPTION:Title: PARD: Patient-Specific Abnormal Region Detection in Alzheimer’s Disease Studies \nVenue and time: ISRT\, 2:00 pm \nSpeaker: Avizit Adhikary\, PhD candidate\, Florida State University \nAbstract: \nAlzheimer’s disease (AD) is the primary cause of dementia\, leading to cognitive challenges in processing new information\, handling complex tasks\, and experiencing personality fluctuations. To better understand and treat AD\, extensive research is needed to detect abnormal brain regions in an AD patient that can facilitate providing targeted medicine and improve the treatment pathways. However\, these regions may vary among the subjects due to the heterogeneity arising from demographic factors such as age and gender. Furthermore\, brain cells within a subject have inherent spatial dependence among themselves\, and a diseased cell may affect its neighboring cells to an unknown extent. In addition\, unmeasured confounders and measurement errors can partially or entirely mask the abnormal regions. All these points make these diseased regions challenging to detect. To this end\, we propose a Patient-specific Abnormal Region Detection (PARD) algorithm to identify the heterogeneous diseased regions by solving a Bayesian latent-space variable selection problem. Using Bayesian hierarchical modeling\, we account for the heterogeneity among the subjects as a large-scale variability and incorporate the inherent spatial dependence within subjects using ising priors into the latent space. A Gibbs sampling framework is derived for efficiently estimating the model parameters and hyper-parameters. The simulation study shows the superiority of the proposed algorithm over popular unsupervised learning methods. The algorithm is further applied to the resting-state MRI brain scans of subjects collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI)\, and the detected regions are validated and analyzed by cross-matching with the brain’s default mode network (DMN).
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-thursday-january-4-2024/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20231106T140000
DTEND;TZID=UTC:20231106T153000
DTSTAMP:20260424T051509
CREATED:20231105T053544Z
LAST-MODIFIED:20231105T054143Z
UID:6243-1699279200-1699284600@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday\, November 6\, 2023
DESCRIPTION:Title: On the Development and Validation of Risk Prediction Models for Rare Outcomes \nVenue and time: ISRT\, 2:00 pm \nPresenter:  Dr. Md. Shafiqur Rahman\, Professor of Applied Statistics\, ISRT \nAbstract: \nRisk prediction models are commonly developed in clinical research to predict patients’ future health outcomes such as death or state of illness due to disease and/or to classify patients into clinical risk groups (low\, medium\, and high). Predictions from these models are useful to make joint decisions with both patient and clinician for future courses of treatment. However\, clinicians will be reluctant to use these models unless they can trust their predictions. To maximize the prediction accuracy and clinical utility of these models\, it is essential to ensure that they are rigorously developed\, validated\, and evaluated. However\, the standard process of model development and validation faces serious problems when the outcome is rare. This talk discusses the methodological challenges and possible solutions of model development and validation for data with rare outcomes. Issues are discussed providing separate examples of predictive models for binary and survival data with rare outcomes and illustrating them using both simulated and practical data.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-november-6-2023/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20231009T140000
DTEND;TZID=UTC:20231009T153000
DTSTAMP:20260424T051509
CREATED:20231006T172907Z
LAST-MODIFIED:20231006T173606Z
UID:6089-1696860000-1696865400@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday\, October 9\, 2023
DESCRIPTION:Title: Joint copula-frailty approach to model clustered survival data with dependent censoring (PhD proposal) \nVenue and time: ISRT\, 2:00 pm \nPresenter:  Nasrin Sultana\, PhD student at ISRT \nAbstract: \nWith the development of modern medical technology\, in many situations naturally a fraction of patients can be cured\, or the disease free\, while the non-cured patients lengthen their survival time. Recurrence of a disease or event is a very common phenomenon in biomedical studies. The uncured patients experience the recurrence of the disease because of some unobserved random effects or heterogeneity. Dependent censoring may arise in some situations when the right censoring is completely or partly caused by an event other than the terminal event of interest\, possibly due to these random effects. We aim at developing both a joint frailty model and a joint frailty copula model for such recurrent event lifetime data considering cure fraction that captures the heterogeneity due to dependent censoring. To achieve dependent censoring\, we also need to add joint modelling of recurrent time and time to death where time to death determines the censoring mechanism. A likelihood-based technique has been developed for the proposed models. The Expectation-Maximization (EM) and Monte Carlo Expectation-Maximization (MCEM) algorithms are being developed to carry out the underlying parameter estimation and inference procedures.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-october-9-2023/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20231008T150000
DTEND;TZID=UTC:20231008T163000
DTSTAMP:20260424T051510
CREATED:20231006T175159Z
LAST-MODIFIED:20231006T175159Z
UID:6091-1696777200-1696782600@isrt.ac.bd
SUMMARY:A talk by Bangladesh Data Center Company Limited on Sunday\, October 8\, 2023
DESCRIPTION:Representatives from Bangladesh Data Center Company Limited (BDCCL)\, a tier IV national data centre\, will be giving a talk this coming Sunday\, October 8\, 2023 at 3 pm on the following topic:\n\n“Empowering Future Leaders of Data-driven Ecosystem through Innovation”\n\n\nThis talk is a part of ISRT’s initiative to organize career development programs for students and to link academia with industry. The talk focuses on the future directions\, scopes and challenges of data science in Bangladesh.\n  \n 
URL:https://isrt.ac.bd/event/a-talk-by-bangladesh-data-center-company-limited-on-sunday-october-8-2023/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230918T140000
DTEND;TZID=UTC:20230918T153000
DTSTAMP:20260424T051510
CREATED:20230914T064104Z
LAST-MODIFIED:20230914T064619Z
UID:6029-1695045600-1695051000@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday\, September 18\, 2023
DESCRIPTION:Title: The Generalized Variable Importance Metric: A model agnostic method to identify predictor outcome relationship \nPresenter:  \nKaviul Anam khan \nPhD  in Biostatistics candidate at the Dalla Lana School of Public Health\, University of Toronto \nAssistant Professor\, Department of Statistical Sciences\, University of Toronto \n  \nAbstract: \nThe aim my research is to define importance of predictors for black box machine learning methods\, where the prediction function can be highly non-additive and cannot be represented by statistical parameters. In this paper we defined a “Generalized Variable Importance Metric (GVIM)” using the true conditional expectation function for a continuous or a binary response variable. We further showed that the defined GVIM can be represented as a function of the Conditional Average Treatment Effect (CATE) squared for multinomial and continuous predictors. Then we propose how the metric can be estimated using any machine learning models. Finally we showed the properties of the estimator using multiple simulations. While the estimators for the GVIM are consistent\, they have small sample biases. We proposed and efficient influence function based approach under some regularity conditions to perform one step correction of the bias. This research is going to significantly impact the public and clinical health sciences\, since this opens the door for effectively using modern machine learning methods in real life applications in health sciences.
URL:https://isrt.ac.bd/event/6029/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230821T140000
DTEND;TZID=UTC:20230821T153000
DTSTAMP:20260424T051510
CREATED:20230819T013713Z
LAST-MODIFIED:20230819T013713Z
UID:5978-1692626400-1692631800@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday\, August 21\, 2023
DESCRIPTION:Title: Hierarchical structural component models for pathway analysis of longitudinal categorical phenotypes \nPresenter:  \nMd. Kamruzzaman\, PhD \nAssociate Professor\, Jagannath University\, Email: kzaman1@isrt.ac.bd \n  \nAbstract: \nSeveral statistical methods for pathway analysis have been developed to test the association between pathways and phenotypes of interest. Since pathways are highly correlated\, a hierarchical structural component models (HisCoM) was developed to analyze all pathways in a single model and take into consideration their correlation. HisCoM was originally developed to analyze a single phenotype using only one measurement per individual. Later\, it was extended to analyze multiple phenotypes (HisCoM-multi) and longitudinal phenotypes (HisCoM-GEE). These methods have been used to analyze continuous\, counts\, and binary phenotypes from cross-sectional\, clustered\, and longitudinal studies. In this study\, we propose a hierarchical structural component model for pathway analysis of longitudinal categorical phenotypes (HisCoM-RCateg). HisCoM-RCateg is proposed by combining the hierarchical structural component model and generalized estimating equations for correlated categorical phenotypes. HisCoM-RCateg accounts for the biological hierarchy of all biomarkers and pathways into a single model. In the simulation\, the proposed HisCoM-RCateg appeared to have high power than other existing methods and effectively controlled type I error for longitudinal multinomial phenotypes. To demonstrate the performance\, we also applied HiscoM-RCateg to two distinct types of longitudinal omics data\, namely the metabolite dataset and the metagenome dataset.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-august-21-2023/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230807T140000
DTEND;TZID=UTC:20230807T150000
DTSTAMP:20260424T051510
CREATED:20230726T145600Z
LAST-MODIFIED:20230728T120224Z
UID:5922-1691416800-1691420400@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on August 7 on "Testing linearity in rapid microbiological method validation"
DESCRIPTION:Title: Testing linearity in rapid microbiological method validation \nAbstract: \nTesting the linearity of a measurement system (MS) is required during its validation. Guidelines clearly discuss the appropriate design and analysis in testing the linearity of an MS when the data is Gaussian distributed but not for count data. This talk addresses how linearity is tested and the corresponding optimal design for testing linearity for Poisson data. \n  \nPresenter: \nDr. Abu Manju\nAssociate Director Statistics\nCenter for Mathematical Science (CMS)\,\nOrganon\, Oss\, the Netherlands \nAssistant Professor \nWittenborg University of Applied Science\, Appeldorn\, the Netherlands
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-august-7-on-testing-linearity-in-rapid-microbiological-method-validation/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230724T140000
DTEND;TZID=UTC:20230724T150000
DTSTAMP:20260424T051510
CREATED:20230718T040215Z
LAST-MODIFIED:20230718T112121Z
UID:5867-1690207200-1690210800@isrt.ac.bd
SUMMARY:Seminar on “Data Science Product Development In the (AWS) Cloud” at 2 pm on July 24\, 2023
DESCRIPTION:Speaker: Sheikh Samsuzzhan Alam\, Senior Data Science Developer for Operation\, Novartis Pharma\, Czech Republic \nVenue: ISRT seminar room \nDate and time: 2 pm on Monday\, 24 July 2023 \nTitle: Data Science Product Development In the (AWS) Cloud \nAbstract: Customer-facing software products are complex in nature and usually developed by multiple teams of engineers. On the other hand\, software products build for internal business teams such as finance\, marketing\, human resources\, etc. are domain-specific and small user group focused. Providing data science solutions as software services for internal business teams with full-stack developer teams can have high development costs. Businesses might fall back or hesitate to build such products due to high maintenance and developer cost. Hence in My presentation\, I would like to showcase how a Data Scientist can wear many hats and provide a full-stack data science solution\, which is easily maintainable\, reusable\, and cost-effective at least from the development point of view due to its Cloud Native nature. In my presentation\, I would like to demonstrate a real-world use case I have implemented using AWS services such as Sagemaker\, Lambda\, API gateway\, S3\, and DynamoDB with Python SDKs to develop a Natural Language Processing (NLP) application for internal business use.
URL:https://isrt.ac.bd/event/seminar-on-data-science-product-development-in-the-aws-cloud-at-2-pm-on-july-24-2023/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230619T110000
DTEND;TZID=UTC:20230619T120000
DTSTAMP:20260424T051510
CREATED:20230618T065328Z
LAST-MODIFIED:20230618T065328Z
UID:5779-1687172400-1687176000@isrt.ac.bd
SUMMARY:Applied Statistics Seminar on "Pairwise Accelerated Failure Time Models for Infectious Disease Transmission Within and Between Households"
DESCRIPTION:Abstract: Pairwise survival analysis handles dependent happenings in infectious disease transmission data by analyzing failure times in ordered pairs of individuals. The contact interval in the pair ij is the time from the onset of infectiousness in i to infectious contact from i to j\, where an infectious contact is sufficient to infect j if he or she is susceptible. The contact interval distribution determines transmission probabilities and the infectiousness profile of infected individuals. Many important questions in infectious disease epidemiology involve the effects of covariates (e.g.\, age or vaccination status) on transmission. Here\, we generalize earlier pairwise methods in two ways: First\, we introduce an accelerated failure time model that allows the contact interval rate parameter to depend on infectiousness covariates for i\, susceptibility covariates for j\, and pairwise covariates. Second\, we show how internal infections (caused by individuals under observation) and external infections (caused environmental or community sources) can be handled simultaneously. In simulations\, we show that these methods produce valid point and interval estimates and that accounting for external infections is critical to consistent estimation. Finally\, we use these methods to analyze household surveillance data from Los Angeles County during the 2009 influenza A(H1N1) pandemic.\n\n\nSpeaker: Yushuf Sharker\, Ph.D.\, Takeda Pharmaceuticals\, USA
URL:https://isrt.ac.bd/event/applied-statistics-seminar-on-pairwise-accelerated-failure-time-models-for-infectious-disease-transmission-within-and-between-households/
LOCATION:isrt seminar room
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230612T120000
DTEND;TZID=UTC:20230612T130000
DTSTAMP:20260424T051510
CREATED:20230607T174356Z
LAST-MODIFIED:20230612T052057Z
UID:5733-1686571200-1686574800@isrt.ac.bd
SUMMARY:Seminar on Classification and Clustering for RNA-seq data with variable selection 
DESCRIPTION:Speaker: Tanbin Rahman PhD\, FDA\, USA\n\nTitle: Classification and Clustering for RNA-seq data with variable selection\n\nAbstract: Clustering and classification play an important role in identifying sub-types of complex diseases as well as building a predictive model in the field of medicine. In recent years\, lowering of cost and high accuracy has made RNA-seq widely popular which is expected to continue to grow over the next few years. One of the important features of RNA-seq data is its count data structure. While there has been a great deal of literature in both clustering and classification methods\, most of them are either heuristic or suitable for continuous data and do not directly generalize to count data.\n\nIn the first part of the presentation\, we develop a negative binomial mixture model with lasso or fused lasso gene regularization to cluster samples (small n) with high-dimensional gene features (large p). A modified EM algorithm and Bayesian information criterion are used for inference and determining tuning parameters. The method is compared with existing methods using extensive simulations and two real transcriptomic applications in rat brain and breast cancer studies. The result shows the superior performance of the proposed count data model in clustering accuracy\, feature selection\, and biological interpretation in pathways. \nIn the second part of this presentation\, we will discuss a classification model based on negative binomial distribution via generalized linear model framework with double regularization for gene and covariate sparsity to accommodate three key elements: adequate modeling of count data with overdispersion\, gene selection and adjustment for covariate effect. The proposed method is evaluated in simulations and two real applications using cervical tumor miRNA-seq data and schizophrenia post-mortem brain tissue RNA-seq data to demonstrate its superior performance in prediction accuracy and feature selection.
URL:https://isrt.ac.bd/event/classification-and-clustering-for-rna-seq-data-with-variable-selection/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230227T133000
DTEND;TZID=UTC:20230227T143000
DTSTAMP:20260424T051510
CREATED:20230225T183508Z
LAST-MODIFIED:20230225T183508Z
UID:5608-1677504600-1677508200@isrt.ac.bd
SUMMARY:Special Applied Statistics Seminar on "Health Statistics in Bangladesh"
DESCRIPTION:Prof. Dr. Syed Abdul Hamid (https://ihe.ac.bd/faculty/syedabdulhamid)\, Health Institute\, Dhaka University\, will give a talk on “Health Statistics in Bangladesh” at ISRT on February 27\, 2023\, from 1.30-2.30 pm. He is an expert on Health Statistics. This talk is being arranged on the eve of National Statistics Day to be celebrated country-wide on February 27\, 2023.
URL:https://isrt.ac.bd/event/special-applied-statistics-seminar-on-health-statistics-in-bangladesh/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230207T120000
DTEND;TZID=UTC:20230207T130000
DTSTAMP:20260424T051510
CREATED:20230203T150054Z
LAST-MODIFIED:20230205T084015Z
UID:5593-1675771200-1675774800@isrt.ac.bd
SUMMARY:Applied Statistics Seminar on Tuesday (February 7\, 2023) at 12 PM
DESCRIPTION:Abstract:\nThe exchangeability of units between treatment groups is a key and typically untestable assumption for evaluating causal intervention effects in observational studies. Standard methods assuming exchangeability can yield biased treatment effect estimates if the assumption does not hold. Existing methods evaluate the sensitivity of treatment effect estimates to non-exchangeability due to unmeasured confounders only. In practice\, non-exchangeability can occur for either unmeasured confounders or reverse causality. We propose an index of sensitivity to non-exchangeability (ISENSE) to measure the impact of non-exchangeability on treatment effect estimates. Unlike existing methods\, ISENSE does not require imposing assumptions on the types\, numbers\, and distributions of unmeasured confounders\, and it can handle both unmeasured confounders and reverse causality. ISENSE is a computationally inexpensive local sensitivity method based on a Taylor-series approximation to the non-exchangeability likelihood\, evaluated at the parameter estimates under the exchangeability assumption. One can interpret ISENSE intuitively through the unit-free “MinNE” statistic values that capture the minimum non-exchangeability needed to cause important sensitivity. We evaluate ISENSE using simulation studies and illustrate its use with an example using administrative data from British Columbia\, Canada.\n\n\nPresenter:\n\n\nMd Rashedul Hoque\nPh.D. Candidate\, SFU\nMethodologist\, Statistics Canada\nTrainee Biostatistician\, Arthritis Research Canada\nMob: +1 778 882 0689
URL:https://isrt.ac.bd/event/applied-statistics-seminar-on-tuesday-february-7-2023-at-2pm/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230110T140000
DTEND;TZID=UTC:20230110T150000
DTSTAMP:20260424T051510
CREATED:20230110T072719Z
LAST-MODIFIED:20230110T072719Z
UID:5551-1673359200-1673362800@isrt.ac.bd
SUMMARY:Applied Statistics Seminar on Tuesday\, 10 January 2023 at 2 PM.
DESCRIPTION:The speaker will be Argho Sarkar\, a Ph.D. candidate at the University of Maryland\, USA. He will give a talk on “Deep Learning for Climate Change: Challenges\, Progress\, and Possibilities.”  Besides presenting his research\, Argho will also talk about his experiences as a graduate student at the University of Maryland.\n\nThe seminar will take place in the ISRT Seminar Room at 2 PM.
URL:https://isrt.ac.bd/event/applied-statistics-seminar-on-tuesday-10-january-2023-at-2-pm/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20221220T140000
DTEND;TZID=UTC:20221220T153000
DTSTAMP:20260424T051510
CREATED:20221211T031929Z
LAST-MODIFIED:20221211T031929Z
UID:5487-1671544800-1671550200@isrt.ac.bd
SUMMARY:Seminar on "Anomaly Detection in Temporal Networks through Topological Features and Motifs with Application to Mobility Data" at 2 pm on December 20\, 2022
DESCRIPTION:Speaker: Dr. Asim Kumer Dey\, Brac University \nVenue: ISRT seminar room \nDate and time: 2 pm on Tuesday\, 20 December 2022 \nTitle: Anomaly Detection in Temporal Networks through Topological Features and Motifs with Application to Mobility Data \nAbstract: This paper aims to shed light on the potential relations among various higher-order network topological features and meso-level functionality of temporal networks. In particular\, we evaluate higher-order topological features\, e.g.\, motif\, betti number\, and persistence images\, to characterize the dynamics of a temporal network. We then use the functional data depth techniques on the resultant betti functions and vectorized persistent images to identify abnormal behavior in the temporal network. We apply the proposed methods to the temporal human mobility networks. Experiments on both synthetic temporal networks and human mobility networks demonstrate the effectiveness of the proposed methods. \n 
URL:https://isrt.ac.bd/event/seminar-on-anomaly-detection-in-temporal-networks-through-topological-features-and-motifs-with-application-to-mobility-data-at-2-pm-on-december-20-2022/
LOCATION:ISRT Seminar Room (3rd floor)\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20221206T140000
DTEND;TZID=UTC:20221206T150000
DTSTAMP:20260424T051510
CREATED:20221126T152410Z
LAST-MODIFIED:20221126T152410Z
UID:5467-1670335200-1670338800@isrt.ac.bd
SUMMARY:Seminar on Subgroup Analysis with Differential Treatment Effects and Biomarker Identification at 2 pm on December 6\, 2022
DESCRIPTION:Title: Subgroup Analysis with Differential Treatment Effects and Biomarker Identification \nAbstract: \nIn the process of drug development and regulatory decision making\, it is important to characterize heterogeneity of subject response to treatment. The heterogenous effect is typically assessed based on variety of information on clinical\, gene and protein expression markers\, commonly known as biomarkers. Previous studies have shown that mRNA expression patterns of tumor samples can predict clinical outcome of cancer patients. Altered mRNA expression profiles from tumor samples can serve as the\nmolecular basis of the cancer patients and hence can be used as the molecular signatures for subgrouping of patients with different survival. The primary objective of this research is two-fold: i) to integrate multi-omic data such as mRNA expression profiles and the copy number variations in order to identify mutated driver genes\, and ii) to use a significant set of driver mutated latent gene structures to identify breast cancer patient subgroups with distinguishable clinical outcomes. As an example\, differential treatment effects of cardio-respiratory fitness (CRF) are assessed on survival experience of a group of individuals from an observational study. Similar approach will be considered to identify subgroups of breast cancer patients with different clinical outcomes. \n  \nSpeaker: \nDr. Munni Begum\nPROFESSOR OF MATHEMATICAL SCIENCES AND DIRECTOR OF THE DATA SCIENCE AND ANALYTICS PROGRAMS\nBall State University\nhttps://www.bsu.edu/academics/collegesanddepartments/math/about/facultyandstaff/faculty/begummunni
URL:https://isrt.ac.bd/event/seminar-on-subgroup-analysis-with-differential-treatment-effects-and-biomarker-identification-at-2-pm-on-december-6-2022/
LOCATION:ISRT Seminar Room (3rd floor)\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20221101T140000
DTEND;TZID=UTC:20221101T150000
DTSTAMP:20260424T051510
CREATED:20221031T174157Z
LAST-MODIFIED:20221031T174157Z
UID:5456-1667311200-1667314800@isrt.ac.bd
SUMMARY:ISRT Applied Statistics Seminar on November 1 (Tuesday) at 2:00PM on "Foundational Learning Skills of Children in Bangladesh: What We Learned from MICS"
DESCRIPTION:Dr. Mohaimen Mansur will give a talk on “Foundational Learning Skills of Children in Bangladesh: What We Learned from MICS”.  Dr. Mansur is an Associate Professor at the Institute of Statistical Research and Training (ISRT)\, University of Dhaka.
URL:https://isrt.ac.bd/event/isrt-applied-statistics-seminar-on-november-1-tuesday-at-200pm-on-foundational-learning-skills-of-children-in-bangladesh-what-we-learned-from-mics/
LOCATION:ISRT Seminar Room (3rd floor)\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20221025T140000
DTEND;TZID=UTC:20221025T150000
DTSTAMP:20260424T051510
CREATED:20221023T155035Z
LAST-MODIFIED:20221023T155035Z
UID:5433-1666706400-1666710000@isrt.ac.bd
SUMMARY:
DESCRIPTION:An “ISRT Applied Statistics Seminar” on October 25 (Tuesday) at 2:00 PM (Venue: ISRT Seminar Room). Mrs. Tasnim Ara will talk on “Explaining geo-spatial variation in mobile phone ownership among rural women of Bangladesh: A multi-level and multidimensional approach”. Mrs. Tasnim Ara is a lecturer at the Institute of Statistical Research and Training (ISRT)\, University of Dhaka.
URL:https://isrt.ac.bd/event/5433/
LOCATION:ISRT Seminar Room (3rd floor)\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220920T140000
DTEND;TZID=UTC:20220920T150000
DTSTAMP:20260424T051510
CREATED:20220918T091217Z
LAST-MODIFIED:20220918T091217Z
UID:5392-1663682400-1663686000@isrt.ac.bd
SUMMARY:Seminar on "The Impact of Food Insecurity on Health Status\, Suicidal Ideation and Quality of Life Among Adults Living in Poverty: A Study in Urban Slums of Bangladesh"
DESCRIPTION:Title:  The Impact of Food Insecurity on Health Status\, Suicidal Ideation and Quality of Life Among Adults Living in Poverty: A Study in Urban Slums of Bangladesh \n  \nAbstract: \nFood insecurity is an ongoing public health issue in developing countries\, including Bangladesh\, exacerbated by the ongoing COVID-19 pandemic. It has considerable health impacts on the physical\, social\, and psychological status of individuals in communities suffering from food insecurity. This study aimed to determine factors associated with household food insecurity\, physical illness\, suicidal ideation\, and quality of life. A cross-sectional study was conducted among 698 adults in the Mirpur and Tongi slum region in Bangladesh using a semi-structured questionnaire. The analysis found that the prevalence of food insecurity was high among adults who are 40 years old and in households with larger family sizes; individuals without education were at higher risk of food insecurity; day laborers and transport drivers were at the highest risk of food; property owner was at lower risk of food insecurity compared to who does not have any types of property. The prevalence of food insecurity was also high among people who were suffering from different kinds of physical illness compared to healthy people. Divorced or widowed individuals were more likely to be food insecure than single individuals. Food insecurity was also associated with quality of life negatively. Lower socio-economic status\, poor quality of life\, and pandemic-induced work loss affected household food insecurity. It is suggested that any interventions with financial aid and complemented food distributions\, particularly among the wage looser\, may improve food insecurity. \n  \nSpeaker:  \nMd. Hasinur Rahaman Khan \nProfessor\, ISRT\, University of Dhaka \n  \nTime and. Date:                    September 20\, 2.00:3.00 PM \n  \nSponsor:                                 Centennial Research Grant (CRG)\, University of Dhaka
URL:https://isrt.ac.bd/event/seminar-on-the-impact-of-food-insecurity-on-health-status-suicidal-ideation-and-quality-of-life-among-adults-living-in-poverty-a-study-in-urban-slums-of-bangladesh/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220814T140000
DTEND;TZID=UTC:20220814T150000
DTSTAMP:20260424T051510
CREATED:20220809T160235Z
LAST-MODIFIED:20220809T160348Z
UID:5321-1660485600-1660489200@isrt.ac.bd
SUMMARY:Seminar on “Identification of delayed transfer of care (DTOC) patients at the time of admission and prediction of their length of stay in NHS hospitals”
DESCRIPTION:Title: “Identification of delayed transfer of care (DTOC) patients at the time of admission and prediction of their length of stay in NHS hospitals” \nSummary: Delayed transfer of care (DTOC)\, also known as bed-blocking has been a persistent challenge for NHS hospitals. It occurs when a patient is clinically ready to be discharged\, however\, the patient cannot be discharged due to unavailability of other necessary care\, support\, or accommodation. The consequences of DTOC are high cost\, mortality\, infections\, depression and reductions in patients’ mobility and their ability to undertake daily activities. In this talk\, a case study based on an NHS hospital will be discussed to identify DTOC patients at the time of admission. Issues related to the date of discharge prediction and other related challenges will be highlighted. \nPresenter: \nMd Asaduzzaman \nAssociate Professor \nDepartment of Engineering\, School of Digital\, Technologies and Arts \nRoom B009A\, Cadman Building\, Staffordshire University \nStoke-on-Trent ST4 2DE\, United Kingdom \n 
URL:https://isrt.ac.bd/event/seminar-on-identification-of-delayed-transfer-of-care-dtoc-patients-at-the-time-of-admission-and-prediction-of-their-length-of-stay-in-nhs-hospitals/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220808T140000
DTEND;TZID=UTC:20220808T150000
DTSTAMP:20260424T051510
CREATED:20220728T112726Z
LAST-MODIFIED:20220728T112726Z
UID:5303-1659967200-1659970800@isrt.ac.bd
SUMMARY:Seminar on "Constrained inference in mixed models for clustered data"
DESCRIPTION:Title: Constrained inference in mixed models for clustered data\n\n\nAbstract:\nMixed models are commonly used for analyzing clustered data\, including\nlongitudinal data and repeated measurements. Unrestricted full maximum\nlikelihood (ML) methods have been extensively studied in the literature\nfor analyzing generalized\, linear\, and mixed models. However\, constraints\nor parameter orderings may occur in practice\, and in such cases\, we can\nimprove the efficiency of a statistical method by incorporating parameter\nconstraints into the ML estimation and hypothesis testing. In this talk\, I\nwill discuss constrained inference with generalized linear mixed models\n(GLMMs) under linear inequality constraints. Methods will be assessed\nusing both Monte Carlo simulations and actual survey data from a health\nstudy. \n\n\nPresenter:\n\nSanjoy Sinha\n\n\n\nProfessor\nSchool of Mathematics and Statistics\nCarleton University\, Ottawa\, ON\, Canada
URL:https://isrt.ac.bd/event/seminar-on-constrained-inference-in-mixed-models-for-clustered-data/
LOCATION:ISRT Seminar Room (3rd floor)\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220731T140000
DTEND;TZID=UTC:20220731T150000
DTSTAMP:20260424T051510
CREATED:20220727T180716Z
LAST-MODIFIED:20220728T112249Z
UID:5296-1659276000-1659279600@isrt.ac.bd
SUMMARY:Seminar on "A Generalized Variable Importance Metric to Identify Important Predictors from Black Box Machine Learning Methods"
DESCRIPTION:There is a statistics seminar at 2:00 pm on Sunday\, 31 July at ISRT. ISRT alumni member\, Kaviul Anam Khan will talk with the title “A Generalized Variable Importance Metric to Identify Important Predictors from Black Box Machine Learning Methods”. \n  \nBiodata of Kaviul Anam Khan is: \n  \nMohammad Kaviul Anam Khan\, PhD (c)\, Rafal Kustra\, PhD \nBiostatistics Division \nDalla Lana School of Public Health\, University of Toronto\,  Canada \n  \n 
URL:https://isrt.ac.bd/event/seminar-on-a-generalized-variable-importance-metric-to-identify-important-predictors-from-black-box-machine-learning-methods/
LOCATION:ISRT Seminar Room (3rd floor)\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220531T143000
DTEND;TZID=UTC:20220531T153000
DTSTAMP:20260424T051510
CREATED:20220529T172315Z
LAST-MODIFIED:20220529T172420Z
UID:5171-1654007400-1654011000@isrt.ac.bd
SUMMARY:Seminar on Analytics in Action
DESCRIPTION:A seminar will be held at 2:30 pm\, Tuesday\, 31 May. Further details are as follows:\n \nTitle: Analytics in Action\nSpeaker: Syed Shakil Ahmed\, Head of Segments\, Business Intelligence\, Commercial Division\, Grameen Phone Ltd\nVenue: ISRT Seminar Room (#402).
URL:https://isrt.ac.bd/event/seminar-on-analytics-in-action/
LOCATION:ISRT Seminar Room (3rd floor)\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
END:VCALENDAR