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X-ORIGINAL-URL:https://isrt.ac.bd
X-WR-CALDESC:Events for Institute of Statistical Research and Training
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TZID:UTC
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TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20190101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20231009T140000
DTEND;TZID=UTC:20231009T153000
DTSTAMP:20260424T064028
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:20260424T064028
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:20260424T064028
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:20260424T064028
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:20260424T064028
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:20260424T064028
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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:20260424T064029
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
BEGIN:VEVENT
DTSTART;TZID=UTC:20211218T200000
DTEND;TZID=UTC:20211218T210000
DTSTAMP:20260424T064029
CREATED:20211213T132043Z
LAST-MODIFIED:20211213T132043Z
UID:4922-1639857600-1639861200@isrt.ac.bd
SUMMARY:Seminar on "What it means to be an Applied Statistician -- an industry perspective"
DESCRIPTION:Abstract:\nThis question was asked many times in the past. I am sure all of you have pondered about it at some point in your academic life. In this talk\, I will explain what it truly means to be an applied statistician. Spoiler alert: statistics is inherently applied from an industry perspective. We do not differentiate between a statistician and an applied statistician. \nSo are you ready to apply your skills? What do we expect a statistician to do in the industry? Are academic institutions making you industry-ready? There is no one correct answer here. It depends on the context. In this talk\, I will share my experience in the industry that would help you understand the opportunities ahead and how you can align your focus and take steps to make the most out of it. \n  \nSpeaker: \nEnayetur Raheem\, Ph.D.\nPrincipal Data Scientist at ConcertAI\n(A DaaS AI startup in Oncology)\nUnited States of America
URL:https://isrt.ac.bd/event/seminar-on-what-it-means-to-be-an-applied-statistician-an-industry-perspective/
LOCATION:Online\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211019T113000
DTEND;TZID=UTC:20211019T123000
DTSTAMP:20260424T064029
CREATED:20210930T173830Z
LAST-MODIFIED:20211018T050015Z
UID:4853-1634643000-1634646600@isrt.ac.bd
SUMMARY:Seminar on Improved Statistical Approach for Climate Projection over Bangladesh using Downscaling of Global Climate Model Outputs
DESCRIPTION:Title: Improved Statistical Approach for Climate Projection over Bangladesh using Downscaling of Global Climate Model Outputs \nSpeaker: Md. Bazlur Rashid \nAbstract:\nBangladesh is facing from severe impacts of climate change because of its low-lying coastal\nareas\, deforestation\, and rapid human population growth\, technological and industrial\nintervention. The climate change parameters namely\, temperature\, heavy rainfall\, sea surface\ntemperature\, frequency of floods\, cyclones and storm surges are showing significant changed at\nevery year and it has a massive impact on food production which may turn into food uncertainty\nby amplifying the environmental and socio-economic pressure. The impact of climate change on\nenvironment is immeasurable and it has large threat in our country. Appropriate strategies based\non the climate information research will reduce the vulnerability of livelihoods and\ninfrastructures to future climate change and contributes to achieve sustainability in resources.\nClimate change projection poses an unprecedented challenge for meteorology\, climatology etc.\nGlobal Climate Model (GCM) has evolved from the Atmospheric General Circulation Models\n(AGCMs) broadly used for daily\, seasonal and long term climate projection. The most widely\ndocumented application is the projection of future climate conditions under several scenarios of\nincreasing atmospheric components. Over the last few eras\, GCMs have been developed to\nmatch the present climate system and to project future climate scenarios. Despite outstanding\nprogress\, GCMs do not deliver seamless simulations of reality and cannot afford the specifics\non very small spatial scales due to imperfect scientific understanding and limitations of\navailable observations in our country. For connecting the gap between the scale of GCMs and\ncrucial resolution for practical applications\, downscaling provides climate change information\nat a suitable spatial and temporal scale from the GCM data. No downscaling for Bangladesh of\ndetail temperature and precipitation has been undertaken. Current research in Bangladesh has not\naddressed seasonal based climate projections. Extreme events especially temperature and rainfall\nalong with seasonality\, under future climate in Bangladesh represent a further research gap and\nopportunity for this research. The main object of study is to develop efficient statistical\nmethods for climate projection. The specific objectives are (i) to identify suitable model with\nbias corrections for assessing and understanding climate impacts on rainfall and temperature\nusing climate model outputs; (ii) to explore the efficiency of the bias correction statistical\ndownscaling method in addressing the model-related uncertainties involved in future climate\npredictions; (iii) to classify a suitable downscaling approach for climate model data to allow\nseasonal meteorological climate impact studies and (iv) to cross check between available\nstatistical downscaling techniques for future climate projections and scenarios generation over\nBangladesh.\nTo achieve the objectives\, this research connects the gap between large and local scale climate\nvariables\, a number of statistical downscaling methods are used. A stepwise multiple linear\nregression method is used in study. One significant motivation behind the empirical statistical\ndownscaling method applied in this research is to make use of the large scales that the models \nare able to reproduce realistically to say something about local changes. Altogether GCMs have\na minimum skillful scale which means that their separate grid-box values are not a good diagram\nof the area they represent in the actual world (because computers work with discrete numbers).\nThe procedure of common EOF analysis makes it possible to identify common spatial patterns in\nreanalysis and GCM data on a scale that is good represented by climate models.\nThis study reveals that future CO 2 emissions are expected to have severe consequences for the\nwinter season in Bangladesh in terms of significant warming in the whole country. All emission\nscenarios show an increasing mean temperature in Bangladesh\, but while RCP2.6 shows the\ntemperature plateauing mid-century\, the average increase is 2 times higher in the far future\ncompared to the near future assuming RCP4.5\, and 4 times higher assuming RCP8.5. Finally\,\nthis research demonstrates that while warming may be unavoidable\, there are still opportunities\nto limit the severity of climate change in the future.
URL:https://isrt.ac.bd/event/improved-statistical-approach-for-climate-projection-over-bangladesh-using-downscaling-of-global-climate-model-outputs/
LOCATION:Online\, Institute of Statistical Research and Training\, University of Dhaka\, Dhaka\, Please Select\, 1000\, Bangladesh
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210320T200000
DTEND;TZID=UTC:20210320T210000
DTSTAMP:20260424T064029
CREATED:20210318T074357Z
LAST-MODIFIED:20210318T081208Z
UID:4680-1616270400-1616274000@isrt.ac.bd
SUMMARY:Clinical Trials and application of Statistical Modeling and Machine Learning in Biomedical Data
DESCRIPTION:Title: Clinical Trials and application of Statistical Modeling and Machine Learning in Biomedical Data \n  \nAbstract: \nClinical trials/research are conducted to examine the clinical questions of practicing physicians. It is important to design trials appropriately in advance.  A randomized\, controlled trial is the ultimate design for treatment comparisons at the final confirmatory stage. The need and impact of a proper clinical trial has comprehended during COVID-19 pandemic. Over the years\, there has been substantial advancement of clinical trial design\, conduct of study and analysis of clinical trial data. In my talk\, I will discuss my experience with the evolvement of trial design including group sequential and adaptive trials\, analysis of clinical data using frequentist and Bayesian approach\, techniques to adjust for multiplicity\, Estimand framework and application of causal inference\, advanced data visualization and use of Machine Learning and Deep Learning to build predictive models for biomedical data. \nSpeaker: Jahangir Alam\, MS\n                Data Scientist Associate Director\,\n                Novartis Pharmaceutical\, New Jersey\, USA.
URL:https://isrt.ac.bd/event/clinical-trials-and-application-of-statistical-modeling-and-machine-learning-in-biomedical-data/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20201010T193000
DTEND;TZID=UTC:20201010T210000
DTSTAMP:20260424T064029
CREATED:20201030T063523Z
LAST-MODIFIED:20201030T063523Z
UID:4474-1602358200-1602363600@isrt.ac.bd
SUMMARY:Opportunities and Challenges for Statisticians During Pandemics
DESCRIPTION:Title “Opportunities and Challenges for Statisticians During Pandemics” \nThe speaker: Abdus S. Wahed\, Professor of Biostatistics\, School of Public Health\, University of Pittsburgh\, USA. \nDate & time: Saturday October 10\, 2020 at 7.30 PM (Dhaka time). \n  \n—————————Abstract———————————————————————– \nCOVID 19 has dramatically changed the lifestyles of people around the globe. In the midst of pandemic\, all of us are struggling to lead a “normal” life that we have been used to: many have lost their jobs\, homes\, and so on. While the first responders\, e.g.\, police\, physicians\, nurses\, grocery vendors are keeping us afloat risking their lives to COVID\, as statisticians\, we have other challenges to overcome. In this talk\, I will informally talk about challenges and opportunities that pandemic brings to the life of a statistician\, and hope to have a fruitful discussion with colleagues and students. \n———————————————————————————————————
URL:https://isrt.ac.bd/event/opportunities-and-challenges-for-statisticians-during-pandemics/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200726T193000
DTEND;TZID=UTC:20200726T210000
DTSTAMP:20260424T064029
CREATED:20201030T063410Z
LAST-MODIFIED:20201030T063410Z
UID:4472-1595791800-1595797200@isrt.ac.bd
SUMMARY:R Shiny app for the beginners
DESCRIPTION:Title: “R Shiny app for the beginners” \nSpeaker: Nabil Awan\, Assistant Professor (on leave)\, ISRT\, DU and PhD candidate at the University of Pittsburgh\, USA. \nDate & Time: Sunday\, July 26\, at 7.30PM \n  \nSuumary: R Shiny app is getting increasingly popular in both industry and academia. One reason is the automation that many companies are focusing on nowadays. Almost all grants in academia have a ‘technology component’ these days that often requires creating an interactive dashboard. There are competitors like Power BI\, Tableau dashboard\, etc. but those can also be integrated with R. Moreover\, using R allows a range of statistical methods that are not readily available in other software. While there are so many free materials available online to learn the R Shiny app\, some of us might have never gotten the time and opportunity to learn it. This session will take a hands-on DIY approach and help the participants create and host their first simple R Shiny app on the spot. We will emphasize explaining the ‘structure’ of the app so that the participants are able to understand the more complex apps available online. This session will also direct the participants to resources where they can learn more.
URL:https://isrt.ac.bd/event/r-shiny-app-for-the-beginners/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200713T090000
DTEND;TZID=UTC:20200713T100000
DTSTAMP:20260424T064029
CREATED:20201030T063253Z
LAST-MODIFIED:20201030T063253Z
UID:4470-1594630800-1594634400@isrt.ac.bd
SUMMARY:A brief introduction to Transcriptomic data analysis
DESCRIPTION:Title: “A brief introduction to Transcriptomic data analysis” \nSpeaker: Dr. Tanbin Rahman\, Postdoctoral Fellow at the MD Anderson Cancer Center\, USA. \nDate & time: Monday\, July 13\, 2020 at 9.00AM \n———————————Summary————————————————————- \nThis presentation will briefly discuss the different types of transcriptomic datasets in the field of statistical genomics. At first\, the structure of the datasets will be discussed. The preprocessing of the datasets followed by Differential Expression (DE) analysis and pathway analysis aimed at identifying the candidate genes and functional annotation of the candidate gene sets respectively\, will be discussed. Finally\, the application of supervised/unsupervised machine learning algorithms in genomic studies will be explored.
URL:https://isrt.ac.bd/event/a-brief-introduction-to-transcriptomic-data-analysis/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200630T110000
DTEND;TZID=UTC:20200630T120000
DTSTAMP:20260424T064029
CREATED:20201030T063146Z
LAST-MODIFIED:20201030T063146Z
UID:4468-1593514800-1593518400@isrt.ac.bd
SUMMARY:Academic Writing Skills: Useful Tips for Beginners
DESCRIPTION:Title: ‘Academic Writing Skills: Useful Tips for Beginners’\, \nSpeaker: Prof. Tamanna Howlader\, ISRT\, University of Dhaka \n  \nDate & time: Tuesday\, June 30\, 2020 at 11.30AM.
URL:https://isrt.ac.bd/event/academic-writing-skills-useful-tips-for-beginners/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200628T090000
DTEND;TZID=UTC:20200628T100000
DTSTAMP:20260424T064029
CREATED:20201030T062927Z
LAST-MODIFIED:20201030T062927Z
UID:4464-1593334800-1593338400@isrt.ac.bd
SUMMARY:Introduction to Deep Neural Network using R
DESCRIPTION:Title: “Introduction to Deep Neural Network using R” \n  \nSpeaker: Tuhin Sheikh\, ISRT\, University of Dhaka \nand PhD candidate at the University of Connecticut\, USA\, \n  \nDate and Time: Sunday\, June 28\, 2020 at 9.00AM \n  \n—————————–Summary———————————————— \nThe deep neural network (DNN) modelling has been considered to be a thriving topic in recent years. The DNN can be considered as a generalization of traditional regression analysis. Considering a particular objective of predicting output\, traditional regression analysis extracts low level features based on the observed input covariates. However\, when we deal with high dimensional data and a numerous input features\, low level feature extraction often leads to low prediction accuracy. The DNN on the other hand\, has been found to effective in extracting high level feature with promising prediction accuracy. In DNN\, we assume that similar to the neural system\, the input covariates go through different neurons at different layers until it reaches the final output layer. The higher the number of middle layers\, the deeper the network is. If there is no (hidden) layers in the middle\, it generalizes to only input and output layer as in traditional regression. Like regression\, DNN requires a loss function and criterion for minimization. The key difference would be\, extraction of hidden features and connecting those to the final output prediction. \nDue to the advancement of computer algorithms and emergence of interesting data\, this research field has been found to interesting in present times. Many big companies (e.g. Google\, Facebook\, Boehringer Ingelheim\, etc.) have been practicing deep neural network modelling due to the satisfactory performance. In the past\, mostly computer scientists and engineers contributed in this field. However\, the attractive mathematical foundation behind this interesting methodology got the attention of the Statisticians recently. As Statisticians\, there are huge scopes to contribute to this emerging field through statistical innovation. In this session\, the audience can expect the discussion on background and basic mathematical foundation of DNN. Also\, I will introduce an R package “Keras”\, which can be used to work with deep neural network. At the end of the discussion on this interesting topic\, I will spend some time discussing higher study experience in USA.
URL:https://isrt.ac.bd/event/introduction-to-deep-neural-network-using-r/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200624T150000
DTEND;TZID=UTC:20200624T170000
DTSTAMP:20260424T064029
CREATED:20201030T063035Z
LAST-MODIFIED:20201030T063035Z
UID:4466-1593010800-1593018000@isrt.ac.bd
SUMMARY:Sample Size Determination for Survey Research
DESCRIPTION:Title: “Sample Size Determination for Survey Research” \nSpeaker: Prof. Muhammad Shuaib\, ISRT\,  University of Dhaka \nDate & time: Wednesday\, June 24\, 2020\, at 3.15 PM.
URL:https://isrt.ac.bd/event/sample-size-determination-for-survey-research/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200622T110000
DTEND;TZID=UTC:20200622T130000
DTSTAMP:20260424T064029
CREATED:20201030T062800Z
LAST-MODIFIED:20201030T062808Z
UID:4462-1592823600-1592830800@isrt.ac.bd
SUMMARY:Academic Writing
DESCRIPTION:Title: “Academic Writing” \nSpeaker: Professor Syed Shahadat Hossain \, ISRT\, University of Dhaka \nDate and Time: Monday\, June 22\, 2020 at 11.00AM.
URL:https://isrt.ac.bd/event/academic-writing/
CATEGORIES:seminar
END:VEVENT
END:VCALENDAR