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X-WR-CALNAME:Institute of Applied Statistics and Data Science
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X-WR-CALDESC:Events for Institute of Applied Statistics and Data Science
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BEGIN:VEVENT
DTSTART;TZID=UTC:20230110T140000
DTEND;TZID=UTC:20230110T150000
DTSTAMP:20230110T072719Z
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:20221211T031929Z
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:20221126T152410Z
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:20221031T174157Z
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:20221023T155035Z
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:20220918T091217Z
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:20220809T160348Z
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:20220728T112726Z
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:20220728T112249Z
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:20220529T172420Z
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:20211213T132043Z
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:20211018T050015Z
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:20210318T081208Z
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:20201030T063523Z
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:20201030T063410Z
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:20201030T063253Z
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:20201030T063146Z
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:20201030T062927Z
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:20201030T063035Z
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:20201030T062808Z
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
BEGIN:VEVENT
DTSTART;TZID=UTC:20200617T110000
DTEND;TZID=UTC:20200617T130000
DTSTAMP:20201030T062612Z
CREATED:20201030T062612Z
LAST-MODIFIED:20201030T062612Z
UID:4458-1592391600-1592398800@isrt.ac.bd
SUMMARY:Visualizing world-wide Covid-19 data using R\, especially ggplot2
DESCRIPTION:Title:  “Visualizing world-wide Covid-19 data using R\, especially ggplot2” \n  \nSpeaker: Professor Mahbub Latif \, ISRT\, University of Dhaka \nDate &Time: Wednesday\, June 17\, 2020 at 11.00 AM.
URL:https://isrt.ac.bd/event/visualizing-world-wide-covid-19-data-using-r-especially-ggplot2/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200203T113000
DTEND;TZID=UTC:20200203T130000
DTSTAMP:20200130T024252Z
CREATED:20200130T022629Z
LAST-MODIFIED:20200130T024252Z
UID:4138-1580729400-1580734800@isrt.ac.bd
SUMMARY:Big Data Analytics: Wisdom or Folly!
DESCRIPTION:Title: Big Data Analytics: Wisdom or Folly! \n\nTime and venue: 11:30 am on 3rd of February (Monday) 2020 in ISRT Seminar Room\n\nSpeaker: Ejaz Ahmed\, PhD Professor at Brock University\, Canada\n\nAbstract:\n\nThere are hosts of buzzwords in today’s data-centric world\, and especially in digital and print media. We encounter data in every walks of life\, and for analytically and objectively-minded people\, data is everything. However\, making sense of the data and extracting meaningful information from it may not be an easy task. We come across buzzwords such as big data\, high dimensional data\, data science\, and open data without a proper definition of such words. The rapid growth in the size and scope of data sets in a host of disciplines has created a need for innovative statistical strategies analyzing such data. For example\, many private and public agencies are using sophisticated data mining strategies and/or big data analytics to reveal patterns based on collected information. Some examples of big data that have prompted demand are digital marketing\, customer service standards\, gene expression arrays\, social network modeling\, clinical\, genetics and phenotypic data. \nThe need for novel statistical strategies to analyze such data sets is pressing. This talk focuses on the development of statistical and computational strategies for a sparse regression model in the presence of mixed signals. The existing estimation methods have often ignored contributions from weak signals. However\, in real scenario many predictors altogether provide useful information for prediction\, although the amount of such useful information in a single predictor might be modest. The search for such signals\, sometimes called networks or pathways\, is for instance an important topic for those working on personalized medicine. We discuss a new “post selection shrinkage estimation strategy” that takes into account the joint impact of both strong and weak signals to improve the prediction accuracy and opens pathways for further research in such scenarios.
URL:https://isrt.ac.bd/event/big-data-analytics-wisdom-or-folly/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20200102T150000
DTEND;TZID=UTC:20200102T160000
DTSTAMP:20191230T090712Z
CREATED:20191230T084938Z
LAST-MODIFIED:20191230T090712Z
UID:3875-1577977200-1577980800@isrt.ac.bd
SUMMARY:Is Data Science the next step for the Statisticians?
DESCRIPTION:Data Science was hot a few years ago. It is no longer a hot topic. The tremendous growth in terms of using/adopting data science\, machine learning that the scientific community and the industry have observed over the past two years is noteworthy. Good thing is\, statisticians are gradually coming out of their cocoon to experience the new landscape. In this talk\, I will discuss why and how statisticians should take the next steps to learn machine learning before they (statisticians) potentially become obsolete in the industry.
URL:https://isrt.ac.bd/event/is-data-science-the-next-step-for-the-statisticians/
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:20191203T140000
DTEND;TZID=UTC:20191203T153000
DTSTAMP:20191124T172807Z
CREATED:20191124T172807Z
LAST-MODIFIED:20191124T172807Z
UID:3762-1575381600-1575387000@isrt.ac.bd
SUMMARY:PhD seminar talk on Tuesday\, December 3\, 2019 at 2pm
DESCRIPTION:Title: Modified Inferential Methods on Restricted Parameters in Multivariate Regression Analysis: Applications in Socio-demographic Research\n\nSpeaker: Sheikh Mohammad Sayem\, PhD researcher at ISRT\, DU\n\nAbstract:\n\nEfficient and significant empirical estimate of the multivariate regression parameters will be helpful for the policymaker to make the right decisions about sophisticated interrelated issues in the dynamic world. Since the end of the twentieth century\, statisticians are going forward to develop unique working methodology for estimating and testing restricted parameters. This study reviews existing methods and suggests modified maximum likelihood estimator\, modified multivariate t statistic and modified joint confidence interval to get efficient estimates for linear restricted parameters of multivariate regression with continuous responses. A Monte Carlo experiment is conducted to examine relative performance of the modified methods. The proposed methodology has been also applied to detect the numerical nexus among socioeconomic determinants\, food expenditure and total monthly expenditure in “Haor” areas of Bangladesh. The study reveals that logarithm form of total monthly expenditure and food expenditure as multivariate continuous responses are significantly related to total operating land\, logarithm form of family size and total monthly income considering a restriction on the parameters at different level of significance. The modified methods are found to perform significantly better than the existing methods.
URL:https://isrt.ac.bd/event/phd-seminar-talk-on-tuesday-december-3-2019-at-2pm/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20190827T140000
DTEND;TZID=UTC:20190827T150000
DTSTAMP:20190813T104343Z
CREATED:20190813T103955Z
LAST-MODIFIED:20190813T104343Z
UID:3597-1566914400-1566918000@isrt.ac.bd
SUMMARY:PhD seminar talk on August 27 at 2pm
DESCRIPTION:Title: Improved Statistical Approach for Climate Projection over Bangladesh using Downscaling of Global Climate Model (GCM) Outputs\n\nSpeaker: Md. Bazlur Rashid\n\nAbstract:\nGlobal Climate Model (GCM) has evolved from the Atmospheric General Circulation Models\n(AGCMs) widely used for daily\, seasonal and long term weather prediction. The most widely\nrecognized application is the projection of future climate states under various scenarios of\nincreasing atmospheric elements. Over the last few decades\, GCMs have been developed to\nemulate the present climate system and to project future climate scenarios. Despite notable\ndevelopment\, GCMs do not provide perfect simulations of reality and cannot provide the details\non very small spatial scales due to incomplete scientific understanding and limitations of\navailable observations in our country. For bridging the gap between the scale of GCMs and\nrequired 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\, precipitation\, wind speed\, pressure and humidity has been undertaken.\nCurrent research in Bangladesh has not addressed thermal comfort under climate change. In\naddition\, current studies for Bangladesh do not look at seasonality clearly. Extreme events\n(rainfall and temperature)\, thermal comfort\, humidity and wind characteristics\, along with\nseasonality\, under future climate in Bangladesh represent a further research gap and opportunity\nfor this research. The main object of study will attempt to find and/or develop efficient statistical\nmethods/tools for climate projection. The specific objectives are (i) to identify suitable model\nwith bias corrections for climate projection using climate model outputs; (ii) to explore the\neffectiveness of the bias correction statistical downscaling method in addressing the model-\nrelated uncertainties involved in future climate predictions; (iii) to identify a suitable\ndownscaling approach for climate model data to allow daily/ monthly or seasonal meteorological\nclimate impact studies; (iv) to develop modify algorithms for quantifying the time-variant\nuncertainty associated with meteorological extreme weather systems and persistent events under\nfuture climate scenarios and (v) to cross check between available statistical downscaling\ntechniques for future climate projections and scenarios generation over Bangladesh. So\, GCM\ninformation can be enhanced for better representation of the conditions in specific places by\nusing historically observed local climate information from weather stations. Statistical\nDownscaling of climate model can include independent components of climatic signals like El\nNino-Southern Oscillations (ENSO)\, Indian Ocean Dipole (IOD) and Pacific Decadal\nOscillations (PDO) for a long-lead weather forecast. So\, this information can also be used to\nimprove the future climate projections to assess potential impacts and guide climate-smart\ndecisions about climate resilience in Bangladesh.
URL:https://isrt.ac.bd/event/3597/
LOCATION:isrt seminar room
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20190730T140000
DTEND;TZID=UTC:20190730T150000
DTSTAMP:20190727T010829Z
CREATED:20190727T010829Z
LAST-MODIFIED:20190727T010829Z
UID:3560-1564495200-1564498800@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, 30th July 2019 from 2:00-3:00 pm
DESCRIPTION:Title: Optimum designs for multiple objectives \nSpeaker: Mahbub Latif\, PhD \nProfessor\, ISRT \nUniversity of Dhaka \nVenue: ISRT Seminar Room \nAbstract: \nTBA
URL:https://isrt.ac.bd/event/seminar-on-tuesday-30th-july-2019-from-200-300-pm/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20190115T140000
DTEND;TZID=UTC:20190115T150000
DTSTAMP:20190110T134806Z
CREATED:20190107T065821Z
LAST-MODIFIED:20190110T134806Z
UID:2996-1547560800-1547564400@isrt.ac.bd
SUMMARY:Seminar on 15 January Tuesday at 2 pm
DESCRIPTION:Title: Long-run relationship between the unemployment rate and the trade balance in the United States: an empirical analysis \nSpeaker: Haydory Akbar Ahmed \nMissouri State University \nDepartment of Economics\, 901 S National Avenue \nSpringfield\, MO 65897\, USA \n  \nAbstract: \nDynamics between the unemployment rate and the trade balance has both economic and political relevance.\nFrom a macroeconomic perspective\, the presence of a long run co-movement or equilibrium relationship\nalong with the nature of the short-run fluctuations in the long run co-movement will help us analyze the\ndynamics between the two in an objective manner. We use quarterly data from 1947: Q1 to 2017: Q4 on\nunemployment and trade balance to GDP ratio in the United States. Although traditional cointegration\ntests fail to detect a statistically significant long run co-movement\, a couple of threshold cointegration tests\nconfirm statistical evidence in favor of threshold cointegration or threshold long run co-movement between\nthe two. Estimated threshold vector error-correction model shows statistically significant evidence of falling\nunemployment rate with deteriorating trade balance. This finding indicates that as unemployment rates go\ndown\, the trade balance deteriorates to maintain the long run co-movement. Arguably\, as unemployment\ndecline imports rise faster than exports causing the trade balance to deteriorate.
URL:https://isrt.ac.bd/event/seminar-on-15-january-tuesday-at-2-pm/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20181021T140000
DTEND;TZID=UTC:20181021T153000
DTSTAMP:20181019T063917Z
CREATED:20181019T063917Z
LAST-MODIFIED:20181019T063917Z
UID:2834-1540130400-1540135800@isrt.ac.bd
SUMMARY:Seminar on Sunday\, 21 October 2018 at 2 pm
DESCRIPTION:Title: Joint Modeling of Longitudinal Response and Time-To-Event Data Using Conditional Distributions: A Bayesian Perspective\n\n \nSpeaker: Arindom Chakraborty\, PhD\n\n               Assistant Professor\n               Department of Statistics\n               Visva-Bharati University\, India\n \nAbstract: Over last twenty or more years a lot of methodological development and clinical application of joint models of longitudinal and time-to-event outcomes have come up. In these studies patients are followed until an event\, such as death occurs. In most of the articles\, using subject specific random effects as frailty\, the dependency of these two processes has been established. In this article\, we propose a new joint model that consists of a linear mixed effects submodel for longitudinal data and an accelerated failure model for the time-to-event data. These two submodels are linked together by not only latent random process\, but also by the conditional distributional assumption. This model will capture the dependency of the time-to-event on the longitudinal measurements more directly. Using standard priors\, a Bayesian method is developed for estimation. All computations based on the Bayesian inference via MCMC is implemented using OpenBugs. Our proposed method is evaluated by a simulation study which shows the efficiency of the conditional model over two other models: joint models with local independence and independent models. One clinically motivating data on Duchenne muscular dystrophy (DMD) syndrome and a popular data on AIDS are also analyzed.
URL:https://isrt.ac.bd/event/seminar-on-sunday-21-october-2018-at-2-pm/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20180717T140000
DTEND;TZID=UTC:20180717T150000
DTSTAMP:20180709T074018Z
CREATED:20180709T074018Z
LAST-MODIFIED:20180709T074018Z
UID:2670-1531836000-1531839600@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, July 17 at 2 pm
DESCRIPTION:Title: Non-inferiority testing under generalized Poisson distribution\n\nVenue: ISRT seminar room \n\nSpeaker:  Md Abu Manju\, PhD\n                Department of Mathematics and Computer Science\n                Eindhoven University of Technology\, Eindhoven\, The Netherlands\n\n \nAbstract: In recent years\, non-inferiority studies for count data have been increasingly used in the evaluation of new test methods or new treatments (e.g.\, microbiological test methods\, particle counters or drugs\, vaccines) and the Poisson distribution is commonly assumed as it provides a standard framework for the analysis of count data. A generalization of the Poisson distribution\, referred to as the generalized Poisson distribution (GPD)\, which models not only overdispersion\, but also underdispersion\, may be more appropriate in certain applications\, including comparison of microbiological test methods. We therefore propose tests for the assessment of non-inferiority and sample size calculation procedures under GPD. Asymptotic likelihood ratio test (LRT)\, Wald and Exact conditional tests are derived\, and the type I error rate and statistical power are computed for these three tests based on simulations. In terms of type I error rate and statistical power\, LRT and Wald test perform similarly\, but the Exact test is conservative and has less type I error rate and power than the LRT and Wald test. Finally\, expressions are derived for calculating the sample sizes that yield sufficient power to test the non-inferiority of the new methods or treatments based on Wald test and LRT.
URL:https://isrt.ac.bd/event/seminar-on-tuesday-july-17-at-2-pm/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20180606T103000
DTEND;TZID=UTC:20180606T120000
DTSTAMP:20180604T112349Z
CREATED:20180604T112349Z
LAST-MODIFIED:20180604T112349Z
UID:2568-1528281000-1528286400@isrt.ac.bd
SUMMARY:Seminar on Wednesday\, June 6\, 2018 at 10:30 am
DESCRIPTION:Title of the Talk: Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?\n\nSpeaker: Dr. M. Ehsan Karim\n\n               Assistant Professor\, SPPH\, UBC\n               Scientist & Biostatistician\, CHEOS\, Canada\n\nVenue: ISRT Seminar room\n\n—————————————Abstract-——————————————————————-\nThe use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient’s health status–related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders\, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998–2012)\, we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest\, least absolute shrinkage and selection operator\, and elastic net. Our results suggest that\, when the data analysis is done with epidemiologic principles in mind\, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data\, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error\, when a bias-based analysis is used. This talk is based on a joint work with Menglan Pang and Robert W Platt from McGill University [Epidemiology: 2018\,29(2):191–198].\n———————————————————————————————————————–
URL:https://isrt.ac.bd/event/seminar-on-wednesday-june-6-2018-at-1030-am/
CATEGORIES:seminar
END:VEVENT
END:VCALENDAR