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X-ORIGINAL-URL:https://isrt.ac.bd
X-WR-CALDESC:Events for Institute of Applied Statistics and Data Science
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DTSTART:20250101T000000
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DTSTART;TZID=UTC:20260105T140000
DTEND;TZID=UTC:20260105T150000
DTSTAMP:20260101T042836Z
CREATED:20260101T042836Z
LAST-MODIFIED:20260101T042836Z
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SUMMARY:Applied Statistics and Data Science Seminar on Monday 5 January 2026
DESCRIPTION:Title: A Moment-Based Generalization To Post-Prediction Inference \nVenue\, date and time: ISRT\, 5 January 2026\, 2 pm \nSpeaker: Awan Afiaz\, PhD candidate at the Department of Biostatistics\, University of Washington Seattle\, WA\, USA and ISRT alumnus \nAbstract: \nAs artificial intelligence (AI) and machine learning (ML) become increasingly integrated into scientific research\, investigators frequently substitute predicted outcomes for expensive or difficult-to-measure data. However\, treating these AI/ML-generated predictions as true observations can lead to biased estimates and anti-conservative inference. While high predictive accuracy is often assumed to ensure valid downstream inference\, statistical challenges in inference with predicted data (IPD) fundamentally reduce to two sources of error: bias\, when predictions systematically distort relationships among variables\, and variance\, when uncertainty from prediction models is inadequately propagated. Wang et al. (2020) introduced post-prediction inference (PostPI)\, a pioneering method that addresses this challenge by modeling the relationship between predicted and observed outcomes in a small gold-standard dataset to calibrate inference in larger unlabeled samples. PostPI has been influential in formalizing the IPD problem and demonstrating how naive approaches fail to appropriately reflect uncertainty. However\, PostPI relies on a critical assumption: that prediction errors are uncorrelated with covariates of interest. In realistic settings where prediction algorithms exhibit systematic errors related to input features\, this assumption is often violated\, leading to biased parameter estimates and inadequate error control. We revisit PostPI in light of recent methodological advances and propose a moment-based generalization that relaxes this restrictive assumption. Our extension explicitly accounts for the covariance between prediction errors and covariates by incorporating an additional correction term estimated from the labeled dataset. This approach yields unbiased point estimates under standard conditions while incorporating a simple scaling factor that appropriately reflects the contribution of relationship model uncertainty regardless of sample size allocation. Through extensive simulations across three data-generating scenarios\, we demonstrate that our method maintains nominal Type-I error rates and achieves proper coverage probability\, even when the labeled sample is substantially smaller than the unlabeled sample settings where both naive approaches and original PostPI fail. Our work illustrates the classic bias-variance trade-off inherent to IPD’s challenges and confirms that there is no free lunch when substituting predicted outcomes for true measurements.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-5-january-2026/
CATEGORIES:seminar
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DTSTART;TZID=UTC:20260305T120000
DTEND;TZID=UTC:20260305T133000
DTSTAMP:20260304T100223Z
CREATED:20260221T064222Z
LAST-MODIFIED:20260304T100223Z
UID:8957-1772712000-1772717400@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Thursday 05 March 2026
DESCRIPTION:Title: FertiMeter: A Data-Driven Innovation to Address the Reproductive Health Crisis of Polycystic\nOvary Syndrome \nVenue\, date and time: ISRT\, 5 March 2026\, 12:15 pm \nSpeaker: K. M. Tanvir\, Lecturer\, ISRT\, University of Dhaka \nAbstract: \nBackground:\nPolycystic ovary syndrome (PCOS) affects around 12.5% of women in Bangladesh and is a major cause of infertility and pregnancy complications. Although early detection can help manage symptoms and reduce risks\, nearly 70% of women remain undiagnosed due to limited awareness and inadequate access\nto medical care.\nObjectives:\nThis study aims to develop a data-driven machine learning model that predicts the likelihood of PCOS using non-clinical features and to integrate it into a mobile application\, FertiMeter.\nMethods:\nA total of 546 participants\, including 273 women diagnosed with PCOS and 273 without PCOS\, were enrolled in the study. The CatBoost machine learning algorithm was applied to develop a predictive model for PCOS status and the model was incorporated into the FertiMeter mobile application.\nKey Findings:\nUsing eight SHAP-selected non-clinical features\, the CatBoost model achieved an average cross-validated accuracy of 86%.\nConclusions:\nApproximately 6.7 million women in Bangladesh who remain undiagnosed with PCOS can use FertiMeter mobile application to assess their likelihood of having the condition free of cost.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-23-february-2026/
CATEGORIES:seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260410T150000
DTEND;TZID=UTC:20260514T213000
DTSTAMP:20260408T052830Z
CREATED:20250512T170620Z
LAST-MODIFIED:20260408T052830Z
UID:7859-1775833200-1778794200@isrt.ac.bd
SUMMARY:R for Applied Statistics and Data Science 2026
DESCRIPTION:The upcoming R training program starts on April 10\, 2026.\nFollow the website for details: https://isrt.ac.bd/training/r/
URL:https://isrt.ac.bd/event/r-for-applied-statistics-and-data-science-2025/
LOCATION:ISRT\, ISRT\, University of Dhaka\, Dhaka\, Bangladesh
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260728T140000
DTEND;TZID=UTC:20260728T150000
DTSTAMP:20260723T055519Z
CREATED:20260723T053020Z
LAST-MODIFIED:20260723T055519Z
UID:9317-1785247200-1785250800@isrt.ac.bd
SUMMARY:ASDS Seminar on Tuesday 28 July 2026
DESCRIPTION:Title: Sir Ronald A. Fisher: The Father of Modern Statistics – His Life\, Contributions\, and Legacy \nVenue\, date and time: IASDS (former ISRT)\, 28 July 2026\, 2:00 pm \nSpeaker: Dr. Md. Hasinur Rahman Khan\, Professor\, IASDS\, University of Dhaka \nAbstract: \nSir Ronald A. Fisher (1890–1962) is widely regarded as the father of modern statistics and one of the most influential scientists of the twentieth century. Beyond statistics\, Fisher played a central role in establishing modern population genetics by integrating Mendelian genetics with Darwinian evolution. This lecture presents a chronological journey through Fisher’s life\, highlighting his scientific achievements\, major publications\, collaborations\, and intellectual debates with contemporaries. It also discusses his controversial views on eugenics and smoking\, offering a balanced perspective on his legacy.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-tuesday-28-july-2026/
CATEGORIES:seminar
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