Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Susmita Datta

Unraveling Disease Mysteries: Statistical Models Reveal Cellular Conversations using Spatial Transcriptomics data. Abstract: Understanding cell microenvironments from spatially resolved transcriptomics data is a cutting-edge approach in biomedical research. This innovative method enables scientists to investigate the spatial organization of cells near diseased tissues and identify their inter - and intracellular communications through…


Ying Guo

A Regularized Blind Source Separation Method for Disentangling Dynamic Functional Connectome Abstract: Brain connectomics has become increasingly popular in neuroimaging studies to advance understanding of neural circuits and their association with neurodevelopment, mental illnesses, and aging. These analyses often face major challenges, including the high dimensionality of brain networks, unknown latent sources underlying the observed…


Zhen Xiang

Toward Trustworthy Machine Learning Under Training-Time Adversaries Abstract: Machine learning has demonstrated remarkable performance across various applications. However, significant concerns have arisen about its trustworthiness, such as risks posed by training-time adversaries. Facilitated by the need for 'big data', whether publicly available or locally accessible, training-time adversaries can easily manipulate a machine learning model by…


Xiuwei Zhang

Studying single cells through multi-condition and spatial context Abstract: With the advances in single cell technologies, cells are profiled through multiple modalities, and data on samples from an increasing number of individuals are obtained. I will present our method, scDisInFact, that disentangles variation in multi-batch multi-condition scRNA-seq datasets, and predicts data under unseen conditions. I will also present a few methods…


TN Sriram

Robust Recovery of the Central Subspace for Regression Using the Influence Function of the Renyi Divergence Abstract: A considerable amount of research in the literature has focused on quantifying the effect of extreme observations on classical methods for estimating the Central Subspace (CS) for regression through the study of influence functions and their sample estimates. Alternatively, a method that is inherently robust to data contamination…


Ching-Yu Huang

Got it?! Using Active Learning Techniques to Assess Student Learning Just in Time  Abstract: Explore how and why active learning techniques can enhance student engagement, collaboration, and reflection. In this workshop, we’ll discuss what an active learning course looks like from the instructor’s perspective across various disciplines. You’ll also experience and discuss a collection of active learning techniques- simple, easy to implement…


Tianming Liu

Unlock Brain Architectures to Harness AI and Model Neurologic Diseases Abstract: The human brain is an intricate generative system that segregates, integrates, and executes diverse functions seamlessly. Unlocking and representing the brain’s structural and functional architectures hold fundamental significance for neuroscience, healthcare of brain diseases, and brain-inspired artificial intelligence (AI), particularly generative AI (GenAI). This…


Daniela M. Witten

Agenda: 3:30 - 4:00pm - Arrival 4:00 - 4:05pm - Opening Remarks, Brooks Hall 145 4:05 - 5:00pm - Lecture, Dr. Daniela Witten, University of Washington, Brooks Hall 145  5:00 - 5:30pm - Break 5:30 - 7:00pm - Dinner, Founders Memorial Garden 7:05 - 7:45pm - After Dinner Talk, Dr. Daniela Witten. Brooks Hall 145 Biography: Dr. Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford…


Hani Safadi

Validation Criteria for Computationally Intensive Theory Construction Abstract: Computationally intensive theory construction (CITC) combines computational techniques with traditional quantitative and qualitative techniques to identify patterns in data and generate theoretical insights from those patterns. While guidelines exist for methodological approaches in CITC, the open-ended and exploratory nature of the genre presents challenges in terms…


Jin Lu

Provable Algorithms for Machine Learning in the Wild: Mobilizing, Hierarchizing, and Adaptive Morphing Abstract: Amidst increasing data volumes, addressing large-scale machine learning challenges in environments characterized by inherent variability is crucial. Such variability impacts data collection, format, quality, computational capacity, and connectivity within cyber-physical systems, thereby shaping the development of resilient machine…
Subscribe to Colloquium Series

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.