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MUIDSI DISSERTATION DEFENSE: Explainable Artificial Intelligence To Stratify Pan-Cancer Patients For Immune Checkpoint Inhibitor Decision Making
Presenter:

Yuanyuan "Daisy" Shen

Date:

09-08-2022

Time:

4:30PM-5:30PM

Location:

Zoom

Immune checkpoints are a normal part of the immune system. It engages when proteins on the surface of immune cells called T cells recognize and bind to partner proteins on other cells, such as some tumor cells. Immune based therapies such as ICIs work by blocking checkpoint proteins from binding with their partner proteins. This

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Overhead imagery training data quality control: Methods for deep feature label anomaly detection
Presenter:

Aaron Wesley

Date:

09-08-2022

Time:

4:00PM-4:30PM

Location:

2501 Student Center (Leadership Auditorium)

Spatial analysis of large remotely-sensed imagery (RSI) training datasets for within-class variation and between-class separability is key to uncovering issues of data diversity and potential bias, not just when vetting datasets for usage, but also during the actual dataset creation stage. Project managers of complex imagery annotation campaigns have a largely unaddressed need for tools that continuously

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Biological pathways as graphs: comparison of select similarity methods
Presenter:

Misha Kovalenko

Date:

09-08-2022

Time:

3:30PM-4:00PM

Location:

2501 Student Center (Leadership Auditorium)

We extracted biomedical pathways from 47 publications related to non-small cell lung cancer (NSCLC) and mergedthem into a Neo4j graph database. With this graph serving as ground truth for comparing to other pathways that were extracted from other publications, we investigated several methods of calculating graph similarity. Unlike ontologies and engineered data sets that have uniform representations of data objects, graphs extracted from unstructured texts haveto be compared as text-described entities first, and by using common graph similarity methods second. In this work, we discuss ways of comparing biological graphs composed of text-described

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MUIDSI Comprehensive Exam — Measuring Geodiversity in Remotely-Sensed Imagery: Deep Spatial Change Detection Methods for Dataset Bias Mitigation and Visual Landscape Characterization
Presenter:

Aaron Wesley

Date:

07-26-2022

Time:

7:00pm-8:00pm

Location:

Zoom

Amid explosive growth in availability of multimodal remotely sensed imagery (RSI) data from a constellation of overhead sensors, a lack of understanding persists concerning the actual content of these data sources, in particular the nature of spatial variation in the visual and contextual features in the landscape being imaged. Whether described as spatial domain shift,

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GIScience as an Interdisciplinary Bridge in Indigenous Health Equity 
Presenter:

Daniel Beene

Date:

05-20-2022

Time:

8:25AM-9:35AM

Location:

Zoom

GIS and geographic theories can help bridge a crucial gap in interdisciplinary research projects. Geography is uniquely poised to offer critical and practical analytical support, wrangle spatial data and relate them to other datasets, and ground community-based science within the communities it aims to serve. In the context of the Navajo Nation, a key concern

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