Keith J Murphy
Biographic Data
| ID | 5906835 |
|---|---|
| NAME | Keith J Murphy |
| GIVEN NAMES | Keith J |
| FAMILY NAME | Murphy |
| SIGNATURE | MURPHY K J |
| AFFILIATIONS | University College Dublin |
| ORCID | 0000-0002-8865-0467 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Leading in the academy
The under-representation of women in senior echelons of the academy, particularly in disciplines which have been historically male-dominated and male-led, is well-documented internationally. The narrative, however, is not a linear one, and there have been intervals of alteration and narrow apertures of opportunity. This article focuses on one of those intervals, the period 1957–1962, which saw three women professors being appointed to the Science…
Exploring Feasibility of Multivariate Deep Learning Models in Predicting Covid-19 Epidemic
Background: Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging to the commonly used mechanistic model when our understanding of this pandemic rapidly refreshes. Objective: We aim to develop a data-driven workflow to extract, process, and develop deep le…
No prominent works on this page.
Exploring Feasibility of Multivariate Deep Learning Models in Predicting Covid-19 Epidemic
Background: Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging to the commonly used mechanistic model when our understanding of this pandemic rapidly refreshes. Objective: We aim to develop a data-driven workflow to extract, process, and develop deep le…
Leading in the academy
The under-representation of women in senior echelons of the academy, particularly in disciplines which have been historically male-dominated and male-led, is well-documented internationally. The narrative, however, is not a linear one, and there have been intervals of alteration and narrow apertures of opportunity. This article focuses on one of those intervals, the period 1957–1962, which saw three women professors being appointed to the Science…
Aesthetics (1 works) · Art (1 works) · Artificial Intelligence (1 works) · Canadian Identity and History (1 works) · Computer Science (1 works) · COVID-19 diagnosis using AI (1 works) · COVID-19 epidemiological studies (1 works) · Data mining (1 works) · Gender Studies (1 works) · Gender Studies (1 works)