Fully Harnessing the Potential of Machine Learning to Expand the Discovery Potential of the LHC
Lead Research Organisation:
UNIVERSITY COLLEGE LONDON
Department Name: Physics and Astronomy
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
People |
ORCID iD |
| Tim Scanlon (Primary Supervisor) | |
| Ava Lee (Student) |
Publications
Aad G
(2021)
Measurements of WH and ZH production in the $$H \rightarrow b\bar{b}$$ decay channel in pp collisions at $$13\,\text {Te}\text {V}$$ with the ATLAS detector
in The European Physical Journal C
Aaboud M
(2018)
Observation of H ? b b ¯ decays and VH production with the ATLAS detector
in Physics Letters B
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/P006736/1 | 30/09/2017 | 30/03/2026 | |||
| 1966430 | Studentship | ST/P006736/1 | 30/09/2017 | 29/09/2021 | Ava Lee |
| Description | Research Data Science Internship |
| Organisation | Alan Turing Institute |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Research privacy-enhancing technologies to build trustworthy digital identity systems. |
| Collaborator Contribution | Their expertise and training. Access to their data and computational resources. |
| Impact | Disciplines involved include Computer Science (Algorithms, Cloud Computing, Computing networks) for building the digital systems, and Mathematics (Cryptography) for security and privacy properties. The collaboration is very new, so no outcomes at the moment. |
| Start Year | 2021 |