Bayes Centre News: Meet the Edinburgh Students Joining the Turing Student Enrichment Scheme
Eight post graduate students from the University of Edinburgh in disciplines ranging from law to computational neuroscience and ethics, have joined this year's Alan Turing Institute Student Enrichment Scheme.
The Enrichment scheme has been designed to give students undertaking a PhD the opportunity to support and enhance their current research by accessing the facilities and opportunities available at The Alan Turing Institute and its partners. Students usually join in their second or third years of a typical doctorate to further the work they are undertaking for their research project and support the completion of the PhD.
We are delighted to announce that this year’s uptake has seen 8 University of Edinburgh’s students successfully make it onto the Enrichment scheme.
Enrichment students have the opportunity to find new collaborators for their research project, or to start a collaboration on something related to their field. Upon starting a placement students join a cohort from across the UK, as well as the range of researchers already active in London, Bristol or Leeds.
Enrichment students can engage remotely prior to their placement start date, in order to provide them with more time to connect and collaborate with the Turing.
Speaking about the Enrichment Scheme, Jack Gargan says:
This year I was very grateful to be awarded the Alan Turing Institute Enrichment Award.
This will afford me the opportunity to collaborate with an eclectic range of researchers in the fields of Artificial Intelligence and Machine Learning, with a view to exploring the application of novel multivariate techniques to my research in High Energy Physics.
See below to meet some of the new students!
Ayça Atabey is a second year PhD candidate at Edinburgh University. She has an LLM (IT Law) degree from Istanbul Bilgi University and an LLB (Law) degree from Durham University. Her PhD research is in Law and HCI fields, specifically, data protection law and Value Sensitive Design (VSD). Her PhD research explores concepts of 'vulnerability' and 'agency' and focuses on 'fairness' in designing privacy-respecting AI-driven technologies used by the elderly and children. Ayça is a PhD affiliate at the Centre for Data, Culture & Society (CDCS) of the Edinburgh Futures Institute and the Editor-in-Chief at SCRIPTed Journal on IT, IP, and Medical Law.
She works as a researcher at BILGI IT Law Institute, a research assistant at Digital Futures Commission-5Rights Foundation, and a Consultant at UN Women Europe & Central Asia. She received Alan Turing Institute's PhD Enrichment Award in 2022 and will start the PhD Enrichment Scheme in October.
During her time at the Alan Turing Institute, she will carry out interdisciplinary research, exploring privacy-respecting and gender-responsive ways to translate "fairness by design" into practice and empower vulnerable groups in their interaction with AI.
Jacob is a PhD student from North East England, working at the University of Edinburgh and now at the Alan Turing Institute. His work sits at the boundary of statistics and biomedical science, reflected by his joint supervisory team of Timothy Cannings, from Edinburgh's Centre for Statistics, and Kevin Myant, at the Institute of Genetics and Cancer).
His PhD work and the focus of his research at the Turing is to use genetic sequencing data from cancer patients, as well as modern methods in statistics and machine learning, to build systems that predict who will benefit from treatment with immunotherapy.
Profile page: Jacob Bradley (ed.ac.uk)
I am about to start my second year of PhD at the University of Edinburgh.
I am interested in statistical methodology surrounding the broad areas of Bayesian inference and causal inference. In both these fields, it is essential to design reliable, generic and efficient ways to compute probabilities. I want to advance understanding and methodological practices towards this goal.
In particular , at the Turing I look forward to seek for promising applications of my research by interacting with Data-Centric engineering researchers focussed on digital-twins and causal inference.
I am a Computational Neuroscience and Ethics PhD student at the University of Edinburgh, funded by Medical Research Scotland. My PhD research revolves around creating Computer models of CaMKII/NMDAR dynamics in 3D using MCell, BioNetGen and Biodynamo.
My carrier's landscape has been shaped by my passion for understanding what is happening in our bodies at the molecular level and how this makes us who we are. At the same time, I aim to sustain a love ethic in all the work that I do, meaning that I want to create work which is open and honestly expresses care, affection, responsibility, respect, commitment and trust. This usually sounds weird to my peer scientists – which is why I want to talk even more about it!
During my time at the Turing Institute, I will be working with various groups, including The Turing Way, The Turing Commons, Data Hazards and Open Life Sciences (cohort 6). I will be working on creating a framework within my PhD that allows the questioning of ethical standards and reproducibility of computer models in Computational Neurobiology. I want to create research that is accessible and reproducible, as well as raise awareness of why these are important for moving science towards a more loving future.
You can find out more about some of the projects I have going on here: https://susana465.github.io/
My name is Jack and I am a PhD student in the School of Physics and Astronomy, working with the ATLAS Collaboration at the Large Hadron Collider, CERN. My research is concerned with searching for long-lived particles beyond the Standard Model. The Standard Model of Particle Physics (SM) represents our best understanding of the fundamental constituents of the Universe and their interactions, standing up to experimental scrutiny to a remarkable degree of accuracy.
A number of observed phenomena, however, stand at variance with the predictions of the SM. To explain this, we therefore require new Physics: Physics “beyond the Standard Model”. This year I was very grateful to be awarded the Alan Turing Institute Enrichment Award.
This will afford me the opportunity to collaborate with an eclectic range of researchers in the fields of Artificial Intelligence and Machine Learning, with a view to exploring the application of novel multivariate techniques to my research in High Energy Physics. I am excited to investigate how the use of such machine learning methods may be employed to increase the sensitivity of our search for new physics.
Xue is a PhD Student in the School of Mathematics on the MAC-MIGS CDT, focusing on the graph-based algorithms and models. Previously her research focuses on the connection between spectral graph embedding and random graph models, and its application in real-world data including food webs, transportation networks, and social networks. During the Enrichment Scheme, she would like to investigate theories and algorithms for hypergraphs that leverage interactions between a group of agents and their applications in learning tasks such as structure discovery and link prediction.
Yuanchao is a PhD student at CSTR, mainly working on spoken language processing and multimodal machine learning. During his time with the Turing, he will be focusing on the recognition of speech and emotion via a joint framework using limited labeled data.
Profile page: https://sites.google.com/view/ycli