Bayes Centre News: Eight Edinburgh Students join the Alan Turing Enrichment Scheme
Eight Edinburgh students among the new cohort of enrichment students in disciplines ranging from environmental design, engineering and geosciences to security and crime science.
There are 75 new Enrichment students joining the Alan Turing Institute for the academic year 2021/2022 from 22 academic institutions across the UK.
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. The students, some biographies and their respective schools can be viewed below.
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.
This year’s scheme offers students the chance to undertake a 3, 6, 9 or 12-month placement at the Institute’s premises or at one of the two new locations – The Jean Golding Institute at the University of Bristol and the Leeds Institute for Data Analytics (LIDA) at the University of Leeds.
For the first time, 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.
Meeting other researchers excites Isabella Deutsch, a mathematics student from the University of Edinburgh about the scheme who says:
“What I’m looking forward to the most? That's simple: the people. I am eager to get to know the other Enrichment students, to work with world-leading researchers, and to contribute to the extraordinary, interdisciplinary network the Turing is building.”
|Isabella Deutsch||School of Mathematics||
Isabella is a PhD Student at the University of Edinburgh at the School of Mathematics focusing on Bayesian statistics. She gained degrees from the University of Vienna and the University of Oxford and collected ample work experience, from marketing to museum pedagogy. She also co-founded of the Piscopia Initiative to encourage women and non-binary students to consider a PhD in mathematics and currently serves as a student trustee on the board of directors of Edinburgh University Students' Association.
From January 2022 on Isabella will be joining the Alan Turing Institute for their Enrichment Scheme at the Leeds Institute for Data Analytics. Together with an industry partner she will be investigating how products sell together. She will focus on self-exciting point processes (`Hawkes Processes’), particularly on their diverse application and interpretation in high dimensions.
|James Fulton||School of Geosciences||James is interested in the pragmatic application of machine learning to climate physics. Weather and climate poses a lot of interesting problems. And some of the solutions, but certainly not all, could be ML. Previously, he studied unsupervised ML for isolating the natural variability of our climate. More recently, he has been using cycleGANs to make our climate simulations match observations better. He is also interested in how ML models can be used to replace the nastier parts of weather models.|
|Marios Kalomenopoulos||School of Physics and Astronomy||Marios is a third year PhD at the University of Edinburgh, working at the interface between cosmology and gravitational waves. He finished his physics undergraduate degree at the University of Athens, followed by a Masters in Theoretical Physics in Edinburgh. Now he studies how matter in the universe affects the propagation of gravitational waves and how the latter can be utilised to calculate cosmological parameters. For this he exploits data from large, numerical simulations. During his time in Turing he wants to learn more about working with big data and discover about new statistical techniques to analyse gravitational wave signals.|
|Nick Homer||School of Geosciences||Nick is a PhD student in the School of GeoSciences on the SENSE Earth Observation CDT programme, researching how we can use advanced image processing and machine learning techniques to better analyse satellite data of the cryosphere. During his time on the ATI Enrichment Scheme he hopes to develop new methods and data pipelines, used in conjunction with existing machine learning models, to map how Antarctic glaciers and ice sheets have responded to climate change since the launch of the first Earth Observation satellites in the 1960s|
|Ondrej Bohdal||School of Informatics||Ondrej is a second-year PhD student from the School of Informatics. He works on meta-learning, a part of machine learning that focuses on automatically learning how to improve learning algorithms. During his time at the ATI he will work on new applications of meta-learning as well as on algorithmic advances that make meta-learning more efficient.|
|Syu-Ning (Shunee) Johnn||School of Mathematics||Syu-Ning (Shunee) is currently a second-year PhD student from the Optimization and Operational Research research group at the University of Edinburgh. She is interested in mathematically modelling and formulating real-life supply chain problems using Combinatorial Optimisation techniques. Her research currently involves vehicle routing, facility location and other general network design problems in food logistics, taking customer uncertainty, driver workload balance and reliability issues into account. She looks forward to bringing Machine Learning into her model to deal with model deterioration, improve computational speed and analyse customer behaviours.|
|Tiffany Vlaar||School of Mathematics||Tiffany has a Physics BSc (Leiden University), Geophysics MSc (joint from TU Delft, ETH Zurich, RWTH Aachen), and a Theoretical Physics MSc (Perimeter Institute). For her Applied Mathematics PhD at the University of Edinburgh (joint with Heriot-Watt) she uses techniques from molecular dynamics and stochastic differential equations to develop new optimisation schemes for the training of deep neural networks. She's eager to talk about how to improve ML research in general and data applications that are not CIFAR/ImageNet.|
|Chang Luo||School of Informatics||Chang is a PhD student in the School of Informatics.|