The Jean Golding Institute’s Seed Corn Scheme
The Jean Golding Institute are pleased to announce the Post Graduate Researcher Seed Corn Funding awards. Every year we provide seed corn funding to Post Doctoral Researchers, but this year we are pleased to also be able to provide funding to small-scale projects for Post Graduate Researchers at the University of Bristol, which we hope will help to develop their projects further. Through our Seed Corn Funding Scheme, we aim to support initiatives to develop interdisciplinary research in data science (including Artificial Intelligence) and data-intensive research.
The Winners
Zinuo You is currently studying a PhD in Computer Science, at the University of Bristol. They have a Bachelor’s degree in Electronic Science & Technology from Southwest University, in addition to a Master’s in Electronic & Electrical Engineering from the University of Sheffield. Their research interests include graph neural networks, graph structural learning and deep learning in finance.
Isolde Glissenaar is a PhD researcher in Glaciology, researching sea ice thickness in the Canadian Arctic using remote sensing and machine learning.
Isolde has created a sea ice thickness product for the channels in the Canadian Arctic Archipelago. Isolde will use their JGI PGR seed corn funding to make the product operationally available for shipping navigators, local communities, and climate researchers to use.
Holly Fraser is a final year PhD student in Digital Health and Care, with a background in psychology and neuroscience.
During her PhD studies Holly has been using machine learning to investigate risk and protective factors for depression and anxiety, using birth cohort data. Her research involves using natural language processing (NLP) techniques to analyse the online discourse of mental health and medication use, using Reddit data.
Holly is excited to start the project and explore a new data analysis method, which she thinks will be of great value to her existing work.
Jiao Wang & Ahmed Mohamed
Jiao Wang is a fourth-year PhD student studying Hydroinformatics with the Department of Civil Engineering. Her research focuses on identifying and quantifying the information content and transmission in the catchment hydrological modelling system.
Ahmed Mohamed is a third-year PhD student studying Rainfall Nowcasting with the Department of Civil Engineering. His research focuses on improving rainfall nowcasting based on deep learning methods and optical flow models.
Both Ahmed and Jiao are interested in water resource problems, climate change, numerical modelling and artificial intelligence.
Sydney Charitos & Lauren Thompson
Sydney Charitos is a second-year PhD student in Digital Health & Care. Her research focuses on the feasibility of Ecological Momentary Assessment (EMA), asking survey questions both in the moment and in context to assess chronic pain in young children aged 5 – 11 years old.
Due to having a background in Electronics and Computer Science, Sydney is especially interested in EMA data visualization for the many stakeholders in a child’s life.
Lauren Thompson is a third-year PhD student in Digital Health & Care. Her research explores the feasibility of a multi-stakeholder self-management tool for children aged 7-11 years with chronic fatigue, through qualitative and participatory design methods.
More information
For more information about other funding we have provided and schemes we offer, find out more on our Funding page, and take a look at previous projects we have supported, on our Projects page.
Ahmed Mohammed is a postdoctoral researcher in the field of water engineering. His PhD research investigates precipitation forecasting at high spatial and temporal scales using various methods such as numerical weather prediction, optical flow-based models, and deep learning techniques.
Hongbo Bo is a Ph.D. student in computer science. His research focuses on social network analysis by using graph data mining techniques like graph neural networks, continual learning, and contrastive learning methods.
Anuradha Kamble is a Postgraduate Research student at Bristol Composite Institute in the Department of Aerospace Engineering. Prior to this, she had three years of industrial experience in the commercial HVAC (Heating Ventilation and Air Conditioning) and EV (Electric Vehicle) industries. Recent discoveries in materials science have seen growing applications of machine learning for materials discovery by leveraging the experimental data generated over many years in different studies. Her research project is titled “Exploiting the deep learning technique to study a novel nano-modified polymer composite.” The goal of this study is to implement deep learning techniques to investigate the most suitable parameters e.g., material composition, processing temperature, and time, to study a nano-modified polymer composite. The objective of this project is to improve the generalizability of deep learning model so that it can be widely applicable to any novel materials developed in the University of Bristol. Similar applications in the field of bio sciences will be explored by collaborations with other universities across the UK.
Levke Ortlieb is a final year PhD student in Physics. She works on supercooled liquids and glasses. Levke uses 200nm colloids as a model system in 3D using super-resolution confocal microscopy. Detecting the particle positions is very challenging so she and Abdelwahab Kawafi are using convolutional neural networks for particle tracking.