PhD on machine learning for constrained optimisation at York
Peter Nightingale
Hello everyone,
I'm advertising a funded PhD place, please see the advert below. Also, I would be happy to discuss other topics related to constraint programming and SAT.
The funding scheme usually only covers 'home' students (i.e.
students eligible for UK fees) but does provide a stipend as well
as paying the tuition fees. We are currently exploring whether the
'international' fees can be waived, but for now it should be
considered as home students only.
Please forward to anyone who may be interested.
Thanks, and best wishes,
Peter
About the Project
Constraint satisfaction and optimization problems are an important class of problems in artificial intelligence, where a set of decisions need to be made together, so that some requirements are satisfied and perhaps also some criteria optimised. There are many examples including vehicle routing, scheduling, and planning.
This PhD project would be to apply machine learning methods to help solve constraint problems, whether by predicting the answer (or part of it) or reducing the amount of work that a constraint solver needs to do.
One possibility is end-to-end learning, where a machine learning model is trained to predict the entire solution to a problem – for example, predicting the solution to a logic problem using a graph convolutional network [1], a type of deep learning network. Even answers that are not 100% correct can be useful, e.g. as a starting point for a systematic solver.
Another direction the project could take is learning-to-prune [2]. In this approach, a machine learning model is trained to reduce the size of the problem, making it easier to solve. For example, in a vehicle routing problem, a learning-to-prune model could delete parts of the road network that are very unlikely to be used in an optimal solution, thus speeding up the solver.
The initial focus of the project would be on demonstrating the chosen method on one problem class. Later, it may be possible to generalise to more than one class, working towards a general system for learning to solve constraint problems specified in a language (such as those written in the Essence Prime language for the tool Savile Row [3]).
Research supervision
If successful, you will conduct your research under the supervision of Dr Peter Nightingale. He is a lecturer, and member of the AI group in the Department of Computer Science.
Funding requirements
To be considered for this funding you must:
- meet the entrance requirements for a PhD in Computer Science
- We will look favourably on applicants that can demonstrate knowledge in undergraduate-level machine learning, and have strong programming skills. Also, a broader background in undergraduate-level AI (particularly the areas of search and logic) would be an advantage.
We welcome applications from home and international students. UKRI studentships are open to international students but please note that UKRI will normally limit the proportion of international students appointed each year through individual doctoral training programmes to 30% of the total.
Apply for this studentship
1. Apply to study
- You must apply online for a full-time PhD in Computer Science.
- You must quote the project title (Machine Learning for Solving Constrained Optimisation Problems) in your application.
- There is no need to write a full formal research proposal (2,000-3,000 words) in your application to study as this studentship is for a specific project.
2. Provide a personal statement. As part of your application please provide a personal statement of 500-1,000 words with your initial thoughts on the research topic.
Deadlines
The closing date for the receipt of applications is Wednesday 22nd March 2023.
Interviews are expected to take place during the week commencing 3rd April 2023.
The studentship will begin in October, 2023.
Informal enquiries
Project enquiries: Peter Nightingale, peter.ni...@york.ac.uk
Application enquiries: cs-pgr-a...@york.ac.uk
Funding Notes
- £17,668 (rate from October 2022) per year stipend
- UK tuition fees
- RTSG (training/consumables/travel) provision.
References
[2] Juho Lauri, Sourav Dutta, Marco Grassia, and Deepak Ajwani, Learning fine-grained search space pruning and heuristics for combinatorial optimization, https://arxiv.org/pdf/2001.01230.pdf
[3] P. Nightingale, Ö. Akgün, I. P. Gent, C. Jefferson, I. Miguel, P. Spracklen, Automatically Improving Constraint Models in Savile Row, Artificial Intelligence, Volume 251, Pages 35-61, 2017.
-- Dr Peter Nightingale Lecturer, Department of Computer Science University of York Disclaimer: https://www.york.ac.uk/about/legal-statements/email-disclaimer/