Potential JRA Projects
This list will be updated as we receive project titles from Schools.
Please note that this list is not comprehensive, so speak to your tutor or School office if you're interested in a JRA project that isn't listed.
- Business, Management and Economics
-
Awaiting project information - please check back later
- Education and Social Work
-
Awaiting project information - please check back later
- Engineering and Informatics
-
Project Title: AI-Based Task Offloading In Vehicular Edge Computing
Supervisor: Naercio Magaia
Email: N.Magaia@sussex.ac.uk
Description: With the benefit of partially or entirely offloading computations to the cloud, or to a nearby server or nearby opportunistic computing resources (e.g., onboard computers of parked vehicles), mobile edge computing gives vehicles more powerful capability to run computationally intensive applications (e.g., Augmented Reality). However, a critical challenge emerged: how to select the optimal set of components to offload considering the vehicle's performance, its resource constraints and offloading costs.
This project focuses on developing and evaluating AI algorithms for optimising MEC offloading decisions. Deep Learning and other alternative techniques will be explored to implement integrated solutions. Evaluation of the developed algorithms will be based on computer simulation.
Project Title: Machine-Learned Network Congestion Control
Supervisor: George Parisis
Email: G.Parisis@sussex.ac.uk
Multiple users accessing a network must share available resources - bandwidth and buffers. Network congestion is a network state characterised by increased network delay and packet loss rate, because of traffic going through one or more bottleneck links where the required bandwidth exceeds the available one. Network congestion results in severe degradation of users’ quality of experience and must therefore be controlled. Congestion control involves end-hosts, and potentially in-network devices, and aims to maximise resource utilisation while fairly allocating resources among all users. This is commonly done on an end-to-end basis by regulating senders’ transmission rate. Recently, a new learning-based congestion control paradigm has gained traction, with the key argument being that congestion signals and control actions are too complex for humans to interpret and that machine-generated algorithms can provide superior policies compared to human-derived ones. An objective function then guides the learning of the control strategy. Early work in this thread included off-line optimisation of a fixed rule table [1] and online gradient ascent optimisation [2], with later work adopting sequential decision-making optimisation via reinforcement learning (RL) algorithms [3, 4].
RL-based congestion control is still in its infancy and substantial research is required to yield deployable algorithms and respective RL policies. In [5] we have shown that existing approaches fall short when it comes to fairness, a fundamental requirement of congestion control. In this project, we will further explore the concept of fairness in RL-based congestion control through experimentation in both emulated [5] and simulated networks [6]. We will specifically experiment using RayNet [6] a simulation framework that we have developed; RayNet integrates state of the art software in packet level simulations (OMNeT++) and unified computing and RL (Ray/RLlib). We will consider novel approaches in fairness by integrating it as an explicit component of the reward during training; e.g., by employing centralised training or, even, during regular operation by generating fairness signals through in-network telemetry.
[1] Keith Winstein and Hari Balakrishnan. 2013. TCP ex Machina: Computer-generated congestion control. ACM SIGCOMM Computer Communication Review 43, 4 (2013), 123–134.
[2] Mo Dong, Tong Meng, Doron Zarchy, Engin Arslan, Yossi Gilad, Brighten Godfrey, and Michael Schapira. 2018. PCC Vivace:Online-Learning Congestion Control. In Proceedings of USENIX NSDI. 343–356.
[3] Soheil Abbasloo, Chen-Yu Yen, and H Jonathan Chao. 2020. Classic meets modern: A pragmatic learning-based congestion control for the internet. In Proceedings of ACM SIGCOMM. 632–647.
[4] Nathan Jay, Noga Rotman, Brighten Godfrey, Michael Schapira, and Aviv Tamar. 2019. A deep reinforcement learning perspective on Internet congestion control. In Proceedings of ICML. 3050–3059.
[5] L. Giacomoni and G. Parisis, Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness, in Proc. of IEEE INFOCOM , 2024 (accepted).
[6] L.Giacomoni,B.Benny,andG.Parisis,“RayNet:Asimulationplatform for developing reinforcement learning-driven network protocols,” CoRR, vol. abs/2302.04519, 2023.
Project Title: Speech denoising with event-based (spiking) neural networks
Supervisors: Prof Thomas Nowotny & Dr James Knight
Mentor: Mr Tom Shoesmith
Email: T.Nowotny@sussex.ac.uk
In our group, we are working on a project with Intel to develop algorithms using event-based (spiking) neural networks for denoising speech data. Speech denoising is the removal of noise from speech recordings or transmissions and is a key technology in many areas of telecommunications. Intel has recently published a speech denoising challenge [1] and, in our project, we aim to train event-based (spiking) neural networks which can be eventually deployed on the Intel Loihi 2 neuromorphic system [2]. As a JRA you would participate in this research, working on speech pre-processing and exploring aspects of the models in the mlGeNN framework [3].
1. Timcheck, J., Shrestha, S.B., Rubin, D.B.D., Kupryjanow, A., Orchard, G., Pindor, L., Shea, T. and Davies, M., 2023. The intel neuromorphic DNS challenge. Neuromorphic Computing and Engineering, 3(3), p.034005.
2. Davies, M., Srinivasa, N., Lin, T.H., Chinya, G., Cao, Y., Choday, S.H., Dimou, G., Joshi, P., Imam, N., Jain, S. and Liao, Y., 2018. Loihi: A neuromorphic manycore processor with on-chip learning. Ieee Micro, 38(1), pp.82-99.
3. Turner, J.P., Knight, J.C., Subramanian, A. and Nowotny, T., 2022. mlGeNN: accelerating SNN inference using GPU-enabled neural networks. Neuromorphic Computing and Engineering, 2(2), p.024002.
4. https://github.com/genn-team/ml_genn, accessed 16/02/2024
- Global Studies
-
Awaiting project information - please check back later
- Law, Politics and Sociology
-
Awaiting project information - please check back later
- Life Sciences
-
Awaiting project information - please check back later
- Mathematical and Physical Sciences
-
Awaiting project information - please check back later
- Media, Arts and Humanities
-
Project Title: Digital Holocaust Memory Innovation Lab
Supervisors: Dr Victoria Grace Walden
Mentor: Mr Tom Shoesmith
Email: V.Walden@sussex.ac.uk
The newly launched Digital Holocaust Memory Innovation Lab at the University of Sussex is offering an opportunity for junior researchers to explore the data collated to form its ‘living database’. The database contains walkthroughs of digital projects created by Holocaust memorials and museums across Europe, the US and Australia, complemented by interviews with those involved in creating them.
The database sheds light on the practical, ethical and financial challenges and opportunities that inform the rapidly developing digital Holocaust memoryscape. Once launched, it will offer rich opportunities for analysis and learning from practice to address the urgent issue of the sustainability of Holocaust memory in the digital future.
The Lab offers the opportunity for junior researchers to explore the existing data from either a humanities or computer sciences perspective and to develop original research, with the support of a supervisor, based on a research question of their choosing.
This is a unique opportunity to participate as part of a research team at the beginning of their inquiry into a new dataset, contributing to groundbreaking research in the field of digital Holocaust memory studies.
Key Reading:
Blanke, T et al. 2020. Understanding Memories of the Holocaust – A New Approach to Neural Networks in the Digital Humanities, Digital Scholarship in the Humanities 35(1), 17-33
Cole, T. and T. Hahmann. 2019. Geographies of the Holocaust: Experiments in GIS, QSR, and Graph Representations, International Journal of Humanities and Arts Computing 13(1-2), 39-52
de Leeuw, D. 2018. Digital Methods in Holocaust Studies: The European Holocaust Research Infrastructure, IEEE 15TH International Conference on e-science, 58-66
Ebbrecht-Hartmann, T. 2020. Commemoration from a Distance: The Digital Transformation of Holocaust Memory in Times of Covid-19, Media, Culture & Society 43(6), 1095-112.
Ebbrecht-Hartmann, T., N. Stiassny and L. Henig. 2023. Digital Visual History: Historiographic Curation using Digital Technologies’, Rethinking History
Frosh, P. 2018. The Mouse, The Screen and the Holocaust Witness: Interface Aesthetics and Moral Response, New Media & Society 20(1), 351-68
Knowles, A.K et al. 2020. Integrative ,Interdisciplinary Database Design for the Spatial Humanities: The Case of the Holocaust Ghettos Project’, International Journal of Humanities and Arts Computing 14 (1-2), 64-80
Pfanzelter, E. 2015. At the Crossroads with Public History: Mediating the Holocaust on the Internet, Holocaust Studies: A Journal of Culture and History 21(4), 250-71.
Presner, T. 2016. The Ethics of the Algorithm: Close and Distant Listening in the Shoah Foundation Visual History Archive’, in Probing the Ethics of Holocaust Culture, eds. C. Fogu, W. Kansteiner and T. Presner. Harvard University Press.
Walden, V.G. (Eds.). 2021. Digital Holocaust Memory, Education and Research. Palgrave Macmillan
- Psychology
-
Awaiting project information - please check back later
- Brighton and Sussex Medical School
-
Awaiting project information - please check back later