Predictive Analytics Lab

We undertake high quality Machine Learning / Computer Vision research, and provide support to a new generation of technology companies.

About Us

Ranked #2

South East England & London Area

Ranked #19


Ranked #109


19 Publications

In top conferences, such as NIPS & ICML

Founded on

Tuesday, Feb 7, 2017 at 7:55 AM

University of Sussex

Part of The Department of Informatics

Rankings are based on CSRankings for Machine Learning. CSRankings is a metrics-based ranking of top computer science institutions around the world. Rankings are compiled from the number of computer science publications presented at the most prestigious publication venues.


Ethical Machine Learning

Ethical Machine Learning

Injecting Ethical and Legal Constraints into Machine Learning Models

Equipping ML models with ethical and legal constraints is a serious issue as without this the future of ML is at risk. In the UK, this is recognized by the House of Commons Science and Technology Committee, which has formed a Council of Data Ethics.

Building ML models with fairness, confidentiality, and transparency constraints is an active research area, and disjoint frameworks are available for addressing each constraint. However, how to put them all together is not obvious. Our long-term goal is to develop an ML framework with plug-and-play constraints that is able to handle any of the mentioned constraints, their combinations, and also new constraints that might be stipulated in the future.

Causal Machine Learning


Causal machine learning for development data

Surgo Foundation is launching the Surgo Machine Learning Initiative for Precision Public Health to explore the feasibility of applying causal machine learning methods to international development data. Surgo has formed a strong and diverse consortium of partners across the private and non-profit sectors including the Bill and Melinda Gates Foundation (BMGF), GNS Healthcare, the University of Manitoba, and the University of Sussex.

In its first proof-of-concept project, ML4PxP will begin by testing several potential causal machine learning approaches on reproductive, maternal, and child health data sets from Uttar Pradesh, India. Together, the consortium is innovating to determine whether and how such models can be applied to help solve big international development questions.

Satellite Image Segmentation

Satellite Image Segmentation

Understanding trade-offs between SDG's in urbanising contexts

Rapid urbanisation creates trade-offs between development, food security and poverty alleviation goals which are often ignored or invisible. Revealing and communicating the nature and scale of these trade-offs to policymakers is a key step towards achieving SDGs around urban sustainability and resilience.

Our project applies deep learning techniques to map peri-urban agriculture in Wuhan, China and explores ways of integrating multiple types of data through a web-based mapping and visualisation tool to support research and stakeholder engagement on urban sustainability policy. This is a cross-departmental project involving several other departments from the University of Sussex.

The Team

The PAL laboratory was co-founded by Novi Quadrianto and Jeremy Reffin. We have a team of 10 members consisting of faculty, research fellows, research associates and PhD students.

Bradley Butcher

David Spence

Thomas Kehrenberg

Viktoria Sharmanska

Joseph Taylor

Zexun Chen

Oliver Thomas

Baris Eray

Novi Quadrianto

Jeremy Reffin


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