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current group members

Dhurim Cakiqi
Category theory for causal inference in machine learning.

Omer Eryilmaz
Topological signal processing for wearable devices.

Abdulrahman Aloyayri
Causal machine learning for smartphone-based sensor data.

Xi He
Exact combinatorial machine learning algorithms.

Nawfal Zakar
Causal inference for epidemiological wearable signal processing.

Feifei Zheng
Algorithms for generalized causal machine learning.

Jianqiao Mao
Causal bootstrapping for healthcare data

Yusuf Baykal
Deep learning for wearable cardioanalysis

past group members

Reem Alanazi (Birmingham, 2019-2023)
Reem's research explored the fundamental limitations of supervised classification, discovering several novel, exact algorithms for optimal 0-1 loss classification.

Yazan Qarout (Aston, 2017-2020)
Yazan has developed novel signal processing algorithms for understanding and quantifying unconstrained human behaviours from sensor data.

Adam Farooq (Aston, 2018-2023)
Adam's primary research is concerned with developing fast nonparametric signal processing algorithms using Bayesian nonparametrics. How is now an Assistant Professor in Doha, Qatar.

Ugur Kayas (Aston and Birmingham, 2018-2022)
Ugur's work investigated the application of abstract algebras to simplify complex machine learning algorithms. He is now an Assistant Professor at Abdullah Gul University, Turkey.

Katy Weihrich (Manchester and Aston, 2017-2022)
Katy's work is concerned with extracting clinically meaningful information about pain in musculoskeletal diseases from wrist-worn sensor data.

Alex Oldroyd (Manchester and Aston, 2017-2021)
Alex is investigating the use of wearable devices for the objective characterization of movement impairment in myositis. He is now an NIHR Clinical Lecturer, at the University of Manchester, UK.

Anna Beukenhorst (Manchester and Aston, 2016-2020)
Anna is currently a postdoc at Harvard University, US.

Reham Badawy (Aston and Birmingham, 2014-2018)
Reham has a combined honours background in neuroscience and computer science, and is investigating how to detect and quantify Parkinson's disease prodromally (that is, before the symptoms are readily apparent in clinic), using mass-scale data collected using smartphones.

Jordan Raykov (Aston, 2013-2016)
Jordan's primary research improved the computational tractability of inference in Bayesian nonparametric models such as combinatorial stochastic processes. For example, he developed novel maximum a-posteriori algorithms for Dirichlet process mixtures and infinite HMMs. He is now an Assistant Professor at the University of Nottingham, UK.

Ben Fulcher (Oxford, 2009-2012)
Ben worked on massively systematic approaches to time series analysis. He applied a vast array of analysis algorithms across multidisciplinary time series, discovering empirical structural relationships between seemingly unrelated analysis algorithms and suggesting new analysis methods that cross disciplinary boundaries, among many other innovations. He is now a Senior Lecturer at the University of Sydney, Australia.

Paul Moore (Oxford, 2008-2014)
Paul developed time series analysis methods for characterizing and predicting bipolar disorder, based on a large and unique set of weekly, self-reported symptom data in the form of mobile phone texts. He used methods such as Gaussian process regression and exponential smoothing.

Athanasios Tsanas (Oxford, 2008-2012)
Thanasis's D.Phil. research explored the use of voice recordings to develop objective, automated tools for detecting Parkinson's and predicting symptom severity. Thanasis is now a Professor of Digital Health and Data Science at the University of Edinburgh, UK.