"Warning, in music-words
devout and large,
that we are each other’s
we are each other’s
we are each other’s
magnitude and bond."

Gwendolyn Brooks, 1971

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Core Members

"The source of these problems of coordination and cooperation is not the nature of the individuals’ goals, or the instrumental character of rationality. Rather it is individualism about rationality, which holds the unit of activity exogenously fixed at the individual."
Hurley 2004

Eduardo Alonso

sample-image I am the Director of the Artificial Intelligence Research Centre (CitAI) and a Reader in Computing at the Department of Computer Science, City, University of London. I study relationships between reinforcement learning and optimization techniques, associative learning, and evolutionary models of aposematism and foraging. I have a keen interest in exploring variational principles and symmetries in learning and behaviour.

More generally, I am interested in applications of AI technology to real-life problems, such as solutions for energy, health, and biochemistry, and in the ethical and legal impact of deep learning, and in investigating whether AI algorithms can produce creative art.

I have published dozens of papers in journals like Neural Computation, Neural Networks, IEEE TNN&LS, and have contributed to The Cambridge Handbook of Artificial Intelligence and several Springer's LNAI and LNCS volumes. My work has been spotlighted by the IEEE Computational Intelligence Society as one of the most important contributions in the area in 2013, and was awarded the First Prize of the European Institute of Innovation and Technology ICT 2014.

I have acted as vice-chair of The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB), the eldest learned Artificial Intelligence society, and am a member of the EPSRC College.

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Esther Mondragón

sample-image I am a computational cognitive neuroscientist working in nature-inspired AI and the Director of the MSc in Artificial Intelligence. My main research focusses on modelling associative learning (AL), and in the conceptual and formal modifications in learning theory that would allow incorporating phenomena that at face value are not susceptible to associative analysis. I have developed several computational learning models such as the Double error Dynamic Asymptote (DDA) model, a fully-connected architecture for Pavlovian conditioning with a dynamic asymptote, which determines the direction of learning, and the Rescorla-Wagner Drift-Diffusion Model (RWDDM) that combines a noisy linear accumulator and the Rescorla-Wagner learning rule.

My current research centres on integrating deep learning architectures and associative learning to explore the possible contribution of associative processes to the formation of representational hierarchies, as a first necessary step towards scaling up to higher-order cognition. AL is core to bottom-up approaches to natural intelligence and thus keystone to the development of AI algorithms and architectures meant to simulate human-like behaviour.

I have published in presetigious journals such as Psychological Review, Trends in Cognitive Science, PloS Computational Biology, and Science.

I am a senior member of The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB)

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Laure Daviaud

sample-image I am a lecturer at City, University of London and member of CitAI. Prior to this, I had the luck to work with amazing researchers: I did my PhD at the University Paris VII under the supervision of Thomas Colcombet and Jean-Eric Pin, and spent a few years as a post-doctoral research fellow in Aix-Marseille University (with Pierre-Alain Reynier and Jean-Marc Talbot), ENS Lyon (with Colin Riba), University of Warsaw (with Mikolaj Bojanczyk) and the University of Warwick (with Marcin Jurdzinski and Ranko Lazic).

I am studying mathematical abstractions for verification problems, such as automata, games and logic formalisms. This way, I try to understand the limitations of computational models, how powerful they are, how fast …

I have studied more precisely optimization models (max-plus automata and tropical algebra), transducers and games (parity and mean-payoff) for example.

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Alex Ter-Sarkisov

sample-image I am lecturer in the Computer Science Department at City, University of London.

My recent research is mostly on COVID-19 prediction and lesion detection in chest CT scans. To this extent, I fuse image classification with instance segmentation algorithms like Mask R-CNN. I like teaching artificial intelligence, both at undergraduate and advanced levels (Convolutional Neural Networks, Generative Adversarial Networks, image segmentation, object detection, etc).

I obtained my PhD at Massey University, New Zealand, in 2012.

I worked as a postdoctoral researcher at the University of Waterloo (2012–2013), Universite du Maine (2014–2015), and Dublin Institute of Technology (2015 – 2018).

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Michaël Garcia-Ortiz

sample-image I obtained my PhD from Bielefeld University in 2013. The doctoral work, on the topic of ‘Prediction of Driver Behavior’, was performed in collaboration with Honda Research Institute Europe, and involved the prediction of driver behaviors for Advanced Driving Assistant Systems. This work was followed by a PostDoc in Ensta ParisTech, on the topic of pedestrian detection and tracking. I joined Softbank Robotics Europe (formerly Aldebaram), a Paris based robotics company, in September 2013.

For more than 5 years, I was a Research Scientist in SBRE’s AI Lab, where I conducted research on the topic of Artificial General Intelligence. I participated there in the European Project APRIL, focused on learning for personal robotics.

I joined City in July 2019 as a Lecturer for the new Masters in AI. I am a member of the new Artificial Intelligence Research Center CitAI, where I continue my research on AGI and the emergence of common sense knowledge.

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Giacomo Tarroni

sample-image I have been a full-time researcher in the field of medical image analysis since 2009. My work has been mainly focused on image segmentation, image registration, quality control and object tracking for cardiovascular, brain and fetal images. In particular, I obtained my Ph.D. from the University of Bologna, Italy (in collaboration with the University of Chicago, U.S.) working on the automated analysis of first-pass myocardial perfusion sequences in MRI. During my post-doc at the University of Padova, Italy, I focused on the automated analysis of fetal ultrasound images. After being awarded a Marie Skłodowska-Curie Fellowship from the European Commission, I moved to Imperial College London, where I became interested in the applications of machine learning and AI to automated organ detection, quality control assessment and motion correction for cardiac MRI.

My current research focus is on machine learning for unsupervised anomaly detection, self-supervised approaches to image classification/segmentation and federated learning, both for medical image analysis and more generally for computer vision applications.

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Daniel Chicharro Raventós

sample-image I am a lecturer in the Computer Science Department at City and a member of the Artificial Intelligence Research Centre (CitAI). Previously, I was a postdoctoral researcher at Harvard Medical School in the Neurobiology Lab of Dr. John Assad and a postdoctoral researcher at Italian Institute of Technology in the Neural Computation Lab of Dr. Stefano Panzeri. I obtained my PhD in Information and Communication Technologies, with specialization on data analysis for neuroscience, at the University Pompeu Fabra, Barcelona, under the supervision of Dr. Ralph G. Andrzejak.

The core of my research has been the development of machine learning and statistical methods to infer the representation of informative features in high-dimensional data, and the application of these methods to understand how the brain represents and processes sensory stimuli and behavioral decisions in the activity of populations of neurons.

More broadly, I am mostly interested in understanding how the causal structure and generative mechanisms of complex systems can be learned from data and exploited to create useful representations of high-dimensional data that are more robust and generalizable across domains. Currently, I am studying how the structure of causal mechanisms is reflected in distinctive sets of independencies between different sources of variability in a system, as well as in its invariance properties. I then exploit these causal trademarks for statistical modeling and to infer the effect of external perturbations or modifications of those mechanisms. The objective of this research is to better characterize complex real-life systems (e.g. neuroscience, genomics) and to improve data representations with causal inference methods also for technological applications.


Atif Riaz

sample-image I am a lecturer in Computer Science at City, University of London and a member of CitAI. Currently, I am exploring deep learning models for the classification of Major Depressive Disorder with Eduardo Alonso and Esther Mondragón.

My Ph.D. topic was in the domain of Artificial Intelligence and neuroimaging under the supervision of Eduardo Alonso and Greg Slabaugh. I have explored different machine learning and deep learning methods for the diagnosis of Attention Deficit Hyperactive Disorder (ADHD) using functional MRI (fMRI) data. I am interested in exploring Artificial Intelligence in the domain of neuroimaging to better understand the most complex human organ –the brain, and to better diagnose brain disorders—using Deep learning algorithms and architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), auto-encoders, and Generative Adversarial Networks (GANs).

I have published my research in a number of journal and conference papers such as Journal of Neuroscience Methods, Computerized Medical Imaging and Graphics, IEEE International Symposium on Biomedical Imaging (ISBI 2018), International Workshop on Connectomics in NeuroImaging (CNI 2017), and International Conference on Image Analysis and Recognition (ICIAR 2016).

Before joining the City, University of London, I completed a MSc at the National University of Sciences and Technology (NUST), Pakistan.


Andrew Ryzhikov

sample-image I am a postdoctoral research associate at City, University of London, working with Laure Daviaud on weighted (max-plus and cost register) automata. These are abstractions used for modeling and analysing quantitative behaviour of systems that perform some computations. I am interested in automata theory, especially its combinatorial and algorithmic aspects, as well as its applications to coding theory, program analysis, and verification. More generally, I enjoy learning about computational and extremal questions in theoretical computer science.

I got my PhD in computer science (supervised by Dominique Perrin) in November 2020 from LIGM, University Paris-Est on the topic of synchronizing automata and synchronizing codes. The idea is that for certain families of systems one can regain control after an occurrence of an error by applying a specific sequence of already existing commands, without adding a special "reset" command. This concept is a source of both deep theoretical questions and nice practical applications.

Before that, I got my B.Sc. in computer science from Belarusian State University and an M.Sc. in applied mathematics from University Grenoble-Alpes.