Víctor Abia Alonso

Hello! My name is Víctor Abia Alonso and I am a PhD candidate at the Artificial Intelligence Research Centre at City, University of London, under the supervision of Dr. Marc Serramià, and Prof. Eduardo Alonso.

One of the core questions of my research is, "What values should AI align with?" My work focuses on learning value systems within ethical AI frameworks and exploring their applications in policy-making. For this research, I consider how to model values in a way that not only reflects human moral principles but is also practical for an intelligent machine to interpret. My references primarily include papers from multi-agent systems, social choice theory, and the growing field of value alignment. Philosophical and sociological questions frequently arise regarding the nature of values and their applicability across various contexts.

My background is in Maths (BSc Mathematics with Economics at UCL, 2020-23) and I also did the CitAI MSc on Artificial Intelligence (2023-24). I have done research in mechanistic interpretability (a paper in 2023 and code in 2024), and have also won the international SAPIENCE competition on autonomous drones.

I am passionate about learning how we as humans think and I am always eager to share my thoughts about it and happily be corrected when wrong.

Corina Catarau-Cotutiu

I am a PhD candidate at the Artificial Intelligence Research Centre, at City, University of London. I have as supervisors Dr. Esther Mondragón and Prof. Eduardo Alonso.

My research focuses on understanding and developing a model that mimics the inner workings of a creative agent by making use of deep learning architectures with a focus on graph networks. I am interested in the process of functional creativity that will allow us to build more robust agents that generalise easier and use their resources more efficiently. Representation learning is also part of my research as in order for an agent to be creative it needs to posses a knowledge space (concept space) that can be manipulated.

Before this I have worked as a software developer in both Python and Java for 5 years. I obtained my MSc in Artificial Intelligence from City, University of London and have a BSc in Computer Science from the Technical University of Cluj-napoca.

I am passionate about learning how we as humans think and I am always eager to share my thoughts about it and happily be corrected when wrong.

Baris Cekic

I am a PhD candidate at the Artificial Intelligence Research Centre at City, University of London, under the supervision of Dr. Esther Mondragón, and Prof. Eduardo Alonso.

My research focuses on advancing human-like reasoning in AI by combining neural networks, symbolic AI, and reinforcement learning. The goal is to develop novel neural architectures integrated with hierarchical reinforcement learning, drawing inspiration from cognitive science insights on human decision-making and skill acquisition. This approach aims to bridge the gap between current AI capabilities and human-like reasoning, potentially leading to more robust, interpretable, and adaptable AI systems.

Prior to my PhD, I accumulated 20 years of experience in the B2B Software industry. I worked for renowned companies such as SAP and Palantir. For the past 6 years, I've been an entrepreneur, founding two startups: DISKOVER and DATAZONE. These companies primarily focus on Big Data and AI Infrastructure solutions for enterprise customers.

I hold an MSc in Artificial Intelligence from City, University of London and a BSc in Industrial Engineering from Istanbul Technical University. My extensive background in both academia and industry provides me with a unique perspective on the practical applications and theoretical foundations of AI.

I am passionate about pushing the boundaries of AI and always eager to engage in discussions about the future of technology and its impact on society.

Abraham Chakawa

I am a PhD candidate at the Artificial Intelligence Research Centre at City, University of London. I have as supervisors Dr. Esther Mondragón, and Dr. Daniel Chicharro.

My research focuses on developing causally informed models capable of predicting the intervention of separate causal mechanisms and capable of reproducing invariance across domains associated with these mechanisms. This will involve inferring the causation structure of complex systems from data and predicting the effects of targeted interventions applied in medicine and climate systems. The extraction of knowledge from the latent independence distinctive of the mechanisms underlying the data and the utilisation of neural networks and deep learning architectures to produce representations that capture these independencies are also part of this research.

Prior to starting my PhD, I worked as a part-time Statistics lecturer at the National University of Science and Technology, Zimbabwe. I obtained my MSc in Operations Research and Statistics from the National University of Science and Technology, Zimbabwe, and then later obtained an MSc in Mathematical Sciences from Stellenbosch University (The African Institute for Mathematical Sciences (AIMS)), South Africa.

I am passionate about developing causally informed models that accurately reflect reality, which will go a long way towards improving AI models.

Alexander Dean

I am a PhD candidate at City, University of London’s Artificial Intelligence Research Centre. My research is on the study of algebraic structures in continual representation learning, under the supervision of Dr. Esther Mondragón, , Prof. Eduardo Alonso, and Dr. Laure Daviaud.

Before starting my PhD, I received an MSci in Physics with Theoretical Physics from Imperial College London then studied for the MSc in Artificial Intelligence at City, University of London.

I am a strong advocate of developing mathematical frameworks to gain insights into problems and my research aims to apply ideas and techniques from Physics to Artificial General Intelligence; I’m also interested in the analysis of variational principles and their symmetries using abstract algebra to develop novel AI techniques and inspire new cognitive architectures.

Azad Deihim (Affiliate)

I am a PhD candidate affiliated to City, University of London’s Artificial Intelligence Research Centre, under the supervision of Dr. Dimitra Apostolopoulou and Prof. Eduardo Alonso.

My research focuses on multivariate time series forecasting using deep learning, namely, exploring novel Transformer-based architectures and embedding implementations to exploit spatio-temporality of a multivariate time series. I am also researching optimization of power systems using deep learning; currently, I am investigating deep reinforcement learning solutions to AC optimal power flow, in an attempt to minimize monetary costs of power generation.

Prior to becoming a PhD student, I earned a BSc in Computer Engineering from Wentworth Institute of Technology and a MSc in Artificial Intelligence from City, University of London.

For leisure, I enjoy playing various card games and strategy games, traveling, and skiing.

Abdul-Basit Hafeez

I am an Industrial Doctoral candidate at the Artificial Intelligence Research Centre, CitAI, at City, University of London working under the supervision of Prof. Eduardo Alonso and and Dr. Atif Riaz. In collaboration with EIT digital and Bosch group, I will be working on developing AI-enabled algorithms for predictive maintenance of connected vehicles. I have two master's degrees in Machine Learning and Data Engineering, with professional experience in bringing machine learning to production in cloud and big data settings.

I am working on algorithms related to sequential prediction and textual representation including embeddings, RNNs, LSTMs, Autoencoders and Attention mechanisms. Besides predictive maintenance, I am also interested in the application of these algorithms in NLP and Recommendation Systems.

I am passionate about software development and in my leisure time, I enjoy reading and travelling.

Vince Jankovics

I am a PhD candidate at the Artificial Intelligence Research Centre. I am working on representations that allow higher level reasoning and decision making for model-based reinforcement learning agents, under the supervision of Prof. Eduardo Alonso and Dr. Michaël Garcia-Ortiz.

I received my MSc degree in Robotics from the University of Bristol and my BSc in Mechatronics from the University of Southern Denmark. I worked as a machine learning engineer for 5 years, contributing to projects from various domains, such as image recognition, image enhancement, financial time series prediction and drug discovery.

I am interested in abstract reasoning that involves the creation of higher-level concepts that is crucial for achieving AGI. In my free time I enjoy skiing, growing vegetables and 3D printing.

Mpagi Kironde

I am a PhD candidate at the Artificial Intelligence Research Centre, CitAI, at City, University of London, working under the supervision of Dr. Esther Mondragón and Prof. Eduardo Alonso.

My research is focused on how we can integrate associative learning principles in deep learning (DL) models to make models that can generalise better and more robustly. Learning phenomena involves formation of complex associations, DL provides a natural framework to accommodate the representation of stimulus and associations. An important part of learning is how to represent and reuse knowledge, I will explore how we can use Convolution neural networks (CNN), a deep hierarchical architecture, to learn representations of stimuli at different levels of abstraction. Associative memory is another area of my research, how a given stimuli is stored and associated with related stimuli for retrieval later. I will explore how we can use deep associative neural architectures with their hierarchical structure for representation of associative memory. I will also be researching attention in learning theory and how to integrate it in DL models, inspired by the recent success of the Transformers architecture.

I obtained my MSc in Artificial Intelligence from City, University of London and have a BSc Hons in Computing science from University of Greenwich, London. Prior to embarking on my research journey, I worked as a Solution architect at Rank group, designing enterprise applications for the gaming platform. Before joining Rank Group, I worked as a tech lead at Bank of England, leading a team of developers to build an online platform for rules that regulate the financial services.

Nils Olof Lindgren Hallerfelt

As a PhD student at the Artificial Intelligence Research Centre at City St George’s, University of London, my research focuses on deep unsupervised learning methods with a special focus on medical imaging. This research is conducted under the supervision of Dr Giacomo Tarroni and external guidance from Dr Carlo Biffi (Cosmo IMD).

I am particularly interested in developing novel self-supervised learning methods in computer vision that can efficiently extend from the natural image domain into the medical image domain, helping to bridge the gap between these two areas. My research addresses several complexities present in medical imaging, driving advancements in computer vision that could significantly impact healthcare and diagnostics. Some of these challenges include lower semantic information, imbalanced data, and the high dimensionality of medical images, where small but crucial features must be identified and accurately interpreted amidst a large amount of less relevant information.

Before the start of my PhD, I completed an MSc in Engineering Mathematics at Lund University (Sweden), with a particular focus on signal processing, control, and learning algorithms.