Alexander McCaffrey

I am a PhD candidate at the Artificial Intelligence Research Centre, at City, University of London. I am currently studying how to design artificial agents that use the Free Energy Principle to guide interactions with their environments, under the supervision of Prof. Eduardo Alonso and Dr. Esther Mondragón, with Prof. David Filliat as external supervisor. I am particularly interested in the integration of Deep Learning models with new Reinforcement Learning techniques, Evolutionary Game Theory and the relationship between Artificial Intelligence and Neuroscience.

Before joining the Artificial Intelligence Research Centre, I received an MSc in Artificial Intelligence from City, University of London, and a BSc in Economics from UCL. A common theme in both Artificial Intelligence and Economics is their tendency to incorporate insights for other disciplines, including psychology, biology, philosophy, and neuroscience. For me, being forced to take an interdisciplinary approach in both fields is what makes them interesting.

I worked as an academic tutor and was part of a team at HSBC responsible for collecting and analysing large amounts of data to deliver insights to management. Outside of research, I enjoy teaching, poker and am trying to become qualified diver.

Esther Mulwa

I am PhD candidate at the Artificial Intelligence Research Centre. I am currently working on integrating Associative Learning Models and Deep Neural Networks, under the supervision of Dr. Esther Mondragón and Prof. Eduardo Alonso.

Before joining the PhD Program, I received a MBA and MSc in Data Science from Strathmore Business School and City, University of London, respectively. I have a vast industrial experience spanning over six years in Information System management in both FMCG and Telecommunications sectors. I have worked with several software and products such as MATLAB, Python, Oracle and Workday for both modelling and system management.

I work as a graduate teaching assistant for modules such as Introduction to AI, and Programming and Mathematics for AI, in the Department of Computer Science, City, University of London. Aside from research, I enjoy taking part in in different sports such as hiking, cycling and have participated actively in several marathons that support different charities.

Dr Fatemeh Najibi (Alumna)

Fatima received her PhD in Computer Science from the City University of London in late August 2021. During her PhD, she was a member of CitAI and worked at the interface of power system operations, machine learning and optimization. Her thesis title was “Enhanced power system operation with coordination and forecasting techniques”. Her main research interests are machine learning, optimization, power system and renewable energy.

Fatima is currently a Senior Energy Consultant at Arup, contributing her knowledge to the transitioning to the net-zero emission power system. She is also an artist who paints, takes photos, and writes in her free time.

Before starting her PhD, she worked at the Moheb Niroo consultancy firm in Iran, leading the power system engineering team to analyse the energy industry and market trends and determine their potential impact to support investment decisions and identify new business opportunities in the renewable energy sector.

She has published among others in the International Journal of Electrical Power and Energy Systems, the Proceeding of 12th International UKACC Conference on Control (CONTROL2018), the Proceedings of the IEEE PowerTech 2021 Conference, Energy and Energy Conversion and Management.

Sergio Naval-Marimont

I am a PhD candidate at the Artificial Intelligence Research Centre, CitAI at City, University of London, working on computer vision for medical image analysis under the supervision of Dr. Giacomo Tarroni and Dr. Aidan Slingsby. I work on algorithms to learn medical images representations without using labels (i.e. unsupervised and self-supervised) and with limited labels (semi-supervised) and applications of these algorithms for the detection and segmentation of anomalies and organs. I have a vested interest in problems related to unsupervised learning such as compression, image generation and transfer learning and, given that medical images are generally high-dimensional, I am also researching on data efficient sparse image representations such as point cloud or implicit fields learning.

Before starting my PhD I completed a MSc in Data Science in City, University of London and worked for more than 10 years in consulting supporting businesses on analytics and forecasting projects. In my free time I enjoy reading, cooking, travelling and scuba diving.

I have published my research on unsupervised anomaly detection in conference papers such as IEEE International Symposium on Biomedical Imaging (ISBI 2021) and Medical Image Computing and Computer Assisted Interventions (MICCAI 2021). I also achieved 2nd and 3rd positions in the Medical Out-of-Distribution Challenge held at MICCAI 2020.

Sami Saadaoui

I am an Industrial PhD candidate at the Artificial Intelligence Research Centre, CitAI, at City, University of London, working under the supervision of Prof. Eduardo Alonso and Dr. Alex Ter-Sarkisov. In collaboration with Ai-London and EIT Digital, I will be working on developing artificial intelligence solutions to enable the automatic configuration of efficient next best action advice in scenarios with different long-term financial planning goals and channel preferences in order to close the Advice Gap for Life, Pension & Investments (LP&I).

I have a master's degree in Computer Science Engineering of Distributed Computer Systems from Université du Littoral-Côte-d'Opale, France, and an Engineering degree (BSc+MSc) in Computer Science Parallel and Distributed Systems from Batna University, Algeria, with a solid professional experience in software development, architecture, and modern DevOps solutions.

I work as Head of AI R&D at Ai-London in different projects using different next generation technology stacks to enable Life, Savings and Investment providers to dramatically reduce their operational cost base and increase their strategic flexibility.

Arman Sarjou

I am a PhD student at the Artificial Intelligence Research Centre at City, University of London under the supervision of Prof. Eduardo Alonso and Dr. Dimitra Apostolopoulou. My research focuses on developing frameworks for scalable, risk-aware multi-agent reinforcement learning with applications in energy portfolio optimization. 

Prior to starting my PhD, I received a BSc in Geophysics from Imperial College London before completing my MSc in Data Science at City, University of London. The skillset learned during these studies lay the foundation for me to work as a Data Scientist within Great Britain’s Energy Industry. Strongest using Python, I love building data-driven products with the aim of solving complex problems during our energy transition.

With this in mind, I am interested in developing architectures for multi-agent reinforcement learning problems which seek to incorporate risk management in decision making while maintaining sample efficiency. This is with the goal of developing algorithms that are fit for purpose in real-world, risk sensitive applications.

Outside of work, I enjoy playing sports such as football, snooker or even darts. I believe there is always something to be learned from watching competition at the highest level!

Vasilis Siomos

I am a PhD student at the Artificial Intelligence Research Centre at City, University of London. I am researching how to best leverage Federated Learning techniques for Medical image Analysis, under the supervision of Dr. Giacomo Tarroni and Dr. Christos Tzelepis, with Dr. Jonathan Passerat-Palmbach as a external supervisor.

My research focus is on developing Multi-Task Learning methods that enable Federated Learning models to be personalised to each participant in a federation, be that institutions or end-users. The emergent commodification of personal data is an issue that affects everyone, hence my interest in any and all things related to Federated Learning.

Before embarking on my PhD journey, I received an MSc with distinction from Imperial College London in Artificial Intelligence and Machine Learning; my thesis was on the intersection between Federated and Reinforcement Learning. Prior to that, I studied Electrical and Computer Engineering, with a focus on telecommunications, at the Aristotle University of Thessaloniki, receiving my MEng with honours.

Outside of academia, I’m a passionate drone and landscape photographer, and a traveller.

Sandamali Wickramasinghe (Affiliate)

I'm a PhD student at the Artificial Intelligence Research Center at City, University of London. My research is on verifying AI systems by extracting automata via learning under the supervision of Dr Dr. Jacob Howe and Dr Dr. Laure Daviaud.

I am particularly interested in the fields of machine learning, deep learning, and explainable AI.

Prior to joining the Artificial Intelligence Research Centre, I earned a BSc in statistics from the University of Colombo in Sri Lanka. I have industrial exposure of three years working in data science, machine learning and deep learning. I worked as an associate data scientist at VeracityAI, an AI-based research firm, where I worked on NLP and computer vision projects. Subsequently, I worked for Pickme, the top ride-sharing business in Sri Lanka, as a data scientist. At PickMe, I worked on implementing big data and AI solutions to meet business needs.

I have published my research on handling edge distortion in discrete wavelet transformation in the American Journal of Applied Mathematics and Statistics. Aside from research, I enjoy traveling, hiking and drawing.