"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
"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."
I am a PhD student 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 the development of self-learning platforms for predicting automotive part failures. The goal of my research is to build a predictive model of faults by retrieving data from various sources, including workshop technician reports, vehicle data, and warranty information. I am the holder of an Industrial Scholarship funded by Bosch ASS Ltd.
Prior to starting my PhD, I worked as a Research Associate at COMSATS University Islamabad in Pakistan for three years. During my time there, I published several research articles in impactful journals and presented my work at various national and international conferences.
As an enthusiastic researcher, I believe that technology can help solve real-world problems and make the world a better place. I am passionate about using my skills to develop intelligent systems that can assist people.
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.
I am a PhD student 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.
I am a PhD candidate at the Artificial Intelligence Research Centre at City, University of London. I am supervised by Prof. Eduardo Alonso and Dr. Esther Mondragón. for my topic of: Episodic Memory and Emotions in Artificial Systems and their Applications.
I am particularly interested in investigating whether the presence of personal experience could provide means for the development of a personality in an artificial agent. Also determining if integrating emotions into these personal experiences could provide a stronger framework on which novel experiences could be met with an emotional response.
Before beginning my PhD, I completed a BSc in Computer Science at City and worked as a software engineer in a technology startup for a year. I have experience in a number of technologies, including: Python, PyTorch, Neo4j, spaCy, AWS, and more. In my free time I enjoy reading and gardening.
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, Dr. Laure Daviaud, Prof. Eduardo Alonso, and Dr. Michaël Garcia-Ortiz.
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.
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.
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.
I am a mathematical physics PhD candidate at City's Artificial Intelligence Research Centre working under the supervision of Prof. Yang-Hui He. My research focus is applying data science and machine learning techniques to study data objects in string theory.
Some examples of projects include; using Siamese neural networks to classify 5-dimensional superconformal field theories via brane webs, using generative adversarial networks to generate Calabi-Yau manifolds from reflexive polytopes, and detecting walls of marginal stability in moduli space using support vector machines.
As well as using supervised learning algorithms to perform difficult computations in high energy physics, I am also interested in applying unsupervised learning techniques to study the structure of mathematical data and formulate conjectures.
I am a doctoral researcher in the Mathematics department at City's Artificial Intelligence Research Centre under the supervision of Prof. Yang-Hui He, and a visiting researcher at London Institute of Mathematical Sciences. I study the application of data science and machine learning methods to abstract databases arising in relation to string and gauge theories in high-energy physics.
My current research has focus relating to the Calabi-Yau landscape and the relevant algebraic geometry via the toric construction. Additionally I am interested in generating and analysing databases relevant to quiver gauge theories, including the cluster algebra mutation process fundamental to Seiberg duality between theories.
I also have further interest in the application of physical ideas from geometry, topology, and symmetry to the study of data.
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.
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.
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