Marco graduated from the University of Bologna with a Bachelor’s degree in Physics in 2014, and with a Master’s Degree in Nuclear and Subnuclear Physics in 2018. After high school graduation, he worked a two weeks internship at CERN, Geneva. Under the supervision of Dr Carla Sbarra, Marco learned to analyse calibration data of LHC detectors. During his Master’s Degree, he won an INFN-MAE scholarship to study CMOS pixel detectors at the ATLAS Pixel Laboratory at CERN.

There, in a group supervised by Dr Heinz Pernegger, Marco measured and reconstructed the trajectories of particles hitting a silicon detector. During his master thesis, which took place also at CERN, he further studied the problem of track reconstruction inside a pixel detector, optimising the code of the currently used software. The work of his thesis featured in four scientific publications. After getting his Master’s Degree, he began to study Machine and Deep Learning techniques, to apply them to the work of his master thesis.

In January 2019, he won a scholarship from the Department of Informatics, Science, and Engineering of the University of Bologna to study this particular problem. Besides studying the application of deep learning techniques to particle physics, he also worked on large-scale automated identification of cells in confocal light-sheet microscopy images by using Deep Learning and convolutional neural networks.

Currently, Marco is working on Machine Learning techniques applied to electronic quantum matter imaging experiments.

Supervisors: Professor Barry O’Sullivan

Victor Coscrato is a Master in statistics by UFSCar/USP and Bachelor in Statistics byUFSCar. He was born in São Paulo. During his BSc he conducted his first scientific research on agnostic statistical inference. During this time, he was a volunteer member in both the tutorial education program in statistics (PET-EST/UFSCar) and the Risk study centre (CER/UFSCar). His BSc graduation work consisted of a comparative study between word2vec and bag-of-words on natural language processing classification applications. In the last period of his BSc, Victor worked as an intern in people analytics for Stone Company in São Paulo. His job was to create automatic mechanisms to accelerate and improve quality of the company’s employee hiring process. During his MSc jorney Victor discovered his passion in machine learning, which became his main research field since then, leading him to join the statistical machine learning lab (SMaLL/UFSCar). During this time, he wrote two method proposal papers on neural networks usage on regression problems: the first method consisting in a neural network regression ensemble and the latter in a novel interface to local linear smoothing. The MSc dissertation was composed by these two papers combined in a fashion to expose how they connect to each other though. Currently, Victor is interested in general machine learning and artificial intelligence problems, more specifically, he is interested in recommender systems and machine learning models interpretations / prediction explanations. Nevertheless, non-parametric and agnostic statistical inference are still attractive fields for him.

Supervisors: Dr Derek Bridge

Sergi Gómez Quintana has a passion for engineering, music and sound. He is from Barcelona (Spain), where he received his Master’s degree in Telecommunications from the Universitat Politècnica de Catalunya. He also worked in several jobs and personal projects related to music, sound and technology.

His background as an engineer and musician place him into a unique position where he can contribute with both analytical and creative solutions. Over the past 12 months he has been conducting research in UCC with the INFANT Centre and Embedded Systems Group. This research was jointly funded by the Wellcome Trust and Grand Challenges Canada Seed Awards. His research is in the areas of AI-based analysis of heart sounds and sound-based analysis of brain. He has developed a novel signal processing algorithm to convert neonatal brainwaves to sound that has shown a high level of achieved accuracy. This resulted in two published conference papers in leading engineering (IEEE) and clinical domains (Paediatric Societies), including a best paper award at IEEE ISSC.

Up to now, he has attended numerous conferences, seminars and events related to engineering and neurophysiology, including the International Conference on Brain Monitoring and Neuroprotection in the Newborn (BMNN), the IEEE Irish Signals and Systems Conference (ISSC) and the European Academy of Paediatric Societies Congress (EAPS). Additionally, he has participated as a volunteer in several outreach events such as the BT Young Scientist & Technology Exhibition and the European Researchers’ Night.
“Being part of CRT-AI gives me a great opportunity to use my expertise developing the ML foundations for the next generation of medical devices and decision support tools that can save or improve lives.”

Supervisors: Dr Emanuel Popovici & Professor Geraldine Boylan

I am Colombian, currently living in the city of Medellín. In this same city, I graduated as an Electronic Engineer and later obtained a master’s degree in Engineering with an emphasis on the development of information systems, both grades coursed at the University of Antioquia. For some years I taught university introductory courses in Programming and Computer Architecture, first in Colombia and then in Argentina. At the same time, I was developing my master’s thesis.

I also had been working on enterprises related to banking and insurance services mainly doing software test automation. Thanks to this last work I know a little about the current use of cloud computing services (mainly AWS), Docker and Kubernetes. In my research work I studied the use of high-performance computing and hybrid metaheuristics to solve combinatorial optimization problems.

I am currently working with Dr. Laura Climent in the use of Constraint Programming and Metaheuristics to solve Combinatorial Optimization Problems arising from the context of smart cities and uncertain environments. On the personal level, when time is enough, I really enjoy doing
long bike rides and practicing a little Yoga. I am always interested in discovering new music and I am definitely passionate about the cinema, although I always have an outstanding debt with it.

Supervisors: Professor Barry O’Sullivan & Dr. Laura Climent

Hello everyone, I am a PhD student under the supervision of Prof. Barry O’Sullivan and am currently working on Ethics in AI. My interests include Counterfactual explanations in AI, NLP, configuration systems, constraint programming, recommender systems etc. Some of my published work includes
1. Dev Gupta, S., Genc, B. and O’Sullivan, B. (2021) ‘Explanation in Constraint Satisfaction: A Survey’, JCAI 2021: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada. Virtual Event, 17-27 August, pp. 4400-4407. doi:10.24963/ijcai.2021/601
2. Finding Counterfactual Explanations through Constraint Relaxations, Sharmi Dev Gupta, Beg¨um Genc, Barry O’Sullivan. Explainable Agency in Artificial Intelligence Workshop 36th AAAI Conference on Artificial Intelligence, February 28-March 1 2022.
3. Rathee, G., Balasaraswathi, M., Chandran, K. P., Gupta, S. D., & Boopathi, C. S. (2021). A secure IoT sensors communication in industry 4.0 using blockchain technology. Journal of Ambient Intelligence and Humanized Computing, 12(1), 533-545.
4. A Semantic Search Pipeline for Causality-driven Adhoc Information Retrieval, Dhairya Dalal, Sharmi Dev Gupta and Bentolhoda Binaei. Causality-driven Adhoc Information Retrieval (CAIR-2021) shared task.
I am currently working towards building a user-friendly system for providing counterfactual explanation and maximally relaxed solutions for constraint satisfaction problems. I am interested in learning and working in multiple domains in Artificial Intelligence. Apart from academics I am interested in learning martial arts and am currently learning various self-defence techniques, I would also like to travel all across the world and experience different cultures.

Supervisors: Barry O’Sullivan

I am Filipe Souza, and I previously worked for seven years in an insurance company in Brazil dealing with large and complex datasets. In the last year, I graduated from a full-time MSc in AI with a successful first honours degree and the overall grade of 86.4%. During the masters, I worked on a novel hybrid algorithm that combined a dedicated genetic algorithm with a local search for solution refinement to solve 2D-BBP. After delivering my thesis, I also wrote a paper about this work and presented it in the 28th Irish Conference on Artificial Intelligence and Cognitive Science. Now I am starting my PhD journey researching about constraint programming with a focus on smart grids optimisation. I am also really interested in game theory and investigating the potential of combining.

ML/DL and constraint programming to address combinatorial optimisation problems.

Supervisors: Professor Barry O’Sullivan & Dr. Diarmuid Grimes

Ciaran is a third year PhD student in the Unit for Time Series forecasting using deep learning at University College Cork where he is supervised by Dr. Steven Prestwich and Dr. Andrea Visentin. His research is in the field of electricity price forecasting using deep learning methods. Ciaran holds an MSc. in computational and financial maths, where his thesis was focused electricity price forecasting using machine learning methods.

Prior to joining the CRT-AI funded PhD programme, Ciaran worked on density functional theory in Queens University Belfast. The project aimed at using hse06 functional to map many body dispersion effects within the domain of quantum mechanics. In his spare time Ciaran enjoys playing soccer and powerlifting.

Supervisors: Dr. Steve Prestwich & Dr. Andrea Visentin