Buvana holds a BSc/MSc in Mathematics from the University of Madras. She was the class rep and Student Union General Secretary during her Bachelor’s Days. Buvana’s academic achievements include: Holding two gold medals as the best Post Graduate student during her Masters and Best student during Bachelors. She also took part in the Mathematical association and also worked with U&I as an educational volunteer teaching English and Mathematics to underprivileged kids in Chennai. Prior to joining CRT in AI, Buvana held a post as a Data and Business Analyst in a start-up company. Her Bachelor’s project was on using Rough set theory for extracting information from datasets using uncertainty. The subject of her Masters project was in Encodings in Lattice based cryptography and following the same lines, Buvana is now researching on making Privacy Preserving storage and computations in Big Data with a focus on Artificial Intelligence.

Supervisors: Dr Paolo Palmieri & Professor Gregory Provan

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

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

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