Flaminia Ronca
Associate professor
Programme director, sport and exercise medical sciences, ucl
Group co-lead, exercise neuroscience research group (enrg)

Dr Ronca is an Associate Professor at UCL with expertise in the field of Exercise & Neuroscience. She promotes human health and performance through collaborative work with law enforcement organisations, schools, industry and charities. Her key research focuses on the impact of physical activity on the brain, where she investigates the mechanisms through which exercise and other lifestyle factors support cognitive development in children and brain health in adults.

Her research impact extends beyond the lab. Her work with law enforcement agencies has led to the re-evaluation of fitness standards and the development of new approaches to support officer physical and mental health. Her work in schools has led to the implementation of cost-effective interventions to support child health, wellbeing and cognition. In elite sport, she is investigating head injury risk in female athletes. Her passion for social impact has led to her close collaboration and leadership with national and international charities to promote access to higher education, to support child and adult wellbeing, and to promote social mobility in disadvantaged groups.

His research interests lie at the intersection of biomedical signal processing, artificial intelligence, machine learning, and data mining. His primary focus is on developing advanced machine learning techniques and signal processing methods to understand (patho)physiological processes and improve diagnosis, prognosis, and monitoring. He has worked on various medical applications, including the study of brain hemodynamics in premature infants and the analysis of EEG, ECG, and EMG signals.

In addition to his applied research, Alexander is working on algorithms to optimize the initialization of parameters and hyperparameters in machine learning models to reduce their training time. He is also interested in developing algorithms that enhance the interpretability of data-driven models by identifying the functional nonlinear relationships between input variables, their interaction effects, and the output. Furthermore, he is open to collaborations with the industrial and public sectors, offering consultancy services in artificial intelligence applications, particularly in natural language processing, computer vision, medicine, and fintech.