AIiH 2024 is delighted to include a plenary session in our conference programme. This session is dedicated to meeting challenges in translating AI research into healthcare applications. We have three speakers with a wide range of industrial and healthcare experiences. This plenary session includes an extended, chaired discussion after the three presentations.

Alba Di Pardo
IRCCS Istituto Neurologico Mediterraneo Neuromed, Italy

Artificial Intelligence and Rare Diseases: Addressing Clinical Needs

Dr. Alba Di Pardo is an Italian medical geneticist with many years of experience in the field of genetic rare diseases and neurodegeneration. Dr. Di Pardo has more than 15 years of research experience and many years of professional genetic testing and counselling expertise in the field of rare genetic neurological conditions. During her career she has received different academic awards and has worked at some of the most respected Universities in the World.
Presently, Dr. Di Pardo works at Centre for Neurogenetics and Rare Disease at IRCCS Neuromed, Italy, as junior group leader and operates as medical geneticist at the Rare Disease Clinics at the same Institute.
She has extensive experience in preclinical and clinical research and she is author of several scientific peer- reviewed papers. Her current research focuses on understanding common molecular mechanisms underlying different rare brain conditions.
Beside her scientific activity, Dr. Di Pardo actively collaborates with associations of patients affected with rare diseases and carries out dedicated dissemination and information programs via events, conferences, seminars and meetings.

Noura Al Moubayed, Durham University & Evergreen Life

Dr Al Moubayed is an Associate Professor at the department of computer science at Durham University, and Head of Applied Machine Learning and AI at Evergreen Life.
Her main research interest is in Explainable Machine Learning, Natural Language Processing, and Optimisation. Dr Al Moubayed received her PhD from Robert Gordon University, followed by post-doctoral positions at the University of Glasgow and Durham University. Her research projects focus on applying machine learning and deep learning solutions in the areas of healthcare, social signal processing, cyber-security, and Brain-Computer Interfaces. All of which involve high dimensional, noisy and imbalance data challenges.
Dr Al Moubayed is an Associate Editor for IEEE Transactions on Emerging Topics in Computational Intelligence and N8 CIR Machine Learning team lead for Durham. She leads multiple projects in collaboration with different industrial partners with a team of over 15 researchers. Her research received several medial coverages (e.g. BBC, ITV, Time Magazine, and Wired Magazine) and she was ranked amongst the top 20 women in AI in the UK by RE•WORK 2019.

Haoda Fu, Eli Lilly and Company, USA

Generative AI for Pharmaceutical Company: Emerging Opportunities

Dr. Haoda Fu is an Associate Vice President and an Enterprise Lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association), and IMS Fellow (Institute of Mathematical Statistics). He is also an adjunct professor of biostatistics department, Univ. of North Carolina Chapel Hill and Indiana university School of Medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin – Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics and data science methodology research. He has more than 100 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modelling, personalised medicine, indirect and mixed treatment comparison, joint modelling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrika, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organisations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session. He is a COPSS Snedecor Awards committee member from 2022-2026, and will also serve as an associate editor for JASA theory and method from 2023.