Within the new phase of CompBioMed we have dedicated a new Work Package to the emerging topic of machine learning and artificial intelligence with a focus on high performance data analytics required for these approaches. This meeting will be an opportunity for certain Core and Associate Partners working on this topic to gather and discuss current progress and the possibilities and needs to advance this burgeoning field.
The agenda incorporates talks from those in the field and those looking to progress into the field, followed by a day of software development sessions to advance the work and to learn from one another.
Confirmed speakers include:
- Keynote speaker: Prof. Ben Leimkuhler, University of Edinburgh, The Interplay of Machine Learning and Molecular Dynamics
- Austin Clyde, Argonne National Laboratory, Duelling GPUs: Scanning Chemical Space with Coupled Generative Models and Property Models
- Ola Engkvist, AstraZeneca, Artificial Intelligence in Drug Design, Progress and Bottlenecks
- Tristan Bereau, UvA, Exploring chemical space with multiscale simulations
- Maxime Vassaux, UCL, Integrating complexity in in silico models of biomaterials by means of concurrent multiscaling and automated data-base driven model reduction
- Marco Verdicchio, SURFsara, High Performance Machine Learning at SURFsara
- Narges Zarrabi, SURFsara, Data Management in CompBioMed: Moving towards FAIR Data
- Andrea Townsend-Nicholson, UCL, Developing a new community of practice by engaging clinicians and biomedical scientists with advanced computational methods
- Shantenu Jha, Rutgers, DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations
- André Mersky, Rutgers, RADICAL-Cybertools: Middleware Building Blocks for HPC & ML Workflows
- James Wilson, UCL, UCL Institutional Infrastructure for FAIR Data
- Gianni De Fabritiis, UPF, From functional to machine learning potentials in molecular simulations
- Philippe Hupé, Institut Curie, Software development and optimisation of bioinformatics pipelines to analyse high-throughput sequencing data in oncology