Schedule

Each day consists of a morning session from 10am to 12pm and an afternoon session from 2pm to 4pm (except for Monday which we start at 11:00). The training week will take place at Imperial, in the room CHEM 660 (except on Thursday which will take place in CHEM 640A).

Monday

• Morning Session (11:00-12:30)
  • Intorduction to the module, data-driven modelling and statistical learning
• Afternoon Session (2:30-4:30)
  • Discussing group presentations and groups discuss.

Tuesday

• Morning Session (10:00-12:00)
  • Mechanistic modelling, Bayesian inference and simulation-based inference
• Afternoon Session (2:00-4:00)
  • Python computer lab on simulation-based inference: ABC Notebook

Wednesday

• Morning Session (10:00-12:00)
  • Free morning for group presentations and the EDI training
• Afternoon Session (2:00-4:00)
  • Introduction to biological data and single cell RNA-sequencing (scRNA-seq) data analysis
  • R compuer lab on scRNA-seq data science

Thursday

• Morning Session (10:00-12:00)
  • R compuer lab on scRNA-seq data science (10:00-11:30)
  • Biological background for advanced lab (11:30-12:00)
• Afternoon session (2:00-4:00)
  • Advanced Python and more R computer lab: SBI Notebook

Friday

• Morning Session (10:00-12:30)
  • Regression (10:00-10:30)
  • Classification (10:30-11:00)
  • Dimensionality Reduction (11:00-11:30)
  • Clustering (11:30-12:00)
  • Resampling (12:00-12:30)
• Afternoon Session (2:00-4:00)
  • Research talks
    • Freddie Whiting, A mathematical framework to learn evolution of cancer drug resistance from genetic barcoding (2:00-2:30)
    • Amaya Gallagher-Syed, Benchmarking foundation models for scRNA-seq data (2:30-3:00)
    • Jack Soulsby, Ordered Diffusion Kernels for pseudo-time analysis of scRNA-seq data (3:00-3:30)
    • Dimitris Volteras, Mechanistic models and amortised inference for learning gene-regulation from scRNA-seq data (3:30-4:00)