Lunch & Learn Machine Learning & AI in Protein Engineering Applications: Key Challenges & Solutions Strategies
There has been an increasing demand recently in using AI/ML to process complex Big Data. The advantage of AI/ML models over classical statistical modelling is that the former can solve highly non-linear problems often encountered in life science problems.
Dr. Alexander Lukyanov, Scientific Director, Machine Learning & AI, Uncertainty Analysis and Quantifications at BISC Global will review applications which converge AI/ML-based learned models including uncertainty quantifications (UQ) with model-based predictions so that all numerical and experimental data can help self-inform predictions. UQ for ML needs to be developed to inform uncertainties in learned models from the overall data. This allows effective building of UQ into a cognitive computing.
During this Lunch & Learn we will:
- Review the capabilities in building and deploying AI/ML models to address challenging questions in life science problems.
- Discuss key challenges & solutions in protein engineering applications
- Discuss the applicability of machine learning & AI technologists