Recent & Upcoming Talks
2024
MLDD -- Hybrid protein language models for fitness prediction and design
Hybrid protein language models for fitness prediction and design
Shanghai Jiao Tong University -- Hybrid protein language models for fitness prediction and design
Hybrid protein language models for fitness prediction and design
GenLife -- Hybrid protein language models for fitness prediction and design
Hybrid protein language models for fitness prediction and design
Profluent JC -- Hybrid protein language models for fitness prediction and design
Hybrid protein language models for fitness prediction and design
MIA @ Broad -- Hybrid protein language models for fitness prediction and design
Hybrid protein language models for fitness prediction and design
2023
NeurIPS -- ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers
ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers
NeurIPS -- ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
GSK AI, Research symposium - Hybrid protein language models for fitness prediction
Hybrid protein language models for fitness prediction and ProteinGym benchmarks
ML for Protein Engineering seminar - Hybrid protein language models for fitness prediction
Hybrid protein language models for fitness prediction and ProteinGym benchmarks
CIRM - AI & Bio Seminar - Hybrid protein language models for fitness prediction
Hybrid protein language models for fitness prediction and ProteinGym benchmarks
University of Cambridge - Protein Dojo - Hybrid protein language models for fitness prediction
Hybrid protein language models for fitness prediction and ProteinGym benchmarks
2022
OpenBioML, Journal club - Disease variant prediction with deep generative models of evolutionary data
Disease variant prediction with deep generative models of evolutionary data
CUHK-Shenzhen, Long Feng Science Forum - Generative models for protein fitness prediction
Generative models for protein fitness prediction
ICML, Workshop on Computational Biology - Moderation of the panel on ML for drug discovery
Spotlight presentation at ICML 2022 of the paper ‘Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval’
ICML, Spotlight talk - Tranception paper presentation
Spotlight presentation at ICML 2022 of the paper ‘Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval’
OpenBioML, Journal club - Tranception paper presentation
Presentation of the Tranception paper at the OpenBioML journal club
Facebook AI Research (FAIR), Journal club - Tranception paper presentation
Presentation of the Tranception paper to the Facebook AI Research (FAIR) team
Center for Basic Machine Learning Research in Life Science (MLLS, ELLIS unit in Copenhagen), Journal club - Disease variant prediction with deep generative models of evolutionary data
Disease variant prediction with deep generative models of evolutionary data
ICLR, Machine Learning for Drug Discovery (MLDD) workshop - Opening remarks
Opening remarks to the first MLDD workshop at ICLR (2022)
International Genetic Epidemiology Society (IGES), Journal club - Disease variant prediction with deep generative models of evolutionary data
Disease variant prediction with deep generative models of evolutionary data
2021
GSK AI, Research symposium - Disease variant prediction with deep generative models of evolutionary data
Disease variant prediction with deep generative models of evolutionary data
Cornell University, Machine Learning in Medicine (MLIM) seminar - Uncertainty in deep generative models with applications to genomics and drug design
Uncertainty in deep generative models with applications to genomics and drug design
EMBL-EBI, Workshop on Machine Learning in Drug Discovery - Large-scale clinical interpretation of genetic variants using evolutionary data and deep generative models
Large-scale clinical interpretation of genetic variants using evolutionary data and deep generative models