Pascal is a DPhil student in the CS Department at Oxford University, supervised by Yarin Gal. His research interests lie at the intersection of Bayesian Deep Learning, Generative Models, Causal Inference and Computational Biology. The current focus of his work is to develop methods to quantify and leverage uncertainty in models for structured representations (e.g., sequences, graphs), with applications in biology and medicine.
He has several years of applied machine learning experience developing AI solutions, primarily within the healthcare and pharmaceutical industries (e.g., Real World Evidence, treatment adherence, disease prediction). Prior to coming to Oxford, he was a Senior Manager at McKinsey & Company in the New York and Paris offices, where he was leading cross-disciplinary teams on fast-paced analytics engagements.
He obtained a M.S. in Operations Research from Columbia University, and a B.S. and M.S. in Applied Mathematics from Ecole Polytechnique. He is a recipient of a GSK iCASE scholarship.
Publications while at OATML • News items mentioning Pascal Notin • Reproducibility and Code • Blog Posts
The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease likelihood, to predicting immune-escape mutations in viruses and designing novel biotherapeutic proteins. Deep generative models of protein sequences trained on multiple sequence alignments have been the most successful approaches so far to address these tasks. The performance of these methods is however contingent on the availability of sufficiently deep and diverse alignments for reliable training. Their potential scope is thus limited by the fact many protein families are hard, if not impossible, to align. Large language models trained on massive quantities of non-aligned protein sequences from diverse families address these problems and show potential to eventually bridge the performance gap. We introduce Tranception, a novel transformer architecture leveraging autoregressive predictions and retrieval o... [full abstract]
Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan Gomez, Debora Marks, Yarin Gal
[Preprint] [BibTex] [Code]
In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database. Such generative models hold the promise of greatly accelerating protein design. We conduct the first systematic study of how capabilities evolve with model size for autoregressive transformers in the protein domain: we evaluate RITA models in next amino acid prediction, zero-shot fitness, and enzyme function prediction, showing benefits from increased scale. We release the RITA models openly, to the benefit of the research community.
Daniel Hesslow, Niccoló Zanichelli, Pascal Notin, Iacopo Poli, Debora Marks
ICML, Workshop on Computational biology, 2022 (Spotlight)
[Preprint] [Code] [BibTex]
In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that serves to assess initial hypotheses about causal associations between biological mechanisms and disease pathologies. With billions of potential hypotheses to test, the experimental design space for in vitro genetic experiments is extremely vast, and the available experimental capacity - even at the largest research institutions in the world - pales in relation to the size of this biological hypothesis space. Machine learning methods, such as active and reinforcement learning, could aid in optimally exploring the vast biological space by integrating prior knowledge from various information sources as well as extrapolating to yet unexplored areas of the experimental design space based on available data. However, there exist no standardised benchmarks and data sets for this challenging task and little researc... [full abstract]
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
International Conference on Learning Representations, 2022
[Preprint] [BibTex] [Code]
Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical “modes”. The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters fro... [full abstract]
Chetan Gohil, Evan Roberts, Ryan Timms, Alex Skates, Cameron Higgins, Andrew Quinn, Usama Pervaiz, Joost van Amersfoort, Pascal Notin, Yarin Gal, Stanislaw Adaszewski, Mark Woolrich
In submission [Preprint] [BibTex]
Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable. Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if... [full abstract]
Jonathan Frazer, Pascal Notin, Mafalda Dias, Aidan Gomez, Joseph K. Min, Kelly Brock, Yarin Gal, Debora Marks
Nature, 2021 (volume 599, pages 91–95)
[Paper] [BibTex] [Preprint] [Website] [Code]
Optimization in the latent space of variational autoencoders is a promising approach to generate high-dimensional discrete objects that maximize an expensive black-box property (e.g., drug-likeness in molecular generation, function approximation with arithmetic expressions). However, existing methods lack robustness as they may decide to explore areas of the latent space for which no data was available during training and where the decoder can be unreliable, leading to the generation of unrealistic or invalid objects. We propose to leverage the epistemic uncertainty of the decoder to guide the optimization process. This is not trivial though, as a naive estimation of uncertainty in the high-dimensional and structured settings we consider would result in high estimator variance. To solve this problem, we introduce an importance sampling-based estimator that provides more robust estimates of epistemic uncertainty. Our uncertainty-guided optimization approach does not require modifica... [full abstract]
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
[Preprint] [Proceedings] [BibTex] [Code]
The ability to quantify the uncertainty in the prediction of a Bayesian deep learning model has significant practical implications—from more robust machine-learning based systems to more effective expert-in-the loop processes. While several general measures of model uncertainty exist, they are often intractable in practice when dealing with high dimensional data such as long sequences. Instead, researchers often resort to ad hoc approaches or to introducing independence assumptions to make computation tractable. We introduce a principled approach to estimate uncertainty in high dimensions that circumvents these challenges, and demonstrate its benefits in de novo molecular design.
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
Uncertainty & Robustness in Deep Learning Workshop, ICML, 2020
We demonstrate 10-40% speedups and memory reduction with Wide ResNets, EfficientNets, and Transformer models, with minimal to no loss in accuracy, using SliceOut---a new dropout scheme designed to take advantage of GPU memory layout. By dropping contiguous sets of units at random, our method preserves the regularization properties of dropout while allowing for more efficient low-level implementation, resulting in training speedups through (1) fast memory access and matrix multiplication of smaller tensors, and (2) memory savings by avoiding allocating memory to zero units in weight gradients and activations. Despite its simplicity, our method is highly effective. We demonstrate its efficacy at scale with Wide ResNets & EfficientNets on CIFAR10/100 and ImageNet, as well as Transformers on the LM1B dataset. These speedups and memory savings in training can lead to CO2 emissions reduction of up to 40% for training large models.
Pascal Notin, Aidan Gomez, Joanna Yoo, Yarin Gal
OATML DPhil student Pascal Notin will give a talk at the Center for Basic Machine Learning Research in Life Science (MLLS), ELLIS Unit in Copenhagen, on generative models for protein fitness and mutation effects prediction.
OATML student Pascal Notin is co-organizing the 7th edition of the Workshop on Computational Biology (WCB) at ICML 2022 jointly with collaborators at Harvard, Columbia, Cornell and others. OATML students Neil Band, Freddie Bickford Smith, Jan Brauner, Andreas Kirsch and Lood Van Niekerk are part of the PC.
OATML students Pascal Notin, Andrew Jesson, Clare Lyle and Professor Yarin Gal are co-organizing the first Machine Learning for Drug Discovery (MLDD) workshop at ICLR 2022 jointly with collaborators at GSK, Harvard, MILA, MIT and others.
The paper “Disease variant prediction with deep generative models of evolutionary data” was published in Nature. The work is a collaboration between OATML and the Marks lab at Harvard Medical School. It was led by Pascal Notin and Professor Yarin Gal from OATML together with Jonathan Frazer, Mafalda Dias, and Debbie Marks from the Marks lab. OATML DPhil student Aidan Gomez and Marks lab researchers Joseph Min and Kelly Brock are co-authors on the paper.
Thirteen papers with OATML members accepted to NeurIPS 2021 main conference. More information in our blog post.
OATML graduate students Sebastian Farquhar and Jannik Kossen receive best reviewer awards (top 10%) at ICML 2021. Further, OATML graduate students Tim G. J. Rudner, Pascal Notin, Panagiotis Tigas, and Binxin Ru have served the conference as expert reviewers.
OATML MSc student Lood van Niekerk received a best poster award at the 2021 ICML Workshop on Computational Biology for his work on “Exploring the latent space of deep generative models: Applications to G-protein coupled receptors”. This is part of an ongoing collaboration between OATML and the Marks Lab. From OATML, Professor Yarin Gal and DPhil student Pascal Notin are co-authors on the paper.
Seven papers with OATML members accepted to ICML 2021, together with 14 workshop papers. More information in our blog post.
OATML graduate student Pascal Notin will give an invited talk on Uncertainty in deep generative models with applications to genomics and drug design at the Cornell Machine Learning in Medicine seminar series. Professor Yarin Gal and collaborator José Miguel Hernández-Lobato are co-authors on the paper.
OATML graduate student Pascal Notin will give an invited talk on “Large-scale clinical interpretation of genetic variants using evolutionary data and deep generative models” at the EMBL-EBI workshop on Machine Learning in Drug Discovery. The work is a collaboration between OATML (Pascal and Professor Yarin Gal) and the Marks Lab at Harvard.
This is the official code repository for the paper “Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval”. The repository also includes the full ProteinGym benchmarks.Code
Official repository for our paper “Improving black-box optimization in VAE latent space using decoder uncertainty”. The codebase provides an example application in molecular optimization with JT-VAE for both Gradient Ascent and Bayesian Optimization.Code
Official repository for our paper “Disease variant prediction with deep generative models of evolutionary data”. The code provides all functionalities required to train the different models leveraged in EVE (Evolutionary model of Variant Effect), as well as the ones to score any mutated protein sequence of interest.Code
OATML group members and collaborators are proud to present 11 papers at the ICML 2022 main conference and workshops. Group members are also co-organizing the Workshop on Computational Biology, and the Oxford Wom*n Social. …Full post...
Sören Mindermann, Jan Brauner, Muhammed Razzak, Andreas Kirsch, Aidan Gomez, Sebastian Farquhar, Pascal Notin, Tim G. J. Rudner, Freddie Bickford Smith, Neil Band, Panagiotis Tigas, Andrew Jesson, Lars Holdijk, Joost van Amersfoort, Kelsey Doerksen, Jannik Kossen, Yarin Gal, 17 Jul 2022
OATML group members and collaborators are proud to present 4 papers at ICLR 2022 main conference. …Full post...
Yarin Gal, Tuan Nguyen, Andrew Jesson, Pascal Notin, Atılım Güneş Baydin, Clare Lyle, Milad Alizadeh, Joost van Amersfoort, Sebastian Farquhar, Muhammed Razzak, Freddie Kalaitzis, 01 Feb 2022
OATML group members and collaborators are proud to present 13 papers at NeurIPS 2021 main conference. …Full post...
Jannik Kossen, Neil Band, Aidan Gomez, Clare Lyle, Tim G. J. Rudner, Yarin Gal, Binxin (Robin) Ru, Clare Lyle, Lisa Schut, Atılım Güneş Baydin, Tim G. J. Rudner, Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Pascal Notin, Angelos Filos, 11 Oct 2021
OATML group members and collaborators are proud to present 21 papers at ICML 2021, including 7 papers at the main conference and 14 papers at various workshops. Group members will also be giving invited talks and participate in panel discussions at the workshops. …Full post...
Angelos Filos, Clare Lyle, Jannik Kossen, Sebastian Farquhar, Tom Rainforth, Andrew Jesson, Sören Mindermann, Tim G. J. Rudner, Oscar Key, Binxin (Robin) Ru, Pascal Notin, Panagiotis Tigas, Andreas Kirsch, Jishnu Mukhoti, Joost van Amersfoort, Lisa Schut, Muhammed Razzak, Aidan Gomez, Jan Brauner, Yarin Gal, 17 Jul 2021
We are glad to share the following 13 papers by OATML authors and collaborators to be presented at this ICML conference and workshops …Full post...
Angelos Filos, Sebastian Farquhar, Tim G. J. Rudner, Lewis Smith, Lisa Schut, Tom Rainforth, Panagiotis Tigas, Pascal Notin, Andreas Kirsch, Clare Lyle, Joost van Amersfoort, Jishnu Mukhoti, Yarin Gal, 10 Jul 2020