Academic contributions
Talks
- From modeling the tissue to modeling all of biology, Broad ML for Drug Discovery Symposium, 2025
- Multi-scale fondation models for the language of life, TEDxParis, 2025
- Ensembling over classifiers: a bias-variance perspective, Bayes-Duality Workshop, 2024
- ML-guided nanobody design targeting COVID-19, Gaussian Process Seminar Series, 2023
Papers
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities, arXiv, 2025
- H-optimus-0, HuggingFace, 2024
- AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions, NeurIPS Datasets & Benchmarks, 2023
- High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2, bioRxiv, 2023
- Ensembles of Classifiers: a Bias-Variance Perspective, TMLR 2022
- Plex: Towards Reliability using Pretrained Large Model Extensions, arXiv, 2022
- Understanding the bias-variance tradeoff of Bregman divergences, arXiv, 2022
- Sparse MoEs meet Efficient Ensembles, TMLR 2022
- Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning, arXiv 2022
- Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling, AISTATS, 2021
- Distilling Ensembles Improves Uncertainty Estimates, AABI 2020
- Population-Based Black-Box Optimization for Biological Sequence Design, ICML, 2020
- Biological Sequence Design using Batched Bayesian Optimization, NeurIPS ML4PS Workshop, 2019
- A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes, ICML, 2019
- Foundations of Sequence-to-Sequence Modeling for Time Series, AISTATS, 2019
- Learning Determinantal Point Processes by Corrective Negative Sampling, AISTATS, 2019
- Learning with generalized negative dependence : probabilistic models of diversity for machine learning, PhD Thesis, 2019
- DppNet: Approximating Determinantal Point Processes with Deep Networks, NeurIPS, 2019
- Exponentiated Strongly Rayleigh Distributions, NeurIPS, 2018
- Maximizing Induced Cardinality Under a Determinantal Point Process, NeurIPS, 2018
- Elementary Symmetric Polynomials for Optimal Experimental Design, NeurIPS, 2017
- Kronecker Determinantal Point Processes, NeurIPS, 2016
- Diversity Networks: Neural Network Compression Using Determinantal Point Processes, ICLR, 2016
- Fixed-point algorithms for learning determinantal point processes, ICML, 2015
Hosted workshops
- Multi-Modal Foundation Models for Cancer Detection and Prevention, ICCV, 2025
- Bridging the Gap Between Practice and Theory in Deep Learning, ICLR, 2024
- Duality Principles for Modern Machine Learning, ICML, 2023
- Negative dependence and submodularity for ML, ICML, 2020
- Negative dependence and submodularity for ML, ICML, 2019
Patents
- Ensembling mixture-of-experts neural networks (US20230107409A1), 2022
- Population-based black-box optimization (US20230083892A1)
Research topics
Foundation models for biomedical research
Negative dependence and related polynomials
Foundation models for biomedical research
Modeling uncertainty in machine learning
Negative dependence and related polynomials
Foundation models for biomedical research
Negative dependence and related polynomials
Negative dependence and related polynomials
Negative dependence and related polynomials
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