Amy X. Lu

I'm a third-year Computer Science PhD student at UC Berkeley and BAIR, advised by Pieter Abbeel and supported by a NSERC PGS-D award. I also work part-time on research at Prescient Design (Genentech).

Broadly, I'm interested in developing artificial intelligence methods for biology, and bringing task generalization capabilities in large models to drug discovery. I'm especially interested in generative approaches leveraging multimodal information and biophysical priors.

Previously, I was a Student Researcher at Google Brain and Machine Learning Engineer at insitro. I completed my MSc at the University of Toronto with Alan Moses and Marzyeh Ghassemi, during which I was a visitor in Anshul Kundaje's group at Stanford University. I completed my undergrad at the University of Waterloo, and completed research internships in clinical genomics at Harvard Medical School and molecular dynamics at EPFL.

CV / Google Scholar / GitHub / Twitter / amyxlu [at] berkeley [dot] edu

Research

*Equal contribution.

	TOPH: Adapting A Contrastive Question-Answering Framework for Protein Search Ron Boger, Amy X. Lu, Seyone Chithrananda*, Kevin Yang, Petr Skopintsev, Ben Adler, Eric Wallace, Peter Yoon, Pieter Abbeel, Jennifer Doudna ICML Workshop on Computational Biology, 2023 Paper / Poster We present a protein semantic similarity search method for RNA-Guided endonuclease discovery, inspired by dense retrieval methods in open-domain question answering, and introduce a new dataset of CRISPR-Cas and evolutionary-related nucleases.
	Pretraining strategies for effective promoter-driven gene expression prediction Aniketh Janardhan Reddy, Michael H. Herschl, Sathvik Kolli, Amy X. Lu, Xinyang Geng, Aviral Kumar, Patrick D. Hsu, Sergey Levine, Nilah M. Ioannidis bioRxiv, 2023 Paper Pretraining and transfer learning strategies for improving model-based design of promoters for cell type specific expression.
	Data-Driven Optimization for Protein Design: Workflows, Algorithms and Metrics Sathvik Kolli, Amy X. Lu, Xinyang Geng, Aviral Kumar, Sergey Levine ICLR Workshop on Machine Learning for Drug Discovery (MLDD), 2022 Paper Strategies for data curation, model-training, optimization, and evaluation heuristics for data-driven proposals of novel de novo proteins.
	Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning Alex X Lu, Amy X Lu, Iva Pritišanac, Taraneh Zarin, Julie D Forman-Kay, Alan M Moses PLOS Computational Biology, 2022 Paper / Preprint Reverse Homology is a self-supervised method which captures evolutionary information by contrastive learning to discover molecular features of intrinsically disordered regions.
	Learned embeddings from deep learning to visualize and predict protein sets Christian Dallago, Konstantin Schütze, Michael Heinzinger, Tobias Olenyi, Maria Littmann, Amy X Lu, Kevin K Yang, Seonwoo Min, Sungroh Yoon, James T Morton, Burkhard Rost Current Protocols, 2021 Paper / Web Server / Code
	Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning Amy X Lu, Alex X. Lu, Alan Moses Machine Learning for Computational Biology (MLCB), 2020 Paper / Poster We outline how viewing evolution as natural sequence augmentation for contrastive learning recapitulates comparative genomics, and maximizes the mutual information between sequence and function.
	Self-Supervised Contrastive Learning of Protein Representations by Mutual Information Maximization Amy X Lu, Haoran Zhang, Marzyeh Ghassemi, Alan Moses Machine Learning for Computational Biology (MLCB), 2020 Paper / Poster / Code CPCProt uses contrastive learning to learn a parameter-efficient way of embedding proteins, and performs competitively with large language models.
	Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings Haoran Zhang, Amy X Lu, Mohamed Abdalla, Matthew McDermott, Marzyeh Ghassemi. ACM Conference on Health, Inference, and Learning (CHIL), 2020 (Spotlight) Paper / arXiv / Poster / Code We apply fairness definitions to quantify the cross-group bias in BERT embeddings pretrained on medical notes, and find statistically significant differences in classifier performance.
	The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers Alex X Lu, Amy X Lu, Wiebke Schormann, Marzyeh Ghassemi, David Andrews, Alan Moses. Neural Information Processing Systems (NeurIPS), 2019 Paper / arXiv Introduces the COOS-7 dataset to benchmark and evaluate the capacity of feature learning methods to generalize to natural distribution shifts in microscopy images.
	History and publication trends in the diffusion and early uptake of indirect comparison meta-analytic methods to study drugs: animated coauthorship networks over time Joann K Ban, Mina Tadrous, Amy X Lu, Erin A Cicinelli, Suzanne M Cadarette BMJ open, 2018 Paper

	Paired Cell Inpainting: A Multiple-Instance Extension of Self-Supervised Learning for Bioimage Analysis Alex X. Lu, Amy X Lu, Alan Moses ICML Workshop on Self-Supervised Learning, 2019 Paper
	Quantifying Fairness in a Multi-Group Setting and its Impact in the Clinical Setting Mohamed Abdalla, Amy X Lu, Haoran Zhang, Irene Chen, Marzyeh Ghassemi NeurIPS Workshop on Fair ML for Health, 2019 Paper
	Transfer Learning vs. Batch Effects: what can we expect from neural networks in computational biology Alan Moses, Alex X Lu, Amy X Lu, Marzyeh Ghassemi Machine Learning for Computational Biology (MLCB), 2019 Paper

Misc. I enjoy road biking through the East Bay redwoods, and playing the piano, especially Chopin and hip-hop covers. I'm usually coding to EDM or Beethoven's complete piano sonatas while eating 90% dark chocolate. My car and bikes are named after F. Scott Fitzgerald characters, and administrative entities call me Xiaoping Lu (逯晓萍).

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