Alex Lu

According to our database, Alex Lu authored at least 26 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2026

MorphoHELM: A Comprehensive Benchmark for Evaluating Representations for Microscopy-Based Morphology Assays.

Emre Hayir, Lorin Crawford, Alex X. Lu

CoRR, May, 2026

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction.

Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen, Haiyu Dong, Zekun Ni, Jeremy Berman, Genevieve Flaspohler, Alex Lu, Jakob Schloer, Joshua Talib, Jonathan A. Weyn, Lester Mackey

CoRR, April, 2026

2025

Causal integration of chemical structures improves representations of microscopy images for morphological profiling.

Yemin Yu, Neil A. Tenenholtz, Lester Mackey, Ying Wei, David Alvarez-Melis, Ava P. Amini, Alex X. Lu

CoRR, April, 2025

scGeneScope: A Treatment-Matched Single Cell Imaging and Transcriptomics Dataset and Benchmark for Treatment Response Modeling.

Joel Dapello, Marcel Nassar, Ridvan Eksi, Ban Wang, Jules Gagnon-Marchand, Kenneth T. Gao, Akram Baharlouei, Kyra Thrush, Nina Riehs, Amy Peterson, Aniket A. Tolpadi, Abhejit Rajagopal, Henry Miller, Ashley Conard, David Alvarez-Melis, Rory Stark, Simone Bianco, Morgan Levine, Ava Amini, Alex X. Lu, Nicolò Fusi, Ravi Pandya, Valentina Pedoia, Hana El-Samad

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Representation Learning Methods for Single-Cell Microscopy are Confounded by Background Cells.

Arushi Gupta, Alan M. Moses, Alex Lu

Proceedings of the 20th Machine Learning in Computational Biology (MLCB), 2025

Investigating Dictionary Expansion for Video-based Sign Language Dictionaries.

Aashaka Desai, Daniela Massiceti, Richard E. Ladner, Hal Daumé III, Danielle Bragg, Alex Xijie Lu

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

Exploring Reduced Feature Sets for American Sign Language Dictionaries.

Ben Kosa, Aashaka Desai, Alex X. Lu, Richard E. Ladner, Danielle Bragg

Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 2025

Exploring Collaboration to Center the Deaf Community in Sign Language AI.

Rie Kamikubo, Abraham Glasser, Alex X. Lu, Hal Daumé III, Hernisa Kacorri, Danielle Bragg

Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility, 2025

2024

Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas.

Aashaka Desai, Maartje de Meulder, Julie A. Hochgesang, Annemarie Kocab, Alex X. Lu

CoRR, 2024

Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models.

Francesca-Zhoufan Li, Ava P. Amini, Yisong Yue, Kevin K. Yang, Alex Xijie Lu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles.

Kayo Yin, Chinmay Singh, Fyodor Minakov, Vanessa Milan, Hal Daumé III, Cyril Zhang, Alex Lu, Danielle Bragg

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition.

Aashaka Desai, Lauren Berger, Fyodor O. Minakov, Vanessa Milan, Chinmay Singh, Kriston Pumphrey, Richard E. Ladner, Hal Daumé III, Alex X. Lu, Naomi Caselli, Danielle Bragg

CoRR, 2023

ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition.

Aashaka Desai, Lauren Berger, Fyodor Minakov, Nessa Milano, Chinmay Singh, Kriston Pumphrey, Richard E. Ladner, Hal Daumé III, Alex X. Lu, Naomi Caselli, Danielle Bragg

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Domain adaptation using optimal transport for invariant learning using histopathology datasets.

Kianoush Falahkheirkhah, Alex Xijie Lu, David Alvarez-Melis, Grace Huynh

Proceedings of the Medical Imaging with Deep Learning, 2023

2022

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.

Alex X. Lu, Amy X. Lu, Iva Pritisanac, Taraneh Zarin, Julie D. Forman-Kay, Alan M. Moses

PLoS Comput. Biol., 2022

Protein structure generation via folding diffusion.

Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X. Lu, Ava P. Amini

CoRR, 2022

2021

Unsupervised machine learning for hypothesis discovery and representation learning in biological image and sequence analysis.

Alex Lu

PhD thesis, 2021

CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning.

Stanley Bryan Z. Hua, Alex X. Lu, Alan M. Moses

CoRR, 2021

Random Embeddings and Linear Regression can Predict Protein Function.

Tianyu Lu, Alex X. Lu, Alan M. Moses

CoRR, 2021

Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis.

Karren D. Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi S. Jaakkola, Caroline Uhler

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning.

Amy X. Lu, Alex X. Lu, Alan M. Moses

CoRR, 2020

Improved Conditional Flow Models for Molecule to Image Synthesis.

Karren D. Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay, Tommi S. Jaakkola, Caroline Uhler

CoRR, 2020

2019

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.

Alex X. Lu, Oren Z. Kraus, Sam Cooper, Alan M. Moses

PLoS Comput. Biol., 2019

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, David W. Andrews, Alan M. Moses

CoRR, 2019

YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.

Alex X. Lu, Taraneh Zarin, Ian Shenyen Hsu, Alan M. Moses

Bioinform., 2019

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 W. Andrews, Alan M. Moses

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019