Current Research Interests

My research focuses on developing machine learning methods to design and understand proteins. Specifically, my work aims to engineer novel and complex functions by combining advances in deep learning and experimental methods. Some of my current interests include:

  • Generative models and pretraining for proteins and chemistry
  • Machine learning for protein engineering
  • Experimental methods that increase the scale or fidelity of measurements

Selected Publications

A full list of my publications is available on Google Scholar.

FLIP2: Expanding Protein Fitness Landscape Benchmarks for Real-World Machine Learning Applications. Kieran Didi, Sarah Alamdari, Alex X Lu, Bruce Wittmann, Kadina E Johnston, Ava P Amini, Ali Madani, Maya Czeneszew, Christian Dallago, Kevin K Yang. Oral (top 168 / 23,918 submissions) at International Conference on Machine Learning 2026. [10.64898/2026.02.23.707496].

Protein Generation with Evolutionary Diffusion: Sequence Is All You Need. Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex X Lu, Nicolo Fusi, Ava P Amini, Kevin K Yang. eLife, 2026. [10.7554/eLife.112029.1].

Scalable and Cost-Efficient Custom Gene Library Assembly from Oligopools. Chase R Freschlin, Kevin K Yang, Philip A Romero. Science Advances, 2026. [10.1126/sciadv.ady2279].

The Dayhoff Atlas: Scaling Sequence Diversity for Improved Protein Generation. Kevin K Yang, Sarah Alamdari, Alex J Lee, Kaeli Kaymak-Loveless, Samir Char, Garyk Brixi, Carles Domingo-Enrich, Chentong Wang, Suyue Lyu, Nicolo Fusi, Neil Tenenholtz, Ava P Amini. Preprint, 2025. [10.1101/2025.07.21.665991].

Computational Scoring and Experimental Evaluation of Enzymes Generated by Neural Networks. Sean R Johnson, Xiaozhi Fu, Sandra Viknander, Clara Goldin, Sarah Monaco, Aleksej Zelezniak, Kevin K Yang. Nature Biotechnology, 2024. [10.1038/s41587-024-02214-2].

Protein Structure Generation via Folding Diffusion. Kevin E. Wu, Kevin K Yang, Rianne van den Berg, Sarah Alamdari, James Y. Zou, Alex X. Lu, Ava P. Amini. Nature Communications, 2024. 10.1038/s41467-024-45051-2.

Masked Inverse Folding with Sequence Transfer for Protein Representation Learning. Kevin K Yang, Niccolò Zanichelli, Hugh Yeh. Protein Engineering, Design and Selection, 2024. 10.1101/2022.05.25.493516.

Convolutions Are Competitive with Transformers for Protein Sequence Pretraining. Kevin K Yang, Nicolo Fusi, Alex X. Lu. Cell Systems, 2024. 10.1101/2022.05.19.492714.

Randomized Gates Eliminate Bias in Sort-Seq Assays. Brian L. Trippe, Buwei Huang, Erika A. DeBenedictis, Brian Coventry, Nicholas Bhattacharya, Kevin K Yang, David Baker, Lorin Crawford. Protein Science, 2022. bioRxiv.

Signal Peptides Generated by Attention-Based Neural Networks. Zachary Wu, Kevin K Yang, Michael J. Liszka, Alycia Lee, Alina Batzilla, David Wernick, David P. Weiner, Frances H. Arnold. ACS Synthetic Biology, 10 July 2020. 10.1021/acssynbio.0c00219.

Machine Learning-Guided Channelrhodopsin Engineering Enables Minimally-Invasive Optogenetics. Bedbrook CN, Kevin K Yang, Robinson JE, Gradinaru V, Arnold FH. Nature Methods, October 14, 2019. 10.1038/s41592-019-0583-8.

Machine-Learning-Guided Directed Evolution for Protein Engineering. Kevin K Yang, Wu Z, Arnold FH. Nature Methods, July 15, 2019. 10.1038/s41592-019-0496-6.