Update info.

about.md

@@ -5,7 +5,8 @@ permalink: /about/
 ---
 I am currently a senior researcher at [Microsoft Research New England](https://www.microsoft.com/en-us/research/lab/microsoft-research-new-england/). Previously, I was a machine learning scientist at [Generate Biomedicines](https://generatebiomedicines.com/), a [Flagship Pioneering](https://www.flagshippioneering.com/) company, where I used machine learning to optimize proteins.
 
-From 2014-2018, I was a PhD student in Chemical Engineering at Caltech. I worked in Frances Arnold's [lab](http://cheme.che.caltech.edu/groups/fha/). The Arnold lab is best known for its pioneering use of [directed evolution](https://en.wikipedia.org/wiki/Directed_evolution) to create useful proteins without requiring a deep understanding of the biophysical underpinnings of protein folding and function. Recently, they've been designing new [light-sensitive proteins](http://www.pnas.org/content/early/2017/03/09/1700269114.abstract) for applications in neuroscience and evolving an enzyme to make [carbon-silicon bonds](http://science.sciencemag.org/content/354/6315/1048.full?ijkey=mIJS6o5p4H63Y&keytype=ref&siteid=sci). They also pioneered the use of [machine learning for protein engineering](http://cheme.che.caltech.edu/groups/fha/publications/Romero_PNAS2012.pdf).
+From 2014-2018, I was a PhD student in Chemical Engineering at Caltech. I worked in Frances Arnold's [lab](http://cheme.che.caltech.edu/groups/fha/), where I helped pioneer the use of [machine learning for protein engineering](https://doi.org/10.1038/s41592-019-0496-6)
+<!--The Arnold lab is best known for its pioneering use of [directed evolution](https://en.wikipedia.org/wiki/Directed_evolution) to create useful proteins without requiring a deep understanding of the biophysical underpinnings of protein folding and function. Recently, they've been designing new [light-sensitive proteins](http://www.pnas.org/content/early/2017/03/09/1700269114.abstract) for applications in neuroscience and evolving an enzyme to make [carbon-silicon bonds](http://science.sciencemag.org/content/354/6315/1048.full?ijkey=mIJS6o5p4H63Y&keytype=ref&siteid=sci). -->
 
 Before moving to California, I completed my undergraduate degree at The Ohio State University, where I studied chemical engineering with a minor in piano performance. Between Ohio State and graduate school, I taught math and physics for three years at a high school in Inglewood, California through Teach for America. In those three years, I transformed from a struggling first-year teacher into an effective instructor and robotics coach with the help of the amazing staff at Animo Inglewood Charter High School and Green Dot Public Schools. In June 2017, I had the honor of watching my last class of freshmen graduate from high school, and I'm excited to see what they do with their futures.
 

index.md

@@ -8,4 +8,7 @@ I'm a computational biologist working at the intersection of machine learning an
 
 Here's some more [about me](/about) and details about [my research](/research). My resume can be found [here](https://github.com/yangkky/resume/blob/master/KKY_cv.pdf).
 
-Please email me at yang dot kevin at microsoft dot com if you are interested in collaborating or a research internship. 
+Please email me at yang dot kevin at microsoft dot com if you are interested in collaborating or a research internship. My previous interns include: 
+
+- Amy Wang
+- Kevin Wu

research.md

@@ -38,6 +38,24 @@ In the second half of my PhD, I focused on developing methods for the two key st
 
 # Publications
 
+**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](https://doi.org/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](https://doi.org/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](https://doi.org/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](https://doi.org/10.1101/2022.02.17.480881)
+
+**Deep self-supervised learning for biosynthetic gene cluster detection and product classification.** 
+Carolina Rios-Martinez, Nicholas Bhattacharya, Ava P Amini, Lorin Crawford, Kevin K. Yang. *PLoS Computational Biology*, 2023. [10.1371/journal.pcbi.1011162](https://doi.org/10.1371/journal.pcbi.1011162)
+
+**Exploring evolution-based &-free protein language models as protein function predictors.**
+Mingyang Hu, Fajie Yuan, Kevin K. Yang, Fusong Ju, Jin Su, Hui Wang, Fei Yang, Qiuyang Ding. [NeurIPS 2022](https://arxiv.org/abs/2206.06583)
+
 **Evolutionary velocity with protein language models.** Brian L. Hie, Kevin K. Yang, and Peter S. Kim. *Cell Systems*, 2022. [10.1016/j.cels.2022.01.003](https://doi.org/10.1016/j.cels.2022.01.003)
 
 **Machine learning modeling of family wide enzyme-substrate specificity screens.**
@@ -72,17 +90,18 @@ Samuel Goldman, Ria Das, Kevin K Yang, Connor W Coley. *PLoS computational biolo
 
 # Preprints
 
-**Exploring evolution-based &-free protein language models as protein function predictors.**
-Mingyang Hu, Fajie Yuan, Kevin K. Yang, Fusong Ju, Jin Su, Hui Wang, Fei Yang, Qiuyang Ding. [arxiv](https://arxiv.org/abs/2206.06583)
+**Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models.** 
+Francesca-Zhoufan Li, Ava P. Amini, Yisong Yue, Kevin K. Yang, Alex X. Lu. 
+[10.1101/2024.02.05.578959](https://doi.org/10.1101/2024.02.05.578959)
 
-**Masked inverse folding with sequence transfer for protein representation learning.**
-Kevin K. Yang, Niccolò Zanichelli, Hugh Yeh. [biorxiv](https://doi.org/10.1101/2022.05.25.493516)
+**Protein generation with evolutionary diffusion: sequence is all you need.**  
+Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex Xijie Lu, Nicolo Fusi, Ava Pardis Amini, Kevin K. Yang. [10.1101/2023.09.11.556673](https://doi.org/10.1101/2023.09.11.556673)
 
-**Convolutions are competitive with transformers for protein sequence pretraining.** Kevin K. Yang, Alex X. Lu, Nicolo Fusi. [biorxiv](https://doi.org/10.1101/2022.05.19.492714)
+**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. [10.1101/2023.03.04.531015](https://doi.org/10.1101/2023.03.04.531015)
+
+**Benchmarking uncertainty quantification for protein engineering.** Kevin P. Greenman, Ava P. Amini, Kevin K. Yang. [10.1101/2023.04.17.536962](https://doi.org/10.1101/2023.04.17.536962)
 
 
-**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. [biorxiv](https://doi.org/10.1101/2022.02.17.480881)
 
 <!--# Presentations
 

talks.md

@@ -4,15 +4,21 @@ title: Talks
 permalink: /talks/
 ---
 
-Here are slides from some of my recent talks. 
+Here are slides and recordings from some of my recent talks. 
 
-[2023 March - "Multimodal deep learning for protein engineering"](https://github.com/yangkky/presentations/blob/main/20230321_nvidia.pdf) covering layer-by-layer analysis, energetics, [COMPSS](https://www.biorxiv.org/content/10.1101/2023.03.04.531015v1),  and [Folding Diffusion](https://arxiv.org/abs/2209.15611). 
+[2023 November - "Protein sequence generation with evolutionary diffusion"](https://github.com/yangkky/presentations/blob/main/20231108_casp_evodiff.pdf) covering [EvoDiff](https://doi.org/10.1101/2023.09.11.556673) in greater detail. 
 
-[2022 Oct - "Multimodal deep learning for protein engineering"](https://github.com/yangkky/presentations/blob/main/20221014_bioinforange_bigcarp_folddiff.pdf) covering [BiGCARP](https://www.biorxiv.org/content/10.1101/2022.07.22.500861v1) and [Folding Diffusion](https://arxiv.org/abs/2209.15611). 
+[2023 September on the Gradient podcast](https://thegradientpub.substack.com/p/kevin-yang-protein-engineering-machine-learning)
 
+2023 June M2D2 ([slides](https://github.com/yangkky/presentations/blob/main/20230606_m2d2.pdf) [video](https://www.youtube.com/watch?v=qFSVVWcCRHs)) covering [signal peptide generation](https://pubs.acs.org/doi/full/10.1021/acssynbio.0c00219), [Folding DIffusion](https://www.nature.com/articles/s41467-024-45051-2), and [EvoDiff](https://doi.org/10.1101/2023.09.11.556673). 
 
-[2022 Oct - "Multimodal deep learning for protein engineering"](https://github.com/yangkky/presentations/blob/main/20221010_plms_columbia.pdf) covering [CARP](https://doi.org/10.1101/2022.05.19.492714), layer-by-layer analysis, [MIF-ST](https://www.biorxiv.org/content/10.1101/2022.05.25.493516v1), and energetics. 
+[2023 March - "Multimodal deep learning for protein engineering"](https://github.com/yangkky/presentations/blob/main/20230321_nvidia.pdf) covering [layer-by-layer analysis](https://doi.org/10.1101/2024.02.05.578959), energetics, [COMPSS](https://www.biorxiv.org/content/10.1101/2023.03.04.531015v1),  and [Folding Diffusion](https://www.nature.com/articles/s41467-024-45051-2). 
 
-[2022 Aug - "Multimodal deep learning for protein engineering"](https://github.com/yangkky/presentations/blob/main/202200802_merck_chr_sp_carp_mif.pdf) covering [channelrhodopsins](https://www.nature.com/articles/s41592-019-0583-8), [signal peptides](https://pubs.acs.org/doi/full/10.1021/acssynbio.0c00219), [CARP](https://doi.org/10.1101/2022.05.19.492714), and [MIF-ST](https://www.biorxiv.org/content/10.1101/2022.05.25.493516v1).
+[2022 Oct - "Multimodal deep learning for protein engineering"](https://github.com/yangkky/presentations/blob/main/20221014_bioinforange_bigcarp_folddiff.pdf) covering [BiGCARP](https://doi.org/10.1371/journal.pcbi.1011162) and [Folding Diffusion](https://www.nature.com/articles/s41467-024-45051-2). 
+
+
+[2022 Oct - "Multimodal deep learning for protein engineering"](https://github.com/yangkky/presentations/blob/main/20221010_plms_columbia.pdf) covering [CARP](https://doi.org/10.1101/2022.05.19.492714), [layer-by-layer analysis](https://doi.org/10.1101/2024.02.05.578959), [MIF-ST](https://doi.org/10.1093/protein/gzad015), and energetics. 
+
+[2022 Aug - "Multimodal deep learning for protein engineering"](https://github.com/yangkky/presentations/blob/main/202200802_merck_chr_sp_carp_mif.pdf) covering [channelrhodopsins](https://www.nature.com/articles/s41592-019-0583-8), [signal peptides](https://pubs.acs.org/doi/full/10.1021/acssynbio.0c00219), [CARP](https://doi.org/10.1101/2022.05.19.492714), and [MIF-ST](https://doi.org/10.1093/protein/gzad015).
 
 [2022 April - "Protein representation learning"](https://github.com/yangkky/presentations/blob/main/20220426_protein_representation.pdf) a tutorial on protein representation learning.