Kevin K. Yang 楊凱筌

@KevinKaichuang · Massachusetts, USA · joined 07 Nov 2016

Principal Researcher in BioML @MSFTResearch (@MSRNE). He/him/他. 🇹🇼

24 111Followers

6 381Following

11 795Posts total

24.3KViews on collected posts

Latest posts

Kevin K. Yang 楊凱筌@KevinKaichuang17 Sep

@KevinKaichuang what do you think is the role of a bioinformatician now that for everything data-science related we have gpt 6, fable 5.1, claude science, and etc? 33 views · 0 likes · 0 reposts · 0 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang17 Sep

@KevinKaichuang Not shocking, proposing doing calculus at work will get me shot when your colleague proposes a simpler heuristic using a basic formula 84 views · 1 likes · 0 reposts · 0 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang16 Sep

It's actually shocking I got as far as I did without taking linear algebra at all in undergrad 11.5K views · 77 likes · 1 reposts · 3 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang16 Sep

FoldDir: A flow-matching structure-conditioned protein sequence design model with excellent computational results and some functional nanobody redesigns. @AlpTartici @MichaelCJewett @Rbaltman https://t.co/Vaew7Oe66T

3.7K views · 67 likes · 9 reposts · 1 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang14 Sep

A nice review on ways to discover and engineer proteins that use fewer amino acids while maintaining structure and function. @FilipBuchel @KlaraH_lab https://t.co/9c2aHiI10B

5.6K views · 97 likes · 16 reposts · 1 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang11 Sep

A tractable way to incorporate pairwise interactions in ancestral sequence reconstruction. https://t.co/aq3O6VWTR8

3.4K views · 53 likes · 11 reposts · 1 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang17 May 2019

We (@ZvxyWu, @francesarnold) wrote a review about machine learning for protein engineering, but there's so many new papers that we can't update it quickly enough. So Zach and I made a public list of papers covering machine learning on proteins. https://t.co/KYKhcDvv2h 0 views · 747 likes · 186 reposts · 23 replies Open on X →

Against accounts of the same size

6 posts from the last 90 days, next to the 10K–100K follower range. shown to more people than peers of the same size.

Median views3 554this account924median for 10K–100K

Reach, %14.74%this account3.62%median for 10K–100K

Engagement, %1.55%this account1.52%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	3 554	924	3.85×
Reach (views ÷ followers)	14.74%	3.62%	4.07×
Engagement rate	1.55%	1.52%	1.02×

Others in this range → Compare with another account → How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

3.4K11 Sep

5.6K14 Sep

3.7K16 Sep

11.5K

8417 Sep

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.92%11 Sep

2.03%14 Sep

2.07%16 Sep

0.71%

1.19%17 Sep

0.00%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes56.2%295 in total

Reposts7.0%37 in total

Replies1.1%6 in total

Quotes0.2%1 in total

Bookmarks35.4%186 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.