Kejun (Albert) Ying

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Kejun (Albert) Ying

Kejun (Albert) Ying

@KejunYing

Incoming TTAP late 2026 (location TBD) | K00 Postdoc Wyss-Coray @Stanford & Baker lab @UWproteindesign | Bio PhD & CS MS @Harvard | BoA Consortium core

Stanford, CA เข้าร่วม Temmuz 2019
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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
🧠 I Let Agentic Cortex Run My Life for a Week. Nobody Noticed. Here's what I learned. The entire system is plaintext markdown. No vector DB. No fine-tuning. Key ideas: • Dendron's dot-notation naming = queryable knowledge graph with zero infra • Structured feedback (rule + rationale + scope) → permanent behavioral change • Voice profile extracted from your sent emails → register-aware drafting • Screenpipe ambient context → the agent knows what you did without you telling it Built for OpenClaw 🦞 / Claude Code. GitHub: github.com/Albert-Ying/ag… Blog: kejunying.com/blog/agentic-c…
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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
@JohnSchloendorn Thank you! It's not really criticism but more like a futuristic view, as we are far from the stage where we target complete solution for aging. Seeking compact solution is still a promising choice for finding partial solutions currently.
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John Schloendorn
John Schloendorn@JohnSchloendorn·
@KejunYing Nicely done. Criticism is of course easy, compared with constructive suggestions. The part where protein design fixes everything might need to be elaborated upon.
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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
@AlexJColville Hopefully with accelerated science, we may gain the ability to design complex biological subsystems soon enough - but here I'm talking about completely solving aging - to merely slowing aging to some extent with a less perfect solution, there might still be shortcuts!
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Alex Colville
Alex Colville@AlexJColville·
@KejunYing I really hope not re: needing a solution as complex as aging… but you may indeed be right! I think that’s what is so mentally seductive about replacement with cell and tissue therapies but comes with its own host of issues.
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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
@AlexJColville Thank you Alex! I think it is inavoidable -- if you want to completely solve aging, we will need a solution at least as complex as aging itself. We can start building it piece by piece but it is important to acknowledge that there might be no general compact/simple solution.
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Alex Colville
Alex Colville@AlexJColville·
@KejunYing Great piece!! Spot on re: complexity. With protein design, I worry about the same complexity though. How long would it take to design a protein for every damage associated with aging? Or is goal to focus on a select few primary drivers?
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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
This is why I think partial reprogramming, iPSC-based approaches, and pharmaceutical interventions for aging are all searching for a short patch to a high-complexity problem -- they might help at the margins, but I don't believe it can fundamentally solve it.
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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
This framing maps onto biology. DNA is a 4MB compressed program that decompresses into 37 trillion cells. The organism is low-complexity. Aging is where complexity explodes: a compact system running in an incompressible environment for decades. The damage has no compact description, so there is no compact fix. You need new biology that evolution never built.
Jonathan Gorard@getjonwithit

I think one of the conclusions we should draw from the tremendous success of LLMs is how much of human knowledge and society exists at very low levels of Kolmogorov complexity. We are entering an era where the minimal representation of a human cultural artifact... (1/12)

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Dr. Rhonda Patrick
Dr. Rhonda Patrick@foundmyfitness·
Instead of "healthspan," we should be thinking about "Peakspan." How long can you maintain ~90% of your peak physical or cognitive function? According to a new paper, different systems reach their “Peakspan” at very different times. Fluid cognitive abilities like processing speed and working memory peak early, around ages 20–30, while crystallized intelligence doesn’t peak until the late 40s or early 50s and can remain stable into the 70s. Cardiorespiratory fitness peaks from adolescence to the mid-20s and then declines steadily, while muscle strength peaks in early adulthood and falls sharply after 60. Bone density, kidney function, hormone levels, sensory function, immunity, digestion, and reproductive capacity all follow their own trajectories too—some peaking in the 20s, others in the 40s or 50s. In other words, human aging is asynchronous. We don’t simply age “overall,” but instead age system by system.
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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
Excited to share our new paper on Peakspan. If healthspan is the broad measure, Peakspan is its extreme phenotype: the state where at least 90% of capacity is retained. Similar framing to extreme longevity versus lifespan. Great working with Dominika and Alex on this one.
Agingdoc🩺Dr David Barzilai🔔MD PhD MS MBA DipABLM@agingdoc1

Peakspan: Defining, Quantifying and Extending the Boundaries of Peak Productive Lifespan @biogerontology @KejunYing @DomiWilczok aginganddisease.org/EN/10.14336/AD…

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Charles Wu 吴英成AI🦞
Charles Wu 吴英成AI🦞@Charles_Y_Wu·
The future of the lab is here! 🚀 So proud to be part of this mission with my mentor @lecong. Thank you for leading the way! LabOS is redefining the wet lab, and together with MedOS, we are ready to bring this AI revolution to the clinic. This is just the beginning!
CL • Le Cong@lecong

Wet lab hasn't changed in 50 years - we are changing that. As written by legendary @denibechard on @sciam LabOS is powering agentic lab via smart glasses, multimodal AI, and collaborating robotics, real-time guiding human scientists and training junior scientists to expert-level in 1 week for complex gene-editing experiments. #ai4science in action!🔬 With LabOS and the sister project MedOS, we hope to turn every lab and clinic into AI-perceivable, AI-operable environment. Not to replace humans — to make us better! scientificamerican.com/article/how-la…

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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
Excited to be part of this team! How lab work is done in academia is about to change fundamentally. LabOS is a real step toward that.
CL • Le Cong@lecong

Wet lab hasn't changed in 50 years - we are changing that. As written by legendary @denibechard on @sciam LabOS is powering agentic lab via smart glasses, multimodal AI, and collaborating robotics, real-time guiding human scientists and training junior scientists to expert-level in 1 week for complex gene-editing experiments. #ai4science in action!🔬 With LabOS and the sister project MedOS, we hope to turn every lab and clinic into AI-perceivable, AI-operable environment. Not to replace humans — to make us better! scientificamerican.com/article/how-la…

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Weidi Xie
Weidi Xie@WeidiXie·
Glad to share DeepRare, published on @Nature ! nature.com/articles/s4158… Super grateful for the news & views from Prof. Timo Lassmann, it really gives the best views on DeepRare. nature.com/articles/d4158… This is the first agentic system of its kind, designed to solve the complex puzzle of rare disease diagnosis. ✅ Outperforms current best methods by ~24% ✅ 95.4% expert agreement on reasoning chains ✅ Handles clinical notes, HPO terms & genetic data
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Cory J Smith 🧬
Cory J Smith 🧬@GalaxyBalanceHQ·
Teaser drop: Decoding Aging w/ @KejunYing coming next week! Albert nails it: 'Doing science is fighting with nature... but you also fight humans to get funding. Academia has limits, need policy warriors too.' Raw take on the real barriers behind breakthroughs in AI, proteins, & longevity. Full ep drops Tuesday Feb 17, save the date! Watch teaser now #Longevity #AIAging #SyntheticBiology @wysscoraylab @davidbaker200 @aubreydegrey
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Kejun (Albert) Ying
Kejun (Albert) Ying@KejunYing·
I started writing blog posts for ideas that don't fit into papers. First one: what "research taste" actually means. I think of it as a search problem. Imagine all possible hypotheses and analyses as a graph. Taste is finding the node that affects the largest number of other nodes. The hard part is recognizing when new technology rewires the topology. CRISPR is the cleanest example. For decades it was a bacterial immune system, a contained node in microbiology. Then Doudna and Charpentier showed it could edit DNA programmably. That single move connected to almost everything else. Full post: kejunying.com/blog/research-…
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Aging Feed
Aging Feed@AgingFeed·
Integrative epigenetics and transcriptomics identify aging genes in human blood nature.com/articles/s4146…
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