Three is a magic number: Professor Su-In Lee earns trio of honors, including the “Korean Nobel Prize” in engineering, for advancing AI for biomedicine

Congratulations to John Clarke, Michel Devoret and John Martinis on receiving the 2025 Nobel Prize in Physics! https://t.co/lWb1iMyxJC I have fond memories of my time in the Clarke lab, where I did my Honors Thesis on ultra low-field MRI w/ SQUIDs as an undergrad at UC Berkeley!

#RECOMB2026 is now accepting submissions and we'd love to see your best work! 📌 Abstract registration: Nov 7, 2025 📌 Full paper submission: Nov 14, 2025 📜More info:

🎉 Excited to share: our paper CellCLIP – Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning has been accepted at #NeurIPS2025! 📄 Paper: https://t.co/WuQbRp4LPj 🌐 Project page: https://t.co/S6jceXVwiP What is CellCLIP? 👇

📢🌟Thrilled to announce that I will be joining 🦉 @RiceUniversity @RiceECE @RiceEngineering as an Assistant Professor in Jan 2026! My group will focus on AI for Biomedicine: aging & age-related disease, foundation models & XAI. Details 👉

Medical #AI errors “can directly impact people’s health and even determine life-altering outcomes.” In @natrevbioeng, @UW #UWAllen’s @ChanwooKim, @soham_gadgil & @suinleelab emphasized the importance of transparency in medical models. #ResearchMakesAmerica

Unveiling Transparency in Medical AI Systems

Some technical delays. But we are underway! First talk by Alexis battle!

🚀 We’re at the tipping point of the AI era, and nowhere is its potential more profound than in medicine 💉🧬. But with great promise comes great responsibility. That is, ensuring transparency and safety of medical AI is crucial to its successful adoption.

📖 Read our full paper here:  https://t.co/jr2uZTFWiM Grateful to the amazing team @soham_gadgil @suinleelab @uwcse who made this possible! Many thanks to @ChristineHorejs for positive and constructive feedback!

Q&A: Transparency in medical AI systems is vital, UW researchers say

Nothing more fun than working with brilliant students @uwcse @ChanwooKim_ & @soham_gadgil on our Nature Reviews bioengineering paper!🎉We review challenges & opportunities for making medical AI trustworthy through transparency in data, models & deployment. https://t.co/ZZBdtgixVz

🚀 We’re hiring! Multiple postdocs & a program manager to push the frontiers of explainable AI in cutting-edge biomedical research—Alzheimer’s, aging, cancer & medical AI. Start immediately. Friends, please RT🙏 Learn more: https://t.co/pAR8O6EKNP #postdocjobs #AI #Biomedicine

Final call for posters @KeystoneSymp on #AI in molecular #biology Deadline: August 21, 2025 Beyond scientific talks, our program also includes panel discussions to address challenges + opportunities for innovation + responsibility in AI-driven biology:

@jengreitz & I are looking to hire a computational biologist/biostatistician with project management expertise to collaborate with our teams to map the regulatory code of the human genome and discover genetic mechanisms of disease. Details & application in next post 1/

🌹Meet #ROSIE, our new AI that predicts spatial multi-protein expression straight from routine H&E images. Built on a large co‑staining dataset (>1k samples, 16M+ cells). It enables cell phenotyping & tissue structure insights w/o extra assays.

These findings provide robust evidence supporting the importance of the built environment in directly improving health-enhancing #physicalactivity and offer potential guidance for public policy activities in this area. https://t.co/7gJEfKTdP7

Congratulations to us! 🎉🥳 I'm sorry I couldn't make it to ISMB this year—really wish I could have been there to celebrate in person!

Our work on "Evaluating the representational power of pre-trained DNA language models for regulatory genomics" led by @AmberZqt with help from @NiraliSomia & @stevenyuyy is finally published in Genome Biology! Check it out! https://t.co/AFBC9Qu4x3

“韓 파운데이션 모델에도 ‘설명 가능한 AI’가 필요” | 중앙일보

.@suinleelab's work in explainable AI is providing a better understanding of the inner workings of deep neural networks that can be applied to new biological discoveries + insights from complex datasets 📸Mark Stone