Can AI tell us when it can predict better than humans and when humans are better? In our AISTATS23 (oral) with MIT-IBM Watson AI Lab paper we build AI models that 1) complement humans and 2) can defer to humans when necessary arxiv.org/abs/2301.06197

"And then we just cancel this infinity with that one and we get the answer"

1. 💡The #MSCA play a crucial role in supporting the EU’s steps towards #genderequality in #research and #innovation. ♀️ On the international #GirlsinICT Day, we celebrate the talent of our women #researchers & creators. Read more: europa.eu/!QT6gfp See thread👇

Statistical modeling from first principle. But with memes and GIFs. Let's start.

Excited to be travelling to Nashville for #MIDL2023 to showcase our work on #multimodal #ContrastiveLearning for #retinalimaging. Come by for a chat during the poster sessions tomorrow! Short paper 👇 openreview.net/forum?id=newla…

Another bias: If you're a non-native English writer, GPT detectors will frequently classify your writing as #AI-generated cell.com/patterns/fullt… James Zou Weixin Liang Stanford University

And on the last day of #MIDL2023 we can hear a series of talks about #segmentation and #ComputerAidedDiagnosis. Tune in 👇 youtu.be/6T5TCBJaYl4

After very rich three-days program at #MIDL2023 there's more to come: #MONAI workshop on #DeepLearning for #MedicalImaging 📍 Online and in-person 📅 Thursday 13 July, 9:00-13:30 (Nashville time, UTC-5) 🌐monaimidlmeetup.splashthat.com

📢 Exciting News! 📚 Our article on AI-driven patient functionality monitoring using #digitalphenotyping is finally out! 🌐Discover how we harness multimodal data for accurate evaluations📊 Read on ScienceDirect: sciencedirect.com/science/articl…

A list explaining key ML concepts and papers: github.com/dair-ai/ML-Pap… v/elvis

📢New #MLFPM publication: Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study By Emese Sükei, Antonio Artés doi.org/10.2196/47167

COMBINATORIAL OPTIMIZATION with DIFFUSION MODELS. presented at #ICML2024 this week!

🚀 Published in Scientific Reports: Our multimodal #ContrastiveLearning study uses 2D & 3D retinal images to enhance prediction accuracy in #Ophthalmology. It also shows that lower-cost fundus imaging can still deliver clinical insights. Learn more! 👀🔍 🔗rdcu.be/dZa2N

Excited to present our work on 3D retinal OCT modeling with xLSTM at #ISBI2025 today! xLSTM captures spatial-temporal structure across OCT volumes—pushing beyond slice-based models for better retinal disease analysis. Come find me at the #poster session!