
Qingyu Zhao
@qingyuzhaowcm
Assistant Professor of Artificial Intelligence @WeillCornell Radiology
Machine Intelligence in Neuroimaging 🤖🧠
ID: 1679603291435728896
https://mini-cornell.github.io/ 13-07-2023 21:27:03
19 Tweet
49 Followers
54 Following

Grateful for our amazing collaborators Qingyu Zhao, Kathleen L Poston, Kilian Pohl, and @EhsanAdeli, whose expertise in #Neuroscience, #AI, #ParkinsonsDisease, & more made xGW-GAT possible! Thanks to Wu Tsai Neurosciences Institute for funding & PyG + others for open-source GNN contributions. N/N

Annnnd we're back! 🕺 💃 🎉 Join us this Friday, Oct 6th at 10am EST for our first seminar of the Machine Learning in Medicine Fall 2023 Series with Anqi Wu (@anqiwu.bsky.social)! "Understanding the Brain Using Interpretable Machine Learning Models" weillcornell.zoom.us/j/92581271707 Passcode: 437063



Are you a PhD researcher in the field of computational medical imaging? Are you looking for faculty jobs? We, at Weill Cornell Medicine Radiology, are hiring. Please read on. 🧵1/



Our clinical #NLP work just published in Nature Medicine! We present a framework to adapt & evaluate #LLMs for summarization. Physicians 🩺 prefer #LLM summaries to those of #medical experts❗ Big step to reduce documentation 📚 and focus more on personalized care 🙌 A 🧵


Faculty Search: Do you have a PhD with a research focus in AI/machine learning for medical imaging and are looking for a faculty job? We are recruiting Assistant Professors Cornell University, in New York City. Come join us: academicjobsonline.org/ajo/jobs/28548





Congrats to Yixin Wang, Wei Peng, Yu Zhang, PhD , Ehsan Adeli, Kilian Pohl. Thank Camila González for presenting it!

🧠 Read "Sex-specific differences in #brain activity dynamics of youth with a family history of #SubstanceUseDisorder" a bioRxiv by L Schilling, Parker Singleton, @crntozlu, M Hédo, Qingyu Zhao, K Jamison, and Amy Kuceyeski | @[email protected] 👉 tinyurl.com/2s4cjw9p


Attending ICML Conference and interested in confounder-free learning? Check out our work "Confounder-Free Continual Learning via Recursive Feature Normalization", where we introduce the flexible R-MDN layer for on-the-fly confounder removal from continuous data streams! 🕟 Thu 17 Jul
