How robust are the solutions to ℓp -regularized linear inverse problems? For p=1.5, they live in the set in the picture, did you know? Read on to find out more.

Big personal news: I will be moving to University of Zurich to setup the BioVisionCenter! Having the opportunity to coordinate research in bioimage analysis and push for the development of standardized open-source resources at home is a dream come true! 🎉🔬

🙌🏼Congratulations to Jonathan Dong (Jonathan Dong ) from Biomedical Imaging Group (BIG) for winning a Swiss National Science Foundation Ambizione grant which supports outstanding postdoctoral researchers who wish to conduct, manage and lead their own independent research project. EPFL Research Office actu.epfl.ch/news/six-ambiz…

🎉 Today marks the official starting day of the new @epfl_en Minor in Imaging! A broad array of courses is available to students, from sensors, cameras and optical systems to image analysis, computer vision and AI! Good luck making your picks 😉 actu.epfl.ch/news/epfl-intr…

Very happy to present our new 2D-SIM reconstruction method : FlexSIM !😀🔬 Paper : biorxiv.org/content/10.110… Code : github.com/esoubies

FlexSIM reconstruction of actin network (gray), microtubules (red), and clathrin (blue) in COS- 7 cells imaged in TIRF-SIM mode. #FluorescenceFriday

🚀 Exciting News! Join us at the Applied Machine Learning Days at EPFL, Lausanne, Switzerland on March 26 for an enlightening Poster Session in the "AI & Physics" track! 🤖⚛️ Explore the rich history and recent breakthroughs where Artificial Intelligence and Physics collide. 1/3

📊🔬 Wonderful discussions yesterday around data management in #imaging at our joint event with Bits in Bio Lausanne! A big thank you to Joel Lüthi, Arianne Bercowsky, and Rémy Dornier for sharing their expertise and insights, and to Thomas Vetterli for making it happen so smoothly! 🙌

🎉 A new year, a new website for the EPFL Center for Imaging! Click on the link below to explore cutting-edge research and resources in #imaging at EPFL🔬📸💻🔭🛰️ Dive in and discover the future of imaging science across scales: imaging.epfl.ch

Currently at Photonics West in San Francisco, my first big conference since Covid. Ping me if you're around too!

⏰ First Imaging Seminar of the year on February 1: "Unique X-ray microscopy capabilities at SLS2.0: dynamic tomographic imaging and beyond" by Marco Stampanoni from PSI Paul Scherrer Institut More info: lnkd.in/dFKa8dtU Registration: lnkd.in/eQyM2HJZ

🤔 How to design your #imaging experiment? Where to acquire images at EPFL or get help for their analysis? What about learning new skills? Today, Laurène Donati & Edward Andò gave Materials Science & Engineering @EPFL scientists an overview of the resources available at EPFL for doing top imaging.

'Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions', by Stanislas Ducotterd et al. jmlr.org/papers/v25/22-… #regularization #optimization #unconstrained

Inverse Problems: Discrete or Continuous? Mehrsa et al. from Biomedical Imaging Group (BIG) explain that box splines and multi-res bridge the gap: "A Box-Spline Framework for Inverse Problems with Continuous-Domain Sparsity Constraints” On early access: ieeexplore.ieee.org/document/10533…

I am excited to share that I will be joining the ECE department UC San Diego as an assistant professor this fall! I would particularly like to thank Rob Nowak for all his support since the beginning of my Ph.D. and Michael Unser (Biomedical Imaging Group (BIG)) for being a great post-doc supervisor.

Congrats Rahul! 🎉 We wish you the best for the rest of your academic journey from BIG!

We are thrilled to announce SplineOps! 🚀 An open-source library unlocking decades of pioneering research on spline algorithms developed by the Biomedical Imaging Group. Check it out! 🤩 Here: splineops.github.io

An exciting opportunity to help shape the future of cancer research through AI and HPC! 📥 Apply today or share with your network! More details and application instructions 👉 cnio.es/empleo/researc… Oscar Llorca

'Random ReLU Neural Networks as Non-Gaussian Processes', by Rahul Parhi, Pakshal Bohra, Ayoub El Biari, Mehrsa Pourya, Michael Unser. jmlr.org/papers/v26/24-… #stochastic #autocovariance #gaussian

CPAL is excited to have an amazing set of Keynote speakers representing sparse computing, optimization, foundations of AI, neuroscience, psychology, and decision making Alison Gopnik Kording Lab 🦖 Jason Lee Andrea Montanari Yuandong Tian Biomedical Imaging Group (BIG) Doris Tsao Richard Baraniuk Fredrik Kjolstad