Event
Listen to world-leading experts in imaging
About
The EPFL Seminar Series in Imaging
Imaging plays a central and ever-increasing role in science and engineering. From the nano to the macro scale, it allows us to capture, quantify, and visualize physical phenomena with unprecedented resolution in both space and time.
It is also the interdisciplinary discipline par excellence. From sample preparation to optical design and image processing, imaging workflows nowadays require the convergence of numerous skills and expertise.
Mindful of this “imaging sweet-spot”, the EPFL Center for Imaging aims at bringing together the best from worldwide experts in imaging through a series of high-visibility talks with interdisciplinary appeal.
September 23, 2024
—
September 29, 2024
The Scientific Images Exhibition 2024
Do you have a passion for unraveling and witnessing the breathtaking beauty of scientific elements and phenomena? If so, we invite you to share your scientific vision through the lens of your camera, microscope, telescope, computer, etc.!
What's next
Upcoming seminars
September 23, 2024
—
September 29, 2024
The Scientific Images Exhibition 2024
Do you have a passion for unraveling and witnessing the breathtaking beauty of scientific elements and phenomena? If so, we invite you to share your scientific vision through the lens of your camera, microscope, telescope, computer, etc.!
Past seminars
Explore our previous events
Review our former seminars and watch recordings if available.
Quantifying Quality in Fluorescence Microscopy
Prof. Siân Culley
SV 1717
Revolutionizing Cellular Imaging: Harnessing Label-Free Flow Cyto-Tomography for Advanced Suspended Cell Analysis
Prof. Pietro Ferraro
IMM CNR, Italy
Diffusion Models for Computational Imaging Problems
Prof. Jong Chul Yue
Exploring and Explaining: Leveraging data visualization for research, communication and public health
Prof. Helena Jambor
University of Applied Sciences of the Grisons (CH)
Content Aware Image Restoration for Light and Electron Microscopy
By Florian Jug
Max Planck Institute, Dresden, Germany
On Instabilities, Paradoxes and Barriers in Deep Learning
By Prof. Anders Hansen
University of Cambridge, UK
Super-Resolution in Diffraction Microscopy
By Anne Sentenac
Institut Fresnel, France
Reflection Matrix Approaches for Imaging
By Mathias Fink
ESPCI Paris, France
On Micro and Nano Imaging ghcmcm
By Prof. Jacques Dubochet
Nobel Laureate in Chemistry, 2017
Ultrastructural Expansion Microscopy
By Prof. Paul Guichard
University of Geneva, Switzerland
Video Understanding and Robotics Manipulation (AMLD 2020)
By Cordelia Schmid
INRIA, France
Imaging: From Compressed Sensing to Deep Learning
By Prof. Yonina Eldar
Weizmann Institute of Science, Israël
Structural Analysis with Cryo-Transmission Electron Microscopy
By Prof. Henning Stahlberg
University of Basel, Switzerland
Functional Ultrasound Imaging gcmbvc
By Prof. Mickael Tanter
INSERM and ESPCI Paris, France
Shedding Light on Tumour Evolution
By Prof. Sarah Bohndiek
University of Cambridge, UK
Machine Learning for Bioimage Informatics (AMLD 2020)
By Virginie Uhlmann
EMBL-EBI, UK
Imaging the Unseen: Taking the First Picture of a Black Hole
By Katie Bouman
Caltech, USA
Reconstruction of Cryo-EM Images of Proteins at Atomic Resolution
By Dr. Sjors Scheres
MRC Laboratory of Molecular Biology, Cambridge
Imaging: Intelligence on the Nanoscale
By Prof. Gabriel Aeppli
Paul Scherrer Institute, Switzerland
End-to-end Learning for Computational Microscopy
By Dr. Laura WALLER
Computational Imaging Lab, UC Berkeley
Imaging the Planet for a Sustainable Future
By Prof. Gilberto Camara
Group on Earth Observations (GEO)
3D Imaging of Cells by FIBSEM with Correlation to cryoFLM
By Prof. Harald Hess
HHMI’s Janelia Research Campus, USA
Beyond the First Portrait of a Black Hole
By Prof. Katie Bouman
California Institute of Technology, USA
Mapping 3D Nanostructures with X-Ray Ptychography
By Dr. Manuel Guizar-Sicairos
Paul Scherrer Institut, Switzerland Copyright
Modeling Deep Networks: Network Learning for Image Processing
By Prof. Stéphane Mallat
Collège de France
Integrating Physical and Learned Models
By Prof. Ulugbek Kamilov
Washington University in St. Louis
Joint Optimization of Learning-Based Image Reconstruction and K-Space Trajectories for MRI
By Prof. Jeff Fessler
University of Michigan
Imaging using X-ray scattering Contrast to Bridge the Nano and Macroscale
By Prof. Marianne Liebi
EPFL and Paul Scherrer Institute
From differential equations to deep learning for inverse imaging problems
By Prof. Carola Bibiane Schönlieb
University of Cambridge
Generative AI, Stable Diffusion, and the Revolution in Visual Synthesis
By Prof. Bjorn Ommer
LMU of Munich
Scanning Ion Conductance Microscopy
By Prof. A. Radenovic & G.Fantner
EPFL
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging
Prof. Thomas Pock
Graz University of Technology
Future of Bioimaging: Next Generation Instruments & Artificial Intelligence
Prof. Loïc Royer
Institut Curie
Normalizing Flows and the Power of Patches in Inverse Problems
Prof. Gabriele Steidl
TU Berlin
Simultaneous 3D imaging in Biology with Multifocus Microscopy
Prof. Sara Abrahamsson
University of California Santa Cruz (US)
Deep Learning-enabled Computational Microscopy and Diffractive Imaging
By Prof. Aydogan Ozcan
UCLA
Visualizing mechanical properties in biology using Brillouin microscopy
Dr. Robert Prevedel
European Molecular Biology Laboratory
Correlative cryoSEM-CryoNanoSIMS mapping of vitrified biological tissue
By. Dr. Priya Ramakrishna
EPFL
Model-Based Machine Learning for Inverse Problems in Imaging
Prof. Pier Luigi Dragotti
Imperial College London
Generalized locality for lightweight, robust AI-driven imaging
Prof. Ivan Dokmanić
University of Basel
Unique X-ray microscopy capabilities at SLS2.0: dynamic tomographic imaging and beyond
Prof. Marco Stampanoni
Paul Scherrer Institut
Contact
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Contact us if you have any questions or require support in the field of imaging.