Are you curious about the secret behind high-rated MICCAI papers? Ever wondered why some papers consistently receive great reviews while others struggle? How can you transform your ideas and experiment results into a stronger submission?

The MICCAI Student Board is excited to invite you to the first Panel Discussion of the MSB Webinar Series 2026. Join us on Wednesday, January 21, 2026, for an interactive session featuring valuable insights into the MICCAI paper writing process, firsthand experiences, from a MICCAI Best Paper Award Recipient and Outstanding Area Chairs.

This webinar is especially tailored for students and early-career researchers aiming for successful submissions to MICCAI. Don’t miss your chance to learn directly from our experienced panelists and elevate your research submission strategy!

Registration is free and open to all!

Angelica Aviles-Rivero is an Assistant Professor at the Yau Mathematical Sciences Center, Tsinghua University. Previously, she was a Senior Research Associate at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. Her research lies at the intersection of applied mathematics and machine learning, focusing on developing data-driven algorithmic techniques that enable computers to extract high-level understanding from vast datasets.  Recognitions include an outstanding paper award (ICML 2020) and elected officer (SIAM SIAG/IS 2022). She is also a member of the organising committee for MICCAI 2026, and is the General Co-Chair for the International Symposium on Biomedical Imaging (ISBI) in 2026. For more information visit: https://angelicaiaviles.wordpress.com/

Fuyong Xing is an Associate Professor in the Department of Biostatistics and Informatics at the University of Colorado Anschutz Medical Campus. He received his Ph.D. in Electrical and Computer Engineering at the University of Florida. His research focuses on machine/deep learning for healthcare, particularly medical image computing and imaging informatics. He has published over 80 peer-reviewed articles including MICCAI, and has received multiple NIH grants as the PI.

Kushal Vyas is a PhD student at Rice University working on novel machine learning methods and data representations for medical and scientific imaging, alongside research in machine learning for large signals. His work includes implicit neural representations of signals, and he was recently awarded the MICCAI Best Paper Award for developing neural fields that jointly enable signal representation and segmentation. Prior to his PhD, he worked at Samsung Research America on machine learning and computational photography, and earned his master’s degree from Carnegie Mellon University. He has authored multiple papers in top-tier venues and is an inventor on several patents.