As a student, conducting research and writing a thesis is a long journey. It might be helpful to hear from folks who just completed the long journey.

To help students in the MICCAI community prepare for their PhD research and thesis writing, the MICCAI Student Board (MSB) is happy to announce the fourth Panel Discussion in the MSB Webinar Series 2024.

The webinar will feature seven finalists of PhD Thesis Madness organized by the MICCAI Student Board at MICCAI 2024, who are final-year PhD students or recently finished doctorates. They will share with us their PhD journey, from identifying the thesis topic to conducting thesis research to creating a thesis plan and writing it. So please mark your calendars and join us for this interactive session.

We have seven amazing panelists who are the finalists of PhD Thesis Madness, organized by the MICCAI Student Board at MICCAI 2024. 

• Cosmin I. Bercea is currently a postdoctoral researcher at the Technical University of Munich (TUM) working with Prof. Julia Schnabel and Prof. Benedikt Wiestler. He focuses on vision & multimodal learning in medicine, with the aim to learn conditional distributions for various downstream medical applications such as counterfactual synthesis, anomaly detection, and visual question-answering. 

• Meirui Jiang received his Ph.D. degree from the Department of Computer Science and Engineering, The Chinese University of Hong Kong, supervised by Prof. Qi Dou. His research centers around the intersection of machine learning and healthcare, with a particular focus on medical image analysis. He is driven by a strong desire to enhance the applicability of machine learning algorithms, with a focus on their robustness, efficiency, generalizability, reliability, and privacy. In particular, he is passionate about leveraging large distributed datasets to empower real-world applications in the healthcare domain. He has published 18 papers in top-tier conferences and journals, including Nature Communications, NEJM AI, TMI, ICLR, CVPR, AAAI, MICCAI, etc. 

• John Kalkhof, a PhD candidate at TU Darmstadt and part of the MEC-Lab under Anirban Mukhopadhyay's guidance, is committed to advancing affordable healthcare AI to expand access to care. Initially exploring feature disentanglement during his Master's, his current research focuses on lightweight and robust segmentation methods through Neural Cellular Automata. His work was recognized at IPMI 2023 with the Francois Erbsmann Prize and the MICCAI 2023 Young Scientist Award.

• Sasan Matinfar is currently pursuing a Ph.D. in Computer Science at the Technical University of Munich (TUM), supervised by Prof. Nassir Navab. His research focuses on transforming medical data into auditory feedback systems that enhance surgical procedures. His work earned him the prestigious Young Scientist Award at MICCAI 2017 and a Best Paper nomination at MICCAI 2023. In addition, Matinfar holds a background in musicology and piano interpretation from the University of Music Franz Liszt Weimar and the Art University of Tehran, which enriches his interdisciplinary approach to his research. He has published his research in leading journals and conferences, including Nature Scientific Reports, IEEE-TVCG, MICCAI, SMC, and IJCARS. 

• Balamurali Murugesan is pursuing his Ph.D. at École de technologie supérieure (ETS), Montreal in Laboratory of Imaging, Vision and Artificial Intelligence (LIVIA) under Dr. Jose Dolz and Dr. Ismail Ben Ayed. He is currently working on applying deep learning to computer vision and medical image analysis.  Earlier, he completed his master's thesis on accelerating MRI reconstruction. He has published 25+ research articles in renowned venues including MICCAI,  CMIG, MedIA, ISBI, MIDL, SPIE, and EMBC. 

• Julian Suk received his M.Sc. in Applied Mathematics in 2020 at the Technical University of Munich (Germany). Currently, he is a fourth-year Ph.D. student at the Department of Applied Mathematics of the University of Twente, (Enschede, The Netherlands). During his PhD, he focusses on geometric deep learning for 3D vascular hemodynamics and beyond. His research interests are deep learning, group theory and partial differential equations which he likes to combine in the field of scientific machine learning for 3D medical data.

• Puxun Tu is a fourth-year PhD student at the School of Mechanical Engineering, Shanghai Jiao Tong University, China, under the supervision of Prof. Xiaojun Chen. His research focuses on augmented reality and surgical AI. He won a funding for PhD student from Natural Science Foundation of China (NSFC) in 2024. He was a visiting PhD student at the PRIA Lab led by Prof. Xiaoyi Jiang at the University of Münster, Germany in 2023, and at the i4health Lab led by Prof. Gary Zhang at University College London, UK in 2024. He is with authorship of over 15 papers, including IEEE TBME/TMI/TVCG, Information Fusion, IJCARS, MICCAI, etc.