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Luc Duchesne

Luc Duchesne is the Director of the Medical Imaging Department at Microwave Vision (MVG). He studied electrical engineering and received a master’s degree in aerospace electronics in 1994 from Supaero, Toulouse, France. He began his professional career in 1994 at Deutsche Aerospace in Munich (now Airbus Defense and Space) as a radio frequency and antenna R&D engineer. During his experience in Germany until July 2000, he participated in the development of antenna payloads and RF front ends for satellite applications and led various research and development projects. In August 2000, he joined SATIMO near Paris (now Microwave Vision) as R&D Director. From 2000 to 2018, he participated in the development of innovative products for the core business of antenna measurement systems. From 2013, he has been increasingly involved in the development of the Wavelia microwave breast imaging scanner (www.wavelia.com) and since 2018, he has participated fully in these activities with the objective of bringing this new, non-ionizing and safe imaging modality to the market.

Katherine Ferrara

Katherine Ferrara, Ph.D., is a Professor of Radiology and the Division Chief for the Molecular Imaging Program at Stanford. She is a member of the National Academy of Engineering and a fellow of the IEEE, AAAS, the Biomedical Engineering Society, the World Molecular Imaging Society, the Acoustical Society of America and AIMBE. Following an appointment as an Associate Professor in the Department of Biomedical Engineering at the University of Virginia, Charlottesville, Dr. Ferrara served as the founding chair of the Department of Biomedical Engineering at UC Davis. Dr. Ferrara is known for work in the development of contrast agents and molecular imaging techniques and instrumentation. She has received the WMIS Gold Medal, IEEE Biomedical Engineering Award, IEEE Achievement Award and IEEE Rayleigh Award.

Stefanos Kollias

Stefanos Kollias, FIEEE, FHEA, has been Professor in the School of Electrical and Computer Engineering of the National Technical University of Athens, since 1997. He has also been Professor of Machine Learning in the School of Computer Science of the University of Lincoln, UK. He has been Chairman of the Greek National Infrastructures for Research and Technology, since 2019, assisting the Hellenic Ministry of Digital Governance in the digital transformation of public administration. He has also been member of the Executive Committee of the European Neural Network Society, 2007-2016; member of the Member States Expert Group on Digitization and Digital Preservation, 2007-2016; member of the European Commission Expert Group on Cultural Heritage (responsible for AI), since 2019. His research covers machine/deep learning and artificial intelligence, multimedia analysis, search, retrieval and recognition, and their application in vision, healthcare and medical imaging, cultural heritage, human computer interaction, agri-food, industrial monitoring and anomaly prediction. He has published 110 journal and 310 conference papers. His work has attracted 14,450 citations and h-index 58 in Google scholar. He has supervised 48 Ph.D. students. He has led his Group participation in more than 100 European research projects, with funding of more than 20 M euro.

Andrew F. Laine
Andrew F. Laine received his D.Sc. degree from Washington University (St. Louis) School of Engineering and Applied Science in Computer Science, in 1989 and BS degree from Cornell University (Ithaca, NY). He was a Professor in the Department of Computer and Information Sciences and Engineering at the University of Florida (Gainesville, FL) from 1990-1997. He joined the Department of Biomedical Engineering in 1997 and served as Vice Chair of the Department of Biomedical Engineering at Columbia University since 2003 – 2011, and Chair of the Department of Biomedical Engineering (2012 – 2017). He is currently Director of the Heffner Biomedical Imaging at Columbia University and the Percy K. and Vida L. W. Hudson Professor of Biomedical Engineering and Professor of Radiology (Physics). Dr Laine is also Co-Chair of Columbia’s Center for Health Analytics in the Data Science Institute. He has served on the program committee for the IEEE-EMBS Workshop on Wavelet Applications in Medicine in 1994, 1998, 1999, and 2004. He was the founding chair of the SPIE conference on “Mathematical Imaging: Wavelet Application in Signal and Image Processing” and served as co-chair during the years 1993-2003. Dr. Laine has served as Chair of Technical Committee (TC-BIIP) on Biomedical Imaging and Image Processing for IEEE EMBS 2004-2009 and has been a member of the TC of IEEE Signal Processing Society, TC-BISP (Biomedical Imaging and Signal Processing) 2003-present. Professor Laine served on the IEEE ISBI (International Symposium on Biomedical Imaging) steering committee, 2006-2009 and 2009 – 2012. He was the Program Chair for the IEEE EMBS annual conference in 2006 held in New York City and served as Program Co-Chair for IEEE ISBI in 2008 (Paris, France). He served as Area Editor for IEEE Reviews in BME in Biomedical Imaging since 2007-2013. He was Program Chair for the EMBS annual conference for 2011 (Boston, MA). Professor Laine Chaired the Steering committee for IEEE ISBI, 2011-2013, and Chaired the Council of Societies for AIMBE (American Institute for Medical and Biological Engineers). He was the General Co-Chair for IEEE ISBI in 2022. Finally, he served as the IEEE EMBS Vice President of Publications 2008 – 2012 and was the President of IEEE EMBS (Engineering in Biology and Medicine Society) 2015 and 2016. He currently serves as the Chair of the Membership Committee for IAMBE (International Academy of Medical and Biological Engineers). He is a Fellow of IEEE, AIMBE and IFMBE.

María J. Ledesma-Carbayo

María J. Ledesma-Carbayo (Senior Member, IEEE) received the M.Eng. degree in telecommunication engineering, with a master’s thesis on medical image analysis, and the Ph.D. degree (Hons.) from the Universidad Politécnica de Madrid, in 1998 and 2003, respectively. She additionally completed two different master’s programs, such as Biomedical Engineering and Medical Physics from the University of Patras, Greece; and Bioengineering from UNED, Spain. She is currently a Full Professor with the Biomedical Image Technologies Laboratory, Universidad Politécnica de Madrid. She has authored or coauthored over 100 publications in indexed journals and conferences. Her main research interests include biomedical image analysis, especially cardiac and pulmonary imaging, image-guided therapy, microscopy image analysis, registration, and motion estimation and compensation.

Natasha Lepore

Natasha Lepore is a faculty at the University of Southern California and Children’s Hospital Los Angeles, where she leads the Computational Imaging of Brain Organization Research Group (CIBORG) laboratory. Her lab specializes in mathematical and numerical methods to study brain anatomy and function through magnetic resonance imaging (MRI). These methods are applied to furthering our understanding of different neurological disorders, as well as normal and abnormal brain growth, in both high- and low-income settings. They have also been developing software to automate clinicians’ tasks and provide quantitative assessment of medical images to help in their daily functions. Dr Lepore graduated with a BSc in physics and mathematics from the University of Montreal and a master’s degree in applied mathematics from Cambridge University, in general relativity. Her PhD is in theoretical physics (Harvard University), and deals with quantum chaos in quantum billiards living on the plane, the sphere and the pseudosphere. After graduation, she became a postdoctoral fellow with Prof. Paul Thompson at the Laboratory of Neuro Imaging at UCLA.

Claudia Mazo

Claudia Mazo is an Assistant Professor at Dublin City University. Her research interests include Artificial Intelligence, e-Health, Bioinformatics, and Medical-Data Science. She previously held a Marie Skłodowska-Curie Postdoctoral Fellowship at University College Dublin and Oncomark Ltd, gaining extensive experience in academia and industry. Dr Mazo has authored and co-authored 26 peer-reviewed journal articles and 14 conference articles, which reflect her active contributions to the field (735 citations, h-index 11, i10-index 13 according to Google Scholar 29.04.2024). Dr Mazo has supervised, co-supervised, and mentored undergraduate, Master, and PhD students in Ireland and Colombia, some of whom received distinctions for their outstanding performance and timely project completion. In addition to her academic roles, she is a committee member of the CA22103 Cost Action “A Comprehensive Network Against Brain Cancer (Net4Brain)”, and a member of Global BioImaging, Latin America Bioimaging, and Euro-Bioimaging.

Cauligi (Raghu) Raghavendra

Cauligi (Raghu) Raghavendra is a Professor in the Ming Hsieh Department of Electrical and Computer Engineering and has a joint appointment in the Computer Science Department, and is the Vice Dean for Global Engagement for the Viterbi School of Engineering at the University of Southern California, Los Angeles. He was Chairman of Electrical Engineering-Systems Department from 2003-2005, Senior Associate Dean for Academic Affairs during 2005-2006, and Senior Associate Dean for Strategic Initiatives during 2006-2011. Previously, he was a faculty in the Department of Electrical Engineering-Systems at USC from 1982-1992, as Boeing Chair Professor of Computer Engineering in the School of Electrical Engineering and Computer Science at the Washington State University in Pullman, from 1992-1997, and with The Aerospace Corporation from August 1997-2001. He received the B.Sc (Hons) Physics degree from Bangalore University in 1973, the B.E and M.E degrees in Electronics and Communication from Indian Institute of Science, Bangalore in 1976 and 1978 respectively. He received the Ph.D degree in Computer Science from the University of California at Los Angeles in 1982. Dr. Raghavendra is a recipient of the Presidential Young Investigator Award for 1985 and is a Fellow of the IEEE. He has extensive research experience in parallel and distributed systems, reliability and fault tolerance issues in computer systems, and on routing, multicasting, and protocols for wireless networks.  Recently, he has worked on traffic engineering in data center networks, data driven predictive analytics for oil fields, and applying machine learning for medical data.

Olivier Salvado

Olivier Salvado graduated with a Master in Electrical Engineering from ESIEE Paris and University Paris XII, where he studied signal processing, control system, and artificial intelligence. He worked for several years designing industrial control systems applying advanced techniques to improve automated machine performance. Attracted by medical challenges, he then graduated in 2006 with a PhD in Biomedical Engineering, specialty Medical Imaging, from Case Western Reserve University, Cleveland, OH, USA. His research was on developing machine learning technologies to analyses MRI data. He joined the radiology department of the Cleveland University Hospitals, working on catheter based MRI imaging, before moving to Australia as a research scientist for the CSIRO, focussing on image analysis. Dr Salvado is now a Senior Principal Research Scientist (eq. Professor) leading the AI for Missions initiative, a $25M program to develop next generation AI technology to support large Missions across the CSIRO in collaboration with external partners. Dr Salvado has been the head of CSIRO Imaging and Computer Vision (35+ staff) since 2018. He joined CSIRO in 2007 and grew a medical imaging team from 8 to 20 staff over a decade. Prior to that, he has held various engineering and project management positions in industry. Dr Salvado is a Honorary Professor at Queensland University of Technology and Griffith University. Dr Salvado holds an executive MBA from the University of New South Wales. Dr Salvado has published more than 300 peer-reviewed publications, which have attracted more than 11,000 citations. He regularly contributes to organising international conferences (e.g. ISBI 2018, 2021,2023), chair sessions (ISBI, ICASSP), and was the general Chair of ISBI 2017. Dr Salvado is a regular reviewer for scientific journals and grants including NHMRC and ARC (main Scientific Australian funding bodies).

Li Shen

Li Shen is a Professor of Informatics and the Deputy Director of the Informatics Division in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He holds a secondary appointment in the Department of Radiology, and faculty appointments in the following graduate groups: Applied Math and Computational Science (AMCS), Bioengineering (BioEng), Epidemiology and Biostatistics (GGEB), Genomics and Computational Biology (GCB), and Neuroscience (NGG). He is a Senior Fellow at the Penn Institute for Biomedical Informatics (IBI) and the Penn Leonard Davis Institute of Health Economics. He serves as the Associate Director for Bioinformatics at the IBI, the Faculty Director of the IBI Bioinformatics Core, and Co-Director of the Penn Center For AI And Data Science For Integrated Diagnostics (AI2D). Dr. Shen obtained his Ph.D. degree in Computer Science from Dartmouth College. His research interests include medical image computing, biomedical informatics, machine learning, trustworthy AI, NLP/LLMs, network science, imaging genomics, multi-omics and systems biology, Alzheimer’s disease, and big data science in biomedicine. He has authored over 360 peer-reviewed articles in these fields. His work has been continuously supported by the NIH and NSF. His current research program is focused on developing and applying informatics, computing and data science methods for discovering actionable knowledge from complex biomedical and health data (e.g., genetics, omics, imaging, biomarker, outcome, EHR, health care), with applications to complex disorders such as Alzheimer’s disease. Dr. Shen has served on a variety of scientific journal editorial boards, grant review committees, and organizing committees of professional meetings in medical image computing and biomedical informatics. He served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society between 2016 and 2019. He is a fellow of the American Institute for Medical and Biological Engineering (AIMBE), a distinguished member of the Association for Computing Machinery (ACM), and a distinguished contributor of the IEEE Computer Society.

Oscar Acosta

Oscar Acosta is, since 2009, an Associate Professor at the Signal and Image Processing Laboratory (LTSI-INSERM 1099), University of Rennes 1, France. He obtained an Electrical Engineering degree in 1995 and subsequently a Master of Sciences degree in 1997 in Biomedical Engineering at the University of Andes in Bogotá, Colombia. In 2004, he obtained a PhD degree in France, under the European/Latin American program ALFA BETA Biomedical Engineering Training Action, at the University of Rennes I. Between 2005 and 2009 he worked as a research scientist for CSIRO in Australia. Based first at the Medical Physics department at Westmead Hospital in Sydney and then at the Australian e-Health Research Centre in Brisbane the focus of his research was in medical image processing applied to Alzheimer’s disease. His current research activities are in image processing and computational methods for devising innovative and personalized radiotherapies in Prostate Cancer. This includes prediction of recurrence and toxicities from data-driven models and in silico simulations. He is part of the Organizing Committee of the IEEE EMBS-SPS International Summer School on Biomedical Imaging. He was in 2023 General Chair of the 20th International Symposium on Biomedical Imaging (ISBI) in Cartagena, Colombia. The first ISBI in Latin America

Amir Amini

Amir Amini is Endowed Chair in Bioimaging and Professor of Electrical and Computer Engineering at the University of Louisville. His prior faculty appointments were at Yale and Washington University in St. Louis. He has had leadership roles in organization of numerous conferences in medical imaging and image analysis as scientific program committee member, scientific program chair, as well as conference chair, and was symposium co-chair of SPIE Medical Imaging in 2007 and the IEEE International Symposium in Biomedical Imaging in 2018. He currently serves as Associate Editor for IEEE Transactions on Medical Imaging, IEEE Trans. On Biomedical Engineering, IEEE Reviews in Biomedical Engineering, IEEE Open Journal of Engineering in Medicine and Biology, and Computerized Medical Imaging and Graphics. He served as Vice President for Publications for the IEEE Engineering in Medicine and Biology Society in 2020-21. Under funding from the NIH, private foundations, and industry, his laboratory conducts research in development and application of MRI methods for motion and flow measurement and development of biomedical image processing and analysis methods based on Deep Learning to cardiovascular imaging, computer aided diagnosis, and radiation therapy of lung cancer. He received the UMASS/Amherst College of Engineering Distinguished Alumni Award in 2020. He was elected a Fellow of the IEEE in 2007, to the College of Fellows of the American Institute for Medical and Biological Engineering in 2017, SPIE, the International Society of Optics and Photonics, in 2018, and the Asia-Pacific Artificial Intelligence Association in 2021.

Moo K. Chung

Moo K. Chung, Ph.D., is a Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison. He obtained his Ph.D. from McGill University, Canada, where he studied under James Ramsay and Keith Worsley, focusing on neuroimage analysis. Chung’s initial research was rooted in computational neuroanatomy, leveraging non-invasive brain imaging techniques like magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) to explore the human brain’s spatiotemporal dynamics. His work primarily involved developing differential geometric methodologies to analyze and compare anatomical shape variations in normal and clinical populations, employing a range of mathematical, statistical, and computational techniques. Chung has recently transitioned towards topological data analysis, treating brain networks obtained from functional-MRI as topological objects, such as simplicial complexes, to examine their persistent topological features across different scales. He has authored three books on brain imaging and is currently working on his fourth.

Celia Cintas

Celia Cintas is a Research Scientist at IBM Research Africa – Nairobi. She is a member of the AI Science team at the Kenya Lab. Her current research focuses on exploring subset scanning for anomaly detection under generative models and improving ML techniques to address challenges on Global Health in developing countries. Previously, grantee from National Scientific and Technical Research Council (CONICET), working on Deep Learning for populations studies at LCI-UNS and IPCSH-CONICET (Argentina) as part of the Consortium for Analysis of the Diversity and Evolution of Latin America (CANDELA). She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). Co-chair of several Scipy Latinamerica conferences, Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019), and Diversity Co-Chair for SciPy (2020-2022). Workshop Co-chair at ICLR 2023, Diversity Co-chair at ISBI 2023-2024, among others.

Miguel A. González Ballester

Prof. Miguel A. González Ballester holds an MEng from Universitat Jaume I, Spain (1996) and a PhD from the University of Oxford, UK (2000). His doctorate, under supervision of Sir Michael Brady and Prof. Andrew Zisserman, focused on the analysis of brain MRI data for multiple sclerosis and schizophrenia. He was awarded the prestigious Toshiba Research Fellowship and moved to Japan to work for two years as a senior researcher at Toshiba Medical Systems, where he developed novel, patented systems for MRI parallel imaging. In late 2001 he obtained a faculty position at INRIA (Sophia Antipolis, France), where he led research projects on medical image analysis and mathematical modelling. In 2004 he joined the University of Bern (Switzerland), as head of the medical image analysis group, and later became head of the surgical technology division at the Faculty of Medicine. There, he supervised a division composed of 4 research groups working on medical image analysis, computer-assisted surgery, and surgical robotics and mechatronics. From 2008 until September 2013 he was in charge of the Research Department of the company Alma IT Systems in Barcelona (Spain), where he led the development of a new generation of computer tools for diagnosis and surgical planning. In October 2013 he was awarded an ICREA Research Professorship, and joined the Department of Information and Communication Technologies at Universitat Pompeu Fabra in Barcelona, where he founded the Barcelona Center for New Medical Technologies (BCN Medtech). He has more than 130 publications in peer-reviewed scientific journals and 300 conference publications, and has supervised to completion 22 PhD theses. He is also co-founder and scientific advisor of the company MiWEndo Solutions S.L., and a Visiting Scientist at the QUANTIC research group of Barcelona Supercomputing Center, where he focuses his research on quantum machine learning. He was Program Chair of ISBI 2019 (Venice, Italy).

Mathews Jacob

Mathews Jacob is a professor in the Department of Electrical and Computer Engineering and is heading the Computational Biomedical Imaging Group (CBIG) at the University of Iowa. His research interests include image reconstruction, image analysis, and quantification in the context of magnetic resonance imaging. He obtained his B.Tech in Electronics and Communication Engineering from National Institute of Technology, Calicut, Kerala, and his M.E in signal processing from the Indian Institute of Science, Bangalore. He received his Ph.D. degree from the Biomedical Imaging Group at the Swiss Federal Institute of Technology in 2003. He was a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign. Dr. Jacob is the recipient of the CAREER award from the National Science Foundation in 2009, the Research Scholar Award from American Cancer Society in 2011, and the Faculty Excellence Award for Research from University of Iowa in 2021. He is currently the associate editor of the IEEE Transactions on Medical Imaging and has served as the associate editor of IEEE Transactions on Computational Imaging from 2016-20. He was the senior author on two best paper awards (2015 & 2021) and one best machine learning paper award (2019) from IEEE ISBI. He was the general chair of IEEE International Symposium on Biomedical Imaging, 2020. He was elected as a Fellow of the IEEE (2022) for contributions to computational biomedical imaging.

Ira Ktena

Ira Ktena is a Staff Research Scientist at Google DeepMind working on Machine Learning research for Life Sciences. Previously, she was a senior Machine Learning Researcher with the Cortex Applied Research team at Twitter UK, where she carried out research at the intersection of recommender systems and algorithmic transparency. Their exploration on algorithmic amplification of political content on Twitter was featured by the Economist and the BBC, among others. She completed a Doctoral degree in Medical Image Computing at Imperial College London under the supervision of Professor Daniel Rueckert, as part of the High Performance Embedded and Distributed Systems (HiPEDS) Doctoral Training Programme, for which she currently serves as an Advisory Board member. Her research focused on developing methods for modelling and analyzing graph-structured neuroimaging data at an individual or population level using traditional graph theoretical approaches and geometric deep learning. During her PhD, Dr Ktena visited the Stroke Group in Massachusetts General Hospital, Harvard Medical School under the supervision of Professor Natalia Rost and Markus Schirmer, while being supported by an EMBO Short-Term Fellowship. She is passionate about community outreach and increasing diversity in technology.

Athina P. Petropulu

Athina P. Petropulu is Distinguished Professor at the Electrical and Computer Engineering (ECE) Department at Rutgers, having served as chair of the department during 2010-2016. Her research interests span the area of statistical signal processing, wireless communications, signal processing in networking, physical layer security, and radar signal processing.
Dr. Petropulu is Fellow of IEEE and the American Association for the Advancement of Science (AAAS), and recipient of the 1995 Presidential Faculty Fellow Award given by the US National Science Foundation and the White House. She has played key roles in her professional society, namely, she was 2022-2023 President of the IEEE Signal Processing Society, Editor-in-Chief of the IEEE Transactions on Signal Processing (2009-2011) and IEEE Signal Processing Society Vice President-Conferences (2006-2008). She was Technical Program Co-Chair of the 2023 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), General Co-Chair of the 2018 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), and General Chair of the 2005 ICASSP. She was Distinguished Lecturer for the Signal Processing Society and the IEEE Aerospace & Electronics Systems Society. For her service, Dr. Petropulu has received the 2012 IEEE Signal Processing Society Meritorious Service Award. She is also co-recipient of the 2005 IEEE Signal Processing Magazine Best Paper Award, the 2020 and 2021 IEEE Signal Processing Society Young Author Best Paper Award (B. Li and F. Liu, respectively), the 2021 Barry Carlton Best Paper Award by IEEE Aerospace and Electronic Systems Society, the 2023 IEEE Machine Learning in Signal Processing Workshop Best Student paper Award (S. Evmorfos), and the 2023 Stephen O. Rice Prize Best Paper Award by the IEEE Communications Society.

Anqi Qiu

Anqi Qiu is a global STEM scholar and Professor at the Department of Health Technology and Informatics at the Hong Kong Polytechnic University. She is also an Adjunct Professor at the Department of Biomedical Engineering at Johns Hopkins University. Her past roles include Deputy Head for Research & Enterprises at the Department of Biomedical Engineering and Director for the BME Innovation Center at the NUS Suzhou Research Institute, Master of Eusoff Hall, National University of Singapore.
Prof. Qiu commenced her academic journey with a BS in Biomedical Engineering from Tsinghua University. She then earned two MS degrees – one in Biomedical Engineering from the University of Connecticut, and another in Applied Mathematics and Statistics from Johns Hopkins University. Her continued dedication to academia led her to earn a PhD from Johns Hopkins.
After the PhD study, Prof. Qiu joined the National University of Singapore as an Assistant Professor, where she founded the Laboratory for Medical Image Data Sciences. Her remarkable contributions to the field have earned her multiple accolades, including the Faculty Young Research Award and the 2016 Young Researcher Award of NUS. In a recent recognition of her outstanding research achievements, she was bestowed with the prestigious “Dean’s Chair” Associate Professorship.
Specializing in computational analyses, Prof. Qiu is deeply committed to understanding the origin of individual health differences throughout a lifespan. She leverages complex and informative datasets that include disease phenotypes, neuroimaging, and genetics to further her research. Her team has high-impact publications in Nature, Nature Neuroscience, Nature, Mental Health, American Journal of Psychiatry, Biological Psychiatry, IEEE Transactions in Medical Imaging, Medical Image Analysis.

Dinggang Shen

Dinggang Shen is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence, Shanghai. He is a Fellow of IEEE, AIMBE, IAPR and MICCAI. He was a Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with the University of North Carolina at Chapel Hill, NC, USA. He was also a tenure-track assistant professor in the University of Pennsylvanian, and an Instructor in the Johns Hopkins University. His research interests include medical image analysis, computer vision and pattern recognition, in which he has published more than 1,500 peer-reviewed papers, with H-index 140 and 85000+ citations. He serves as an Editor-in-Chief for Frontiers in Radiology, and an editorial board member for eight international journals. He has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015, and was the General Chair for MICCAI 2019.

Archana Venkataraman

Archana Venkataraman is an Associate Professor of Electrical and Computer Engineering at Boston University. From 2016-2022, she was an Assistant Professor at Johns Hopkins University. Dr. Venkataraman directs the Neural Systems Analysis Laboratory and is affiliated with the Department of Biostatistics, the Center for Brain Recovery, and the Rafik B. Hariri Institute for Computing at Boston University. Dr. Venkataraman’s research lies at the intersection of biomedical imaging, artificial intelligence, and clinical neuroscience. Her work has yielded novel insights in to debilitating neurological disorders, such as autism, schizophrenia, and epilepsy, with the long-term goal of improving patient care. Dr. Venkataraman completed her B.S., M.Eng. and Ph.D. in Electrical Engineering at MIT in 2006, 2007 and 2012, respectively. She is a recipient of the MIT Provost Presidential Fellowship, the Siebel Scholarship, the National Defense Science and Engineering Graduate Fellowship, the NIH Advanced Multimodal Neuroimaging Training Grant, the CHDI Grant on network models for Huntington’s Disease, numerous best paper awards, and the National Science Foundation CAREER award. Dr. Venkataraman was also named by MIT Technology Review as one of 35 Innovators Under 35 in 2019.

Lilla Zöllei

Lilla Zöllei completed her PhD in Computer Science at the Massachusetts Institute of Technology, working in the Medical Imaging group of Dr. Eric Grimson. Her doctoral work focused on the theoretical and practical aspects of image registration algorithms using information theoretic criteria. Her postdoctoral training involved multiple aspects of pair-wise registration problems using both ex-vivo and in-vivo, as well as structural and diffusion MRI scans. As junior faculty, she started focusing on algorithmic problems associated with the post-processing of infant brain acquisitions in collaboration with the Boston Children’s Hospital. Presently, she is an Associate Professor at the Department of Radiology at Harvard Medical School and Massachusetts General Hospital. Her scientific investigation focuses on perinatal MRI imaging and developing computational tools that can explore the dynamic aspect of human neurodevelopment.

Panelist and Moderator: Behnaz Ghoraani
Short Bio: Dr. Behnaz Ghoraani is an Associate Professor in the Department of Computer & Electrical Engineering at Florida Atlantic University (FAU), where she serves as a Faculty Fellow and Co-Director of the SMART Health Center. Dr. Ghoraani’s research is centered on engineering and computer science applications in medicine, with a focus on biosensor technology, biomedical signal analysis, and data-driven healthcare solutions, supported by grants from entities like the NSF and NIH. Dr. Ghoraani has authored over 80 peer-reviewed works, holds several patents, and has received notable awards including the NSF CAREER award. She serves in editorial capacities for several journals, including the IEEE Journal of Biomedical and Health Informatics and BioMedical Engineering Online.  Dr. Ghoraani is active in professional societies, serving on the IEEE Biomedical Image and Signal Processing Technical Committee and as chair of the IEEE Women in Signal Processing Committee. Her contributions have earned her several distinctions, including Engineering Educator of the Year by The Engineers’ Council in 2024. More details at
https://www.fau.edu/engineering/directory/faculty/ghoraani/

Panelist:  Celia Cintas
Short Bio:  Celia Cintas is a Research Scientist at IBM Research Africa – Nairobi. She is a member of the AI Science team at the Kenya Lab. Her current research focuses on exploring subset scanning for anomaly detection under generative models  and improving ML techniques to address challenges on Global Health in developing countries. Previously, grantee from National Scientific and Technical Research Council (CONICET), working on Deep Learning for populations studies at LCI-UNS and IPCSH-CONICET (Argentina) as part of the Consortium for Analysis of the Diversity and Evolution of Latin America (CANDELA).  She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). Co-chair of several Scipy Latinamerica conferences, Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019), and Diversity Co-Chair for SciPy (2020-2022). Workshop Co-chair at ICLR 2023, Diversity Co-chair at ISBI 2023-2024, among others. More details at https://celiacintas.io/

Panelist:  Julie Coloigner
Short Bio: Julie Coloigner is currently a CNRS permanent researcher and a member of the Empenn team in a joint lab IRISA – CNRS UMR6074, Inserm, Inria, University of Rennes 1. She has a background in applied mathematics and more particularly in numerical analysis. After a theoretical PhD in signal processing in LTSI, Rennes, France, she began neuroimaging analysis during her postdoctoral research position in the Children’s Hospital Los Angeles (CHLA) . She is an international expert in cerebral connectivity analysis using functional MRI and diffusion MRI. She has been developing multimodal imaging biomarkers for brain diseases and translating this research to clinical neuroscience for more than 10 years. On this topic, she has published 16 articles in international journals specialised on signal and image processing. For the past two years, I have been exploring multimodal MRI biomarkers of cerebral connectivity in patients suffering from depression ans sickle cell disease.  She is the principal investigator of an ANR project, funded in 2022, focusing on the development of multimodal analysis to identify specific biomarkers of each stage of Alzheimer’s and Parkinson’s diseases. More details at https://juliecoloigner.fr/

Panelist:  Leticia Rittner
Short Bio: Dr. Letícia Rittner is an Associate Professor (with tenure) at the School of Electrical and Computer Engineering, University of Campinas (Unicamp), Brazil. She is the co-founder and currently Director of the Medical Image Computing Laboratory, and also part of the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN). Dr. Rittner holds a Master in Business and Administration and MSc and PhD in Computer Engineering from the University of Campinas. She held fellowships at the Montreal Neurological Institute, McGill University (Canada), and at the Perelman School of Medicine, University of Pennsylvania (USA). She was also a visiting Professor at Università degli Studi di Verona, Italy and a visiting Researcher at the Hospital Israelita Albert Einstein, Brazil. Her research field is Medical Image and Signal Analysis, developing techniques based on machine learning, such as segmentation and characterization of anatomical structures, to support large-scale studies. Since 2012, she runs programming workshops for children and adolescents and has an active role in promoting gender diversity in the STEM areas. She is a member of the Brazilian Society of Biomedical Engineering (SBEB), of the International Photonic Society (SPIE) and a senior member of IEEE, serving on the IEEE Biomedical Image and Signal Processing Technical Committee. Organized in 2021, as General Chair, the first SIPAIM Conference in Brazil and in 2023, as Program Chair, helped bring the IEEE International Symposium of Biomedical Imaging (ISBI) to Latin America for the first time. More details can be found here https://lrittner.fee.unicamp.br/ and https://miclab.fee.unicamp.br/.

Panelist:  Farnaz Khun Jush
Short Bio: Farnaz Khun Jush works as Medical Imaging Scientist at Bayer AG in Berlin. She obtained her bachelor’s degree in Electrical Engineering from Amirkabir University of Technology in Tehran, Iran, and her Master’s degree in Computational Engineering with a focus on digital signal processing at Friedrich–Alexander-University of Erlangen–Nuremberg (FAU). In 2018, she joined Siemens Healthineers and the Pattern Recognition Lab of FAU as PhD Researcher, where she focused on developing deep-learning-based solutions for ultrasound image reconstruction, with a specific emphasis on speed-of-sound imaging for breast cancer screening. Joining Bayer AG in 2022, her current work revolves around leveraging AI to develop state-of-the-art medical imaging algorithms, within the scope of radiomics, CT imaging biomarkers, and image search.