Parvin Mousavi is a Professor at the School of Computing, Queen's University, and holds prestigious chairs including the Canada CIFAR AI Chair and Canada Research Chair in Medical Informatics. She directs the Medical Informatics (Med-i) Laboratory, focusing on computational approaches for biological process prediction and explanation. Education: Ph.D., University of British Columbia (2001) Research interests span machine learning in computer-assisted diagnosis, ultrasound imaging, medical image computing, bioinformatics, systems biology, and quantitative modeling of gene regulatory networks. The lab emphasizes interdisciplinary collaboration with medical professionals and institutions. Affiliations & Collaborations: Kingston General Hospital, Human Mobility Research Centre (HMRC), and University of California, San Francisco. The Med-i Laboratory is equipped with advanced parallel computing servers, clusters, and imaging devices. Awards: Canada CIFAR AI Chair Canada Research Chair in Medical Informatics Contact: Office: Goodwin 720 | Phone: 613 533-6070
Cesare Franchini is a full Professor at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics research group. His work focuses on theoretical understanding and computational modeling of quantum materials using first principles methods, particularly VASP. He maintains an active research program with numerous postdocs, PhD students, and collaborations across multiple institutions including the University of Bologna. Professor Franchini's research centers on quantum materials with many interacting degrees of freedom (lattice, spin, and electron orbital) that enable novel electronic and magnetic phases. His specific interests include metal-insulator transitions, polaron physics (electron-phonon interactions), non-collinear spin orderings, topological Dirac/Weyl phases, multiferroism, and superconductivity. He has increasingly incorporated machine learning data-driven tools and diagrammatic Monte Carlo techniques into his computational approaches. Analysis of his recent publications (2024-2025) reveals a strong focus on polaron physics across multiple material systems, with significant work on hematite, titanium dioxide, and quantum paraelectrics like KTaO3. His research increasingly integrates machine learning with traditional first-principles methods, particularly for studying hydrogen diffusion, surface science phenomena, and electronic structure calculations. There's also substantial work on single-atom catalysis and the application of advanced computational techniques to understand fundamental charge transport mechanisms in energy materials. Professor Franchini actively supervises numerous PhD students and postdocs, including Andrea Angeletti, Viktor Birschitzky, Lorenzo Celiberti, and several others working on diverse aspects of computational materials physics. He leads or participates in major research projects including TACO (Taming Complexity in Materials Modeling), DCAFM (Doctoral College Advanced Functional Materials), and the recently launched Spin-orbit entangled anharmonic polarons project. His group maintains strong collaborations with experimentalists at Charles University, Technical University of Vienna, and other international institutions.
Mahmoud El-Sakka is an Associate Professor at the Department of Computer Science, University of Western Ontario since 1999. Previously, he was a faculty member at the University of Waterloo (1997–1999). He holds a B.Sc. and M.Sc. from Alexandria University (Egypt) and a Ph.D. in Systems Design Engineering from the University of Waterloo. His research focuses on medical imaging, image processing, and computer-aided diagnostics. He has served as Chair of the graduate program (2002–2007) and undergraduate program (2017–present) in Computer Science at Western Ontario. El-Sakka is a Senior Member of the IEEE and a licensed Professional Engineer in Ontario. His work spans grants from NSERC, internal university funding, and industry collaborations. Major research areas include image compression, segmentation, and medical applications like vascular analysis and echocardiography. He has led over 20 funded projects since 1999, emphasizing interdisciplinary approaches in healthcare technology. Academic contributions include advisory roles in summer programs, thesis evaluations, and conference participation. His service includes roles as Pro-Chancellor at convocations and involvement in equipment purchasing committees. Collaborations include consulting with NCR Canada and VRP Web Technology.
Xiaoqing Pan is a Professor and Henry Samueli Endowed Chair in Engineering at the University of California, Irvine, with dual appointments in the Department of Materials Science and Engineering and the Department of Physics and Astronomy. He serves as Director of the Irvine Materials Research Institute (IMRI) and the Center for Complex and Active Materials (NSF MRSEC). A renowned electron microscopy expert, Pan has developed advanced transmission electron microscopy (TEM) techniques for atomic-scale material characterization. Ph.D., Universität des Saarlandes, Germany (1991) His research focuses on atomic-scale structure-property relationships in oxide heterostructures, ferroelectrics, nanocatalysts, and 2D functional materials. Pan leads development of novel 4D-STEM and momentum-resolved vibrational electron microscopy methods to study single-atom catalysts and complex oxides. With over 400 high-impact publications in Nature , Science , and Nature Materials , his work has been recognized by major fellowships and awards from the American Ceramic Society, American Physical Society, and National Science Foundation. Pan's recent work includes: Atomic-scale analysis of grain boundary phonon anisotropy Advances in FeSe/SrTiO 3 interface electron-phonon coupling Plastic waste upcycling through carbon intermediate interception Control of metal-support interactions in photocatalysts Strain engineering in high-entropy oxide films His laboratory at UCI represents the forefront of materials characterization technology development.
Olaf Ronneberger is an associate professor at the Albert-Ludwigs-Universität Freiburg and works at Google DeepMind . His research focuses on deep learning architectures , AI applications to scientific problems , and protein structure prediction . He leads seminars on deep learning and 3D image analysis, emphasizing vision-language integration and generative models. His publications include foundational work on U-Net architectures for biomedical image segmentation, AlphaFold 3 for biomolecular interaction prediction, and Gemini models for multimodal AI systems. Key subfields span medical imaging , protein folding , and vision-language models . Co-developer of U-Net , a widely used biomedical image segmentation framework. Contributor to AlphaFold 3 for structural biology. Research on Gemini 1.5/2.5 models for multimodal reasoning.
Prof. Dr. Soeren Lienkamp is an Assistant Professor at the Institute of Anatomy , Faculty of Medicine , University of Zurich . His work bridges digital education and genetic research , focusing on enhancing medical teaching through innovative formats. Research Interests : Genetics, developmental biology, kidney disease modeling, CRISPR applications, digital medical education, and advanced microscopy. Methodologies : Combines Xenopus tropicalis models, deep learning , and bioengineering to study genetic kidney disorders and improve diagnostic tools. Publication Trends : His recent articles highlight predictable genome editing , 3D imaging technologies , and mechanistic insights into kidney and eye development. Earlier works focus on ciliary function , Wnt signaling , and metabolic stress in renal cells.
Charless Fowlkes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI). His research focuses on computational vision, spanning human visual system understanding, machine vision systems, and applications in biomedical informatics and forensic science. He holds a Ph.D. from UC Berkeley (2005). His work integrates techniques from computer vision, AI, and applied mathematics to address challenges in automated biological data analysis, morphology, and spatial gene expression. Key research areas include forensic science (e.g., shoeprint matching via 3D reconstruction), biomedical applications (e.g., heart function mapping and pollen classification), and AI-driven systems for scene understanding. Recent projects include a $20M forensic science center funded by the National Institute of Justice. His publications emphasize geometric reasoning, 3D reconstruction, and adaptive learning algorithms. Notable contributions include developing algorithms for 3D human pose estimation with scene constraints, automated pollen identification via CNNs, and frameworks for cross-domain forensic analysis. His work bridges theoretical computer vision with real-world applications in forensics, healthcare, and environmental science.
Dr. Min Xu is a Courtesy Professor in the Computational Biology Department within the School of Computer Science at Carnegie Mellon University. His research focuses on advancing computer vision and machine learning for biomedical image analysis, particularly cellular cryo-electron tomography (Cryo-ET) and automated science video analysis. He leads a lab developing cutting-edge computational tools for structural biology and medical imaging. Key research directions include: High-resolution 3D Cryo-ET image analysis AI-driven medical image segmentation Few-shot learning for cryo-EM analysis Video analysis frameworks for laboratory automation Notable contributions include the AITom toolkit for Cryo-ET analysis and pioneering work in adapting foundation models for medical imaging tasks. His work has been published in top venues like CVPR, MICCAI, and Nature-associated journals. No academic awards or grants are explicitly listed in the provided text. He maintains an active lab focused on translating computational methods into impactful biomedical research tools.
Jean Provost is a Full Professor in the Department of Engineering Physics at Polytechnique Montréal , with affiliations to the Montreal Heart Institute , IVADO , and the Institute of Biomedical Engineering . His research focuses on ultrasound imaging , cardiac and cerebral vascular imaging , and superresolution image reconstruction using machine learning and optimization . Based on 96 publications, his work emphasizes ultrasound localization microscopy , neural network applications , and microvascular hemodynamics . Education : Ph.D. (Columbia University), MPhil (Columbia University), M.Sc.A. (École Polytechnique Montréal), Engineering Degree (École Centrale Paris), License (Université Paris XI), B.Eng. (École Polytechnique Montréal) Research trends from 15 recent articles include: 3D and dynamic ultrasound localization microscopy for microvascular mapping Deep learning for image reconstruction and neural network pruning Machine learning-driven aberration correction and superresolution imaging Acoustoelectric and cavitation-based imaging techniques Applications in cardiac diagnostics and dementia detection Supervision includes 2 Ph.D. and 8 Master's theses completed at Polytechnique Montréal (2023), covering topics like optical ultrasound detection , microbubble modulation , and spatiotemporal sampling .
Jessica J. Walsh, PhD is an Assistant Professor in the Department of Pharmacology at the University of North Carolina at Chapel Hill School of Medicine and a member of the UNC Neuroscience Center. She leads the Walsh Lab, which focuses on understanding neural circuit mechanisms underlying motivated social behavior using a multi-level approach to elucidate the molecular and circuit mechanisms that govern social interactions and their alterations in disease states. Dr. Walsh earned her B.A. in Neuroscience & Behavior from Columbia University, where she began her research journey volunteering in Dr. Gerald Fischbach's laboratory. During her graduate work, she explored neural circuit mechanisms underlying social stress susceptibility at the Icahn School of Medicine at Mount Sinai under Dr. Ming-Hu Han. Prior to joining UNC, she completed her postdoctoral fellowship at Stanford University with Dr. Robert Malenka, investigating neural circuit mechanisms in genetic mouse models with social deficits. Her research focuses on neural circuit mechanisms underlying motivated behavior, neurodevelopmental and psychiatric disorders, and functional/anatomical brain mapping. The Walsh Lab specifically uses genetic mouse models to investigate how genetic mutations and experience lead to circuit adaptations that govern impaired behavior seen in autism spectrum disorders. They combine whole brain optical clearing methods, light sheet microscopy, in vivo imaging, and machine learning based behavioral analysis to elucidate neural adaptations responsible for motivated behavior. Her publication record demonstrates a strong focus on neural circuits related to social behavior, with particular emphasis on autism spectrum disorders, serotonin and dopamine signaling, and the neural basis of prosocial behaviors. She has published extensively in high-impact journals including Nature, Nature Neuroscience, PNAS, and Neuropsychopharmacology, with research spanning from molecular mechanisms to circuit-level analyses of behavior. Dr. Walsh mentors several trainees in her lab, including a postdoctoral fellow, multiple graduate students, and numerous undergraduate researchers. Her lab team includes researchers with diverse interests spanning from molecular biology to machine learning applications in neuroscience. The lab actively recruits postdocs and graduate students interested in joining their research on motivated behavior and psychiatric disorders. The Walsh Lab employs a comprehensive research approach including genetic manipulation, whole brain activity mapping, viral tracing, slice physiology, optogenetics, chemogenetics, fiber photometry, and machine learning based behavioral classification to gain a nuanced understanding of neural circuits involved in motivated social behavior.
Keith A. Brown is an Associate Professor in Mechanical Engineering at Boston University's College of Engineering with additional appointments in Materials Science & Engineering and Physics. He serves as Associate Chair for Graduate Programs in ME and leads the interdisciplinary KABLab research group. Education: PhD, Harvard University Dr. Brown's research centers on hierarchical soft matter systems including polymers and smart fluids. His group develops innovative approaches to accelerate materials research through nanocombinatorics , autonomous experimentation , and scanning probe lithography . Key focus areas include connecting nanoparticle properties to bulk smart fluid behavior, designing 3D-printed structures with programmed mechanics, and creating self-driving laboratories for materials discovery. His recent publications (2024-2025) demonstrate a strong emphasis on autonomous experimentation systems integrating machine learning with physical research. This work spans energy-absorbing foam design, nanoscale fluid manipulation, and physics-informed modeling for mechanical systems, establishing new paradigms in accelerated materials development. Scientific Awards: The Early Career Research Excellence Award, College of Engineering, 2021 Professor of the Year, Mechanical Engineering, 2020 Frontiers of Materials Award, The Minerals Metals and Materials Society (TMS), 2020 Dean’s Catalyst Award (2018) Dean’s Catalyst Award (2020) Moorman-Simon Interdisciplinary Career Development Professor, 2016 Dr. Brown teaches undergraduate courses including Fluid Mechanics (ME 303), Introduction to Materials (ME 306), and Nanomanufacturing (ME/MS 576). His research is supported by: Federal Grants : AFOSR MURI, NSF Nanomanufacturing, ACS Petroleum Research Fund Foundations : Gordon and Betty Moore Foundation Industry : Google Faculty Research Award University : BU Dean's Catalyst Award, Nanotechnology Innovation Center The KABLab employs interdisciplinary teams to develop novel instrumentation for hierarchical soft matter research, with particular expertise in autonomous experimentation platforms that combine scanning probe techniques with machine learning for accelerated materials discovery.
Dr. Richard Gault is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on computer vision and deep learning applied to microscopy data, particularly in medicine, health, and life sciences. He leads a team developing novel methods for medical image analysis, including histopathology and digital pathology, with applications in cancer diagnosis and environmental science. He is actively involved in teaching, having received Excellence in Teaching awards from Queen's University Belfast in 2019 and 2022. His work bridges computational intelligence and healthcare, with notable contributions to AI-driven diagnostics, stain normalization in histopathology, and multimodal data fusion. Dr. Gault's research interests include ensemble learning, fuzzy systems, and generative models like diffusion networks. He supervises multiple PhD students and has mentored graduates now working in machine learning engineering and postdoctoral research. His team’s achievements include awards such as the 2023 Best Oral Presentation at the Pan Ireland Ophthalmology Day and a 2021 Best Paper Award from his school. Key contributions include the LymphoSight AI application for detecting lymphoid structures and HistoClean , open-source software for improving CNN development in histopathology. He has been recognized as a Senior Member of IEEE and a Fellow of the Higher Education Academy. His work is supported by grants such as the R5131ECI project on 3D quantifier approximation via 2D video analysis (2019–2025). He actively engages in academic activities, including conference organization and PhD external examinations across Europe.
Thomas Walter is a Professor at Mines ParisTech and Director of the Centre for Computational Biology (CBIO) , a research group affiliated with the Institut Curie and INSERM . His work focuses on applying Machine Learning and Computer Vision to biomedical image analysis, particularly in high-content screening and computational pathology . He also serves as Deputy Director of the Computational Oncology (U1331) unit and leads the Statistical Learning and Modeling of Biological Systems team. PhD in Medical Image Analysis (2003, Mines ParisTech) Postdoctoral work at EMBL (European Molecular Biology Laboratory) Director of CBIO since 2018 Holder of a PRAIRIE Chair (Paris Artificial Intelligence Research Institute) since 2019 Dr. Walter's research bridges biomedical imaging , machine learning , and cancer genomics . Key areas include: Statistical reconstruction of biological networks Prediction of tumor progression at genomic/transcriptomic levels Development of deep learning methods for cell cycle analysis Integration of multi-omics data for precision oncology Tools for spatial transcriptomics (e.g., autoFISH, RNA2seg) Recent publications highlight his work in spatial transcriptomics , immunotherapy outcome prediction , and deep learning for digital pathology . His team has developed open-source tools like FISH-quant and pyHiM for single-molecule RNA imaging analysis. Scientific Honors: PRAIRIE Chair (2019) for AI research in life sciences Dr. Walter actively contributes to teaching deep learning for image analysis in multiple graduate programs across France, including courses at Mines ParisTech , Université Paris-Saclay , and Institut Curie . His software tools (FISH-quant, pyHiM) and methodological frameworks (e.g., Cut-Detector, PointFISH) have become standard resources in bioimage informatics.
Prof. Dr. Thomas Koop is a Professor of Physical Chemistry at Bielefeld University, where he leads the Atmospheric and Physical Chemistry research group within the Faculty of Chemistry. He has served as Dean of the Faculty of Chemistry from 2022-2024 and currently serves as Vice Dean (2024-2025). His research focuses on phase transition phenomena, particularly ice nucleation and growth, supercooled liquids, and the formation of amorphous glassy materials. His work has significant implications for understanding atmospheric aerosols, cloud formation mechanisms, and cryobiological processes. The group employs experimental techniques such as differential scanning calorimetry and optical cryo-microscopy, developing specialized equipment for studying phase transitions at micro and nanoscales. Prof. Koop's publication record shows a consistent focus on atmospheric chemistry with increasing exploration of biological ice nucleators, planetary atmospheres (including Venus), and the physical properties of atmospheric aerosols. His most cited work includes 'Water activity as the determinant for homogeneous ice nucleation in aqueous solutions' (Nature, 2000), which established fundamental principles in the field. 2024-2025: Vice Dean of Faculty of Chemistry 2022-2024: Dean of Faculty of Chemistry 2001-2022: Co-founder and Executive Editor of Atmospheric Chemistry and Physics Since 2004: Coordinator of Graduate School of Chemistry and Biochemistry Prof. Koop has mentored numerous students and postdoctoral researchers, contributing significantly to the development of the next generation of atmospheric scientists. His research has been supported by various funding agencies and has led to collaborations with institutions worldwide, from MIT and UC Berkeley to research centers in Switzerland and Israel.
Prof. Dr. Roderick Lim is an Associate Professor at the Biozentrum, University of Basel , where he leads a research group since 2014. His work bridges biophysics, nanotechnology, and molecular biology , focusing on the nuclear pore complex (NPC) and mechanobiology of cells . He develops biomimetic systems for selective molecular transport and ARTIDIS , a nanomechanical tissue diagnostic platform commercialized for breast cancer prognosis . Education : BSc (UNC Chapel Hill), PhD (NUS/IMRE Singapore), Postdoc (Swiss Nanoscience Institute) Positions : Argovia Professor (2014–present), Tenure Track Asst. Prof. (2009–2013), Postdoc (2004–2008) His research on NPC transport selectivity reveals how karyopherins modulate the FG Nup barrier via multivalent interactions, with implications for viral entry and Alzheimer’s disease . His ARTIDIS platform uses atomic force microscopy to detect cancer via tissue softness, linking hypoxia to metastasis . Recent 2025 publications explore bacterial nanoharpoon defense mechanisms and DNA origami-based NPC mimics . Scientific Awards : Pierre-Gilles de Gennes Prize (2008), A*STAR Fellowship (2004) Collaborations : NCCR Molecular Systems Engineering, NanoTera, KTI He mentors PhD students in institutions across Switzerland, Singapore, Sweden, and the UK , with alumni working on polymersome delivery, mechanotransduction, and pathogen transport . His lab pioneered high-speed atomic force microscopy for real-time NPC dynamics and plasmonic nanopores for synthetic biology applications.
