Georg Fantner is an Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) with dual appointments in the School of Engineering (STI) within the Institute of Bioengineering and the School of Life Sciences (SV) for teaching. He directs the Laboratory for Bio- and Nano-Instrumentation (LBNI) and holds leadership roles including President of the Open Science Strategic Committee and the Association des Professeurs de l'EPFL. Research Focus: Bioinstrumentation, Nanotechnology, Scanning Probe Microscopy, and Metrology Teaching: Structural Mechanics for Life Sciences, Metrology, and Metrology Practicals His research pioneers advanced instrumentation for nanoscale characterization, emphasizing data-driven approaches to enhance microscopy techniques. Recent work integrates deep learning with scanning probe microscopy for real-time biological imaging and develops novel MEMS devices for fluid-compatible nanoscale manipulation. Key innovations include hermetically sealed sample chambers for pathogen studies and deterministic nanotopography engineering. Professor Fantner actively mentors 7 current PhD students and has supervised 14 graduates. His laboratory fosters interdisciplinary collaboration across engineering, physics, and life sciences to advance nanoscale measurement technologies and instrumentation development.
Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.
Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.
Distinguished Professor Dayong Jin is a leading academic in nanotechnology and biomedical engineering at the University of Technology Sydney (UTS). He holds roles including Director of the Institute for Biomedical Materials and Devices (IBMD), ARC Laureate Fellow, and Chair Professor at Southern University of Science and Technology (China). His research focuses on photonics, luminescent materials, and their applications in healthcare, including cancer detection, rapid diagnostics, and super-resolution microscopy. Key innovations include 'Nano Torch' technology for disease detection and 'Super Dots' nanocrystals for imaging and anti-counterfeiting. Education: PhD from Macquarie University (2007). Leadership: Established UTS's IBMD and multiple research hubs, including the ARC IDEAL Research Hub and Australia-China Joint Research Centre. Research Interests: Transforming nanophotonics into diagnostic tools, rapid antigen tests (e.g., for COVID-19), and biomedical devices. His work bridges physics, engineering, and biology to address global health challenges. Awards: Australian Museum Eureka Prize (2015), Prime Minister's Prize for Science (2017), ARC Laureate Fellowship (2021), and Fellow of the Australian Academy of Technology and Engineering. Grants: Overseeing funded projects on quantum biotechnology, deep-tissue imaging, and nanoscale thermometry. Active in interdisciplinary collaborations, including with Chinese institutions. Labs/Teams: Leads IBMD, the ARC IDEAL Hub, and the UTS-SUSTech Joint Research Centre, fostering innovation in wearable biomaterials and point-of-care technologies.
Jørgen Arendt Jensen is a Professor of Biomedical Signal Processing at the Technical University of Denmark (DTU), with dual affiliations in the Department of Health Technology and the Department of Electrical Engineering (DTU Elektro). He leads the Center for Fast Ultrasound Imaging (CFU), a collaborative initiative involving DTU, BK Medical, Rigshospitalet, and DTU Nanotech. His research focuses on advanced medical ultrasound technologies, including synthetic aperture imaging, vector flow imaging, and ultrasound simulation, aiming to improve clinical image acquisition efficiency and accuracy. He teaches medical imaging courses and co-initiated the joint biomedical engineering program between DTU and the University of Copenhagen. Jensen’s work contributes to UN Sustainable Development Goals related to health and innovation. His research interests span algorithm development for fast ultrasound imaging, blood velocity characterization, and simulation of ultrasound systems. He supervises multiple PhD students and collaborates on projects involving transducer design, real-time imaging systems, and microvascular pathology analysis. Recent publications emphasize advancements in super-resolution ultrasound imaging, transducer optimization, and pressure gradient estimation. His lab, CFU, develops cutting-edge imaging solutions for clinical applications. Jensen’s contributions include patents on ultrasound imaging techniques and collaborative ventures to enhance diagnostic capabilities through interdisciplinary engineering.
Suliana Manley is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences and the Laboratory of Experimental Biophysics . She also holds teaching and research roles in EPFL's School of Life Sciences and Swiss Plasma Center , focusing on interdisciplinary biophysical studies. Education : PhD in Physics (2004), Harvard University Bachelor's in Physics & Mathematics (1997), Rice University Manley's research centers on super-resolution fluorescence imaging , single-molecule tracking , and quantitative biophysics . Key themes include: Understanding protein assembly dynamics at cellular membranes Elucidating viral assembly mechanisms (e.g., HIV-Gag) Developing 3D imaging algorithms and high-density data reconstruction tools like PALMsiever and FALCON Quantifying nanoscale organization in systems like telomeres and centrioles Her work bridges optical physics , computational image analysis , and cellular biology , with notable Nature and PNAS publications. Collaborations span bioengineering , genetics , and medical research . Scientific Awards : Featured in Nature Methods Research Highlights (3x) Very Important Paper and Cover Article (ChemBioChem, 2012) Postdoctoral Fellow, NIH and MIT Advising & Collaborations : Current PhD students in biophysics, cellular biology, and bioengineering Former students: Anna Archetti, Aleksandr Benke, Andrea Callegari, and others Co-founder of tools for high-density super-resolution microscopy and live-cell imaging Labs & Teams : Leads the Laboratory of Experimental Biophysics at EPFL, integrating physics-based methods into biological questions. The lab focuses on quantitative imaging , computational modeling , and software development for nanoscale analysis.
Prof. Casper Hoogenraad is a full professor in Molecular Neuroscience at the Department of Cell Biology, Faculty of Science, Utrecht University. His research focuses on understanding how intracellular protein trafficking underlies neuronal development and function, with particular emphasis on the microtubule cytoskeleton, synaptic cargo trafficking, and synaptic plasticity. He leads an active research group within Utrecht University's Cell Biology department and collaborates extensively with other neuroscience research groups. Education: PhD, Erasmus University Rotterdam (1996-2001) Postdoc, Massachusetts Institute of Technology (2002-2005) Hoogenraad's research spans three main themes: cytoskeleton dynamics during neurodevelopment and synaptic plasticity, motor proteins and adaptors as regulators of synaptic transport, and psychiatric and neurologic disease disorders linked to intracellular transport. His work combines genetics, biochemistry, molecular, and cellular biology methods in in vitro (neuron cultures), ex vivo (brain slices), and in vivo (mice) systems, along with advanced microscopy techniques including immunofluorescent confocal microscopy, high-resolution live cell imaging, and photo-activated localization microscopy (PALM). Analysis of Hoogenraad's recent publications reveals a strong focus on microtubule organization, neuronal polarity, and the molecular mechanisms underlying synaptic function and dysfunction. His work frequently explores how disruptions in intracellular transport contribute to neurological disorders including Alzheimer's disease, schizophrenia, and autism spectrum disorders, with particular attention to the relationship between cytoskeletal organization and cargo transport in neuronal compartments. Scientific Awards and Memberships: ZonMW-VIDI (2004) European Young Investigators (EURYI) award (2005) NWO-ALW VICI (2011) ERC Consolidator grants (2013) FENS-Kavli Network of Excellence (2014) European Molecular Biology Organization (EMBO) (2015) Young Academy of Europe (YAE) (2015) IBRO Kemali Prize (2016) Hoogenraad leads a research group studying neuronal development and function, with a particular focus on how intracellular transport mechanisms contribute to both normal brain function and neurological disorders. His laboratory employs a multidisciplinary approach combining molecular, cellular, and systems neuroscience techniques to investigate the molecular basis of neuronal polarity, synaptic plasticity, and the pathogenesis of neurological disorders. He has secured significant research funding through prestigious grants including ERC Consolidator grants. The Hoogenraad lab operates within the Cell Biology department at Utrecht University, collaborating with other research groups focusing on cellular dynamics, biophysics, and neurobiology. The lab utilizes advanced microscopy techniques including immunofluorescent confocal microscopy, high-resolution live cell imaging (spinning disc microscopy and total internal reflection fluorescence microscopy), and quantitative analysis using advanced high-resolution microscopy (photo-activated localization microscopy). Current lab technicians include Phebe Wulf and Bart de Haan.
Lara A. Estroff is a Full Professor and the current Chair of the Department of Materials Science and Engineering at Cornell University's College of Engineering. She has been a faculty member since 2005 and served as Director of Graduate Studies from 2015 to 2019. Her academic leadership and research excellence position her at the forefront of bio-inspired materials and biomineralization research. Her educational background includes a B.A. in Chemistry from Swarthmore College (1997) and a Ph.D. in Chemistry from Yale University (2003), followed by an NIH-funded postdoctoral fellowship at Harvard University in the lab of Prof. George M. Whitesides. Dr. Estroff's research centers on the fundamental mechanisms of crystal growth, biomineralization, and pathological mineralization. She investigates how organisms control mineral formation and applies these principles to engineer synthetic materials with complex structures and functionalities. Her work spans biomaterials, tissue engineering, and energy materials—particularly hybrid organic-inorganic perovskites for photovoltaics. She employs advanced characterization techniques and has pioneered in situ methods to monitor crystallization dynamics. Her recent publications reveal a strong trend toward interdisciplinary research, integrating materials science with cancer biology, immunology, and machine learning. The articles emphasize bio-inspired synthesis, mineral-tissue interactions, and the development of functional crystalline materials for medical and energy applications. Faculty Early CAREER Award, National Science Foundation (2009) Fiona Ip Li '78 and Donald Li '75 Excellence in Teaching Award, Cornell College of Engineering (2007) Marilyn Emmons Williams Award, Cornell Undergraduate Research Board (2009) Keynote Speaker, Gordon Research Seminar on Biomineralization (2012) Lawrence Berkeley National Lab Affiliate (2013) Dr. Estroff leads a major DOE-funded project titled “Formulation Engineering of Energy Materials via Multiscale Learning Spirals,” a $3 million, three-year initiative using machine learning to optimize perovskite synthesis for solar cells. She has advised numerous graduate students and postdoctoral researchers, and her lab is known for fostering collaborative, cross-disciplinary research. She has also contributed to educational initiatives at Cornell, particularly in undergraduate research and materials education. Her research group operates at the intersection of chemistry, engineering, and biology, focusing on high-resolution characterization of biominerals, in situ crystal growth studies, and the design of in vitro models for cell-mineral interactions. The lab actively collaborates with institutions including Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, and Johns Hopkins University.
Professor Fernando Calamante is a Professor of Biomedical Engineering at The University of Sydney and Director of Sydney Imaging Core Research Facility. He leads the National Imaging Facility node and focuses on advanced MRI methodologies, particularly Diffusion and Perfusion MRI, to study brain connectivity and neurological disorders. His work includes developing the MRtrix software, widely used in diffusion MRI analysis. He holds extensive funding (~$50M) and has been recognized with awards like ISMRM Fellowship and NHMRC grants. His research spans super-resolution imaging, brain connectomics, and clinical applications in stroke and tumors. Education: BSc (Physics, Argentina), PhD (Magnetic Resonance Imaging, University College London). Career highlights include leadership roles at The Florey Institute and ISMRM presidency (2021-2022). Research interests include: Novel MRI methods for brain connectivity and super-resolution imaging Applications of Diffusion and Perfusion MRI in neurology Integration of structural and functional connectomics Key achievements: Over 200 publications, software innovations, and leadership in global MRI societies.
Prof. Stephan J. Sigrist is a Full Professor of Genetics at the Institute of Biology, Free University of Berlin. His lab focuses on synaptic active zone architecture, neuroplasticity, and aging-related neurodegeneration. He leads the Collaborative Research Center 958 on Membrane Scaffolding and co-directs NeuroCure, a DFG Cluster of Excellence at Charité. Education: PhD in Molecular Genetics (1997) and Habilitation (2005) from the University of Göttingen. Positions: Einstein Professor (2014–present), Spokesperson CRC 958 (2012–present), and Co-Director NeuroCure (2009–present). Research explores presynaptic mechanisms using Drosophila and mouse models, combined with STED microscopy. Key contributions include discoveries of Bruchpilot's role in active zones and spermidine's protective effects against age-related synapse decline. Grants: Over €6 million in funding, including DFG CRCs, Einstein Foundation, and ERC Advanced Grant (2025). Awards: Einstein Professorship, Best Habilitation Award, EMBO/HFSP Fellowships. Collaborations span structural biology (e.g., Stefan Hell), neurophysiology (David DiGregorio), and aging research (Frank Madeo).
Daniel Sage is a Lecturer and Scientific Advisor at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Biomedical Imaging Laboratory (LIB) under the College of Engineering (STI) and School of Life Sciences (SV) . He specializes in bioimage informatics , structured-illumination microscopy , and deep learning applications for biomedical imaging. His work spans algorithm development for single-molecule localization microscopy (SMLM) , fluorescence imaging , and 3D reconstruction . His research group has developed open-source tools like FlexSIM for light inhomogeneity correction, DeepImageJ for integrating deep learning in ImageJ, and Steer'n'Detect for orientation-accurate template detection. His publications focus on correcting multiple-blinking artifacts in PALM, optimal transport metrics for SMLM evaluation, and contextual feature analysis for xenograft cell classification. He mentors PhD students and contributes to interdisciplinary education through courses such as Bioimage Informatics and Fundamentals of Image Analysis , emphasizing practical software solutions and Java programming for bioimage processing. His collaborations include institutions like Howard Hughes Medical Institute and Centre National de la Recherche Scientifique (CNRS) .
Sabine Süsstrunk is a Full Professor and Director of the Images and Visual Representation Laboratory (IVRL) at EPFL's School of Computer and Communication Sciences. She holds a BS in Scientific Photography from ETH Zürich, MS from Rochester Institute of Technology, and PhD from University of East Anglia. Her career includes positions at Hewlett-Packard Labs and Corbis Corporation. Her research explores computational imaging, computational photography, color processing, computer vision, and image quality. Key interests include near-infrared applications, multispectral imaging, and computational aesthetics. Her work bridges hardware and software solutions for imaging challenges. Publications demonstrate consistent focus on advancing generative models (diffusion models, neural cellular automata), 3D reconstruction (NeRF variants), and media integrity (DeepFake detection). Recent trends show increased emphasis on 3D vision, robustness in generative AI, and video analysis. Awards & Honors: IS&T/SPIE Electronic Imaging Scientist of the Year (2013) Raymond C. Bowman Teaching Award (2018) EPFL AGEPoly IC Polysphere Award (2020) 8 Best Paper/Demo Awards Fellowships: IEEE, IS&T, ELLIS, AIIA She leads the IVRL lab and advises PhD candidates while serving as President of the Swiss Science Council. Research is supported through competitive grants and industry collaborations.
Gabriele Facciolo is a Professor at the Centre Borelli, ENS Paris-Saclay, France. He is a Senior Member of the Institut Universitaire de France (IUF) and holds an Innovation Chair (2025). His research focuses on image and video processing, remote sensing, and super-resolution techniques. Current affiliations: Centre Borelli (ENS Paris-Saclay), Institut Universitaire de France His research explores advanced algorithms for satellite stereo pipelines, real-time deblurring, denoising, and explainable AI systems for legal evidence enhancement. He coordinates projects like ANR SURECAVI (Super-resolution for visible camera systems) and ANR IMPROVED (video enhancement for judicial use), with recent work on Gaussian Splatting for Earth Observation and multi-date satellite super-resolution. Notable scientific achievements include the IGARSS 2025 Top 10 Student Paper Award and leadership in projects funded by ANR (€890k) and Prime Minister's entities (SGDSN/ANSSI). His work bridges computational imaging, defense applications, and digital forensics. Project leadership: SURECAVI, IMPROVED, BOFOR Key technologies: GPU acceleration, real-time processing, optical flow estimation, RPC refinement Gabriele actively contributes to open-source tools like S2P (Satellite Stereo Pipeline), MGM (MultiGlobal Matching), and OMNIflip. He teaches in the Master MVA program and collaborates across institutions (ENPC, UPF).
Weiyu Xu is a Professor at the University of Iowa, affiliated with both the Department of Electrical and Computer Engineering and the Department of Applied Mathematical and Computational Sciences. He joined the College of Engineering in 2012 and leads the Intelligent Information Processing Lab (IIPL). Ph.D., Electrical Engineering, California Institute of Technology, 2009 M.S., Electrical Engineering, California Institute of Technology, 2006 M.S., Electronic Engineering, Tsinghua University, 2005 B.E., Information Engineering, Beijing University of Posts and Telecommunications, 2002 His research focuses on compressive sensing, information theory, signal processing, network optimization, and deep learning applications in medical imaging and cybersecurity. He has made significant contributions to adversarial attack robustness, distributed optimization algorithms, and medical imaging techniques for OCT segmentation and brachytherapy. Recent publications highlight trends in Adversarial machine learning Medical imaging algorithms Compressed sensing for diagnostics Optimization in wireless communication He has also contributed to federated learning over tree networks and theoretical guarantees for sparse signal recovery. Weiyu Xu's lab, IIPL, integrates deep learning with classical optimization and signal processing. His work bridges foundational theories (e.g., information-theoretic robustness) with real-world applications in healthcare (e.g., cancer treatment planning) and communication systems (e.g., MIMO channel estimation).
Professor Trevor Lithgow is a Research Professor in Microbiology at Monash University, affiliated with the Monash Biomedicine Discovery Institute. He holds a PhD from La Trobe University (1992) and has held fellowships including the ARC Federation Fellowship (2008) and ARC Laureate Fellowship (2014). His research focuses on bacterial cell biology, antimicrobial resistance (AMR), and phage therapies, leveraging nanoscale imaging techniques like cryo-EM and super-resolution microscopy. He leads the Monash Centre to Impact AMR, an interdisciplinary initiative addressing global AMR challenges through collaborations across engineering, social sciences, and clinical medicine. Key achievements include the HFSP Tenth Anniversary Award (1999), Lemberg Medal (2020), and Royal Society of Victoria Medal (2017). His work on bacterial outer membrane assembly, phage-bacteria interactions, and structural analysis of the mitochondrial TOM complex has advanced understanding of pathogen resilience and novel antimicrobial strategies. Current projects include developing phage therapies and cross-sectoral AMR surveillance frameworks. Education: PhD in Biochemistry (La Trobe University, 1992) Research Interests: Bacterial cell surface visualization, nanoscale imaging, phage biology, AMR mechanisms Leadership: Director of Monash Centre to Impact AMR since 2020 Awards: 10+ national/international honors, including ARC Fellowships Publications span over 250 articles on bacterial membrane biology, AMR dynamics, and phage applications, with recent focus on polymyxin dependence in Acinetobacter and phage-driven resistance resensitization.
