Dr. Liu Junmin is an Assistant Professor at the School of New Materials and New Energy , Shenzhen University of Technology. He holds a PhD in Engineering from Shenzhen University and has a strong background in optical communication technologies and all-optical information devices. Education: PhD (2016-2019), Shenzhen University, School of Optoelectronic Engineering Master (2010-2013), Hunan University, School of Information Science and Engineering Bachelor (2006-2010), Hunan University, School of Information Science and Engineering His research spans high-speed optical communication systems, orbital angular momentum mode multiplexing, and intelligent optimization algorithms applied to optical signal processing. He has pioneered work in atmospheric turbulence compensation using deep learning and developed novel optical devices for beam shaping and modulation. Recent publications demonstrate expertise in diffractive neural networks (2021), turbulence mitigation (2019), and Q-switched fiber lasers (2018), with a focus on practical implementations for next-generation optical networks. He currently leads multiple high-impact research projects, including the Shenzhen Basic Research Key Project (RMB 2.5M) and Guangdong Natural Science Foundation initiatives, focusing on stable support for photonic innovation and talent development.
Junjie Yao is the Jeffrey N. Vinik Associate Professor of Biomedical Engineering at Duke University's Pratt School of Engineering. He holds multiple appointments including Associate Professor of Biomedical Engineering, Associate Director of External Partnerships in the Fitzpatrick Institute of Photonics, Affiliate of the Duke Global Health Institute, Faculty Network Member of the Duke Institute for Brain Sciences, and Member of the Duke Cancer Institute. His research focuses on developing cutting-edge photoacoustic tomography (PAT) technologies and translating these advances into diagnostic and therapeutic applications. Dr. Yao received his Ph.D. from Washington University in St. Louis in 2012. His research interests span photoacoustic tomography technologies, with particular emphasis on functional brain imaging and early cancer theranostics. At his PI-Lab, he develops PAT technologies with advanced imaging performance in spatial resolutions, imaging speed, penetration depth, detection sensitivity, and functionality. His work encompasses all aspects of PAT technology innovations, including efficient light illumination, high-sensitivity ultrasonic detection, super-resolution PAT, high-speed imaging acquisition, novel PA genetic contrast, and precise image reconstruction. Dr. Yao's publication record demonstrates consistent innovation in photoacoustic imaging, with a clear progression from fundamental technology development toward clinical applications. His recent work (2023-2025) shows a strong focus on deep-tissue imaging, super-resolution techniques, multimodal integration (particularly combining photoacoustic with ultrasound and other modalities), and translation to specific clinical applications including brain imaging, cancer detection, and urological procedures. His research increasingly incorporates machine learning approaches for image reconstruction and enhancement, while maintaining strong emphasis on the physical principles underlying photoacoustic phenomena. Fellow, Optica (formally OSA), 2022 Early Career Development (CAREER) Award, National Science Foundation (NSF), 2022 Young Investigator Award, IEEE Photonics Society, 2019 Collaborative Sciences Award, American Heart Association, 2018 Multiple Seno Medical Best Paper Awards at SPIE conferences (2013, 2015, 2016) Dr. Yao has secured significant research funding from major organizations including the National Science Foundation, National Institutes of Health, American Heart Association, and industry partners. His current grants include a $750,000 NSF CAREER award for mapping deep brain functions, multiple NIH R01 grants for stroke research and kidney stone treatment, and industry collaborations with Eli Lilly. His lab actively collaborates with clinical researchers across Duke, particularly in neurology, oncology, and urology, demonstrating strong translational focus. Dr. Yao's PI-Lab serves as a hub for developing and disseminating PAT technologies to both research and clinical communities, with particular emphasis on making these advanced imaging capabilities accessible for studying tumor angiogenesis, cancer hypoxia, brain disorders, and for clinical applications in cancer screening and melanoma staging.
Dr. Jon Petzing is a Senior Lecturer in Metrology and Director of Undergraduate Admissions at Loughborough University's Wolfson School of Mechanical, Electrical & Manufacturing Engineering. He holds a BEng (Hons) in Mechanical Engineering, a Diploma of Industrial Studies (DIS), and a PhD in Optical Metrology, all from Loughborough University. His career spans roles in aerospace, confectionery manufacturing, and academia, with a focus on optical metrology and precision engineering. Research interests include flow cytometry data analysis, low-coherence interferometry, surface texture parameters, and manufacturing process optimization. Notable contributions include advancements in automated data analysis for cell therapy manufacturing and innovations in in-situ measurement techniques. Key awards include Chartered Engineer status (CEng), Fellow of the Higher Education Academy (FHEA), and the Helmut N Friedlaender Award for Leadership. Publications highlight trends in synthetic data applications, operator variability reduction, and interdisciplinary metrology solutions for biomedical and industrial challenges. His work bridges academic research with practical manufacturing and clinical applications, emphasizing precision and automation.
Aydogan Ozcan is a Professor of Electrical and Computer Engineering, Bioengineering, and Surgery at the University of California, Los Angeles (UCLA). He holds appointments in the Henry Samueli School of Engineering and Applied Science. His research focuses on developing innovative optical and computational tools for healthcare, diagnostics, and global health challenges. Key interests include lens-free microscopy, point-of-care diagnostics, machine learning applications in healthcare, and portable biomedical imaging systems. Ozcan pioneered low-cost diagnostic technologies using smartphones and has contributed to breakthroughs in virtual staining, label-free imaging, and nanoparticle-based sensors. His work spans over 500 publications, with recent emphasis on integrating AI with microscopy for rapid disease detection (e.g., Lyme disease, cardiac biomarkers, amyloid deposits). Major grants include NIH R21 awards for projects like lens-free polarized microscopy and nano-plasmonic sensors for HIV monitoring. Notable innovations include the BlurScope microscope for automated HER2 scoring and virtual staining techniques for pathology. His labs and teams collaborate across disciplines to translate optical innovations into scalable health solutions.
Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.
Alireza Marandi is a Professor of Electrical Engineering and Applied Physics at the California Institute of Technology (Caltech), leading the Nonlinear Photonics Laboratory. He holds academic positions since 2017, including Visiting Associate (2017-18), Assistant Professor (2018-24), and current Professor (2024-). His research focuses on nonlinear photonics, ultrafast optics, quantum optics, and optical information processing, with applications in sensing, computing, and spectroscopy. Marandi earned a B.S. from the University of Tehran (2006), M.S. from the University of Victoria (2008), and Ph.D. from Stanford University (2013). Research interests include nanophotonic devices, optical frequency combs, mid-infrared photonics, and topological photonics. His lab develops technologies like integrated optical parametric oscillators, ultrafast lasers, and neuromorphic photonic systems. Awards include the 2023 DARPA Young Faculty Award and a Sloan Fellowship. He advises students in advanced photonics research, including Saransh Sharma, Ryoto Sekine, and Louise Schul. His work bridges experimental and theoretical photonics, addressing challenges in quantum computing, optical sensing, and high-speed signal processing. Key innovations include cross-comb spectroscopy, all-optical recurrent neural networks, and topological lasing systems. His lab collaborates on projects funded by DARPA and other agencies, advancing photonic technologies for next-generation computing and sensing applications.
Dr Chris Moore is a Research Fellow in the Faculty of Mathematics at the University of Cambridge, specializing in gravitational wave physics within the Relativity and Gravitation research group. His work focuses on data analysis techniques for gravitational wave detection, particularly related to the groundbreaking LIGO observations including the historic GW150914 event - the first direct detection of gravitational waves from a binary black hole merger. Moore's research interests center on gravitational wave data analysis, parameter estimation techniques, and testing general relativity through gravitational wave observations. His work combines advanced statistical methods with deep theoretical understanding of gravitational physics. A significant portion of his research involves developing and refining Gaussian process regression techniques for gravitational wave parameter estimation, which has proven crucial for extracting maximum information from gravitational wave signals. Analysis of Moore's publication record reveals a strong focus on the first gravitational wave detections by Advanced LIGO, particularly the binary black hole merger events. His work spans multiple aspects including detector characterization, data analysis methodology, parameter estimation, astrophysical interpretation, and tests of general relativity. The recurring themes across his publications demonstrate expertise in both the theoretical framework of gravitational wave generation and the practical challenges of detecting and analyzing these extremely weak signals. Moore is an active member of the international gravitational wave research community, contributing to numerous collaborative papers with the LIGO Scientific Collaboration and Virgo Collaboration. His research has been published in leading journals including Physical Review Letters, Physical Review D, and Classical and Quantum Gravity. Based in Room B2.08 at the University of Cambridge, Moore maintains an active research program in gravitational wave astronomy, contributing to one of the most exciting frontiers in modern physics. His work continues to advance our understanding of black holes, neutron stars, and the fundamental nature of gravity through the new window of gravitational wave astronomy.
Amanda Foust is an Associate Professor in the Department of Bioengineering at Imperial College London's Faculty of Engineering. She leads the Optical Neurophysiology Laboratory, focusing on developing advanced optical and computational strategies for cellular-resolution manipulation and readout of membrane potential in neuroscience research. PhD, Yale University MPhil, Yale University BSc, Washington State University Her research spans interdisciplinary domains including Neuroscience , Biomedical Engineering , and Optical Physics . Key areas of interest involve neurophotonics, brain circuit imaging, and voltage-sensitive optical technologies. Recent work emphasizes deep learning integration with light-field and two-photon microscopy for high-throughput neural activity analysis. Scientific publications demonstrate expertise in cellular-resolution imaging, voltage-sensitive optical probes, and computational methods to mitigate optical scattering. Her 2023-2025 articles highlight hybrid approaches combining physics-based models with AI for neuroimaging advancements, particularly in voltage and calcium transient detection.
Yanjing Li is an Assistant Professor in the Department of Computer Science (Systems Group) at the University of Chicago. She holds a Ph.D. in Electrical Engineering from Stanford University, an M.S. in Mathematical Sciences (with honors) and a B.S. in Electrical and Computer Engineering (with a double major in Computer Science) from Carnegie Mellon University. Her research focuses on computer architecture, hardware security, emerging technologies, and the intersection of machine learning with robust system design. Education: Ph.D., Electrical Engineering, Stanford University M.S., Mathematical Sciences (Honors), Carnegie Mellon University B.S., Electrical and Computer Engineering (Double Major in Computer Science), Carnegie Mellon University Research Interests: Professor Li explores efficient, intelligent, and secure computer architectures. Key areas include photonic interconnects, fail-secure architectures, resilience in deep learning accelerators, and cross-layer system design. Her work emphasizes energy-efficient computing and hardware security against failures and attacks. Awards & Honors: 2022 DAC Under-40 Innovators Award 2021 Google Research Scholar Award 2015 Intel Labs Gordy Academy Award Outstanding Dissertation Award (European Design and Automation Association) Multiple Best Paper Awards at IEEE/ACM Conferences Advising & Projects: Leads the YLab, mentoring PhD students like Yi He and Zequan Zhou. Active projects include UpDown (graph analytics acceleration), photonic interconnects funded by NSF/E2CDA, and fail-secure architectures. Collaborates on NSF-funded photonic computing research with UIUC and UChicago teams. Labs & Groups: Directs YLab and contributes to the Systems Group, fostering interdisciplinary work in systems, programming languages, and hardware-software co-design.
Andreas E Vasdekis serves as an Associate Professor in the Department of Physics at the College of Science, University of Idaho, with cross-affiliated roles as Research Faculty at the Institute for Health in the Human Ecosystem, Member of the Initiative for Bioinformatics and Evolutionary Studies, and Participating Faculty at the Institute for Modeling Collaboration and Innovation. His academic foundation includes a PhD in Physics (2008) and MSc in Optics and Photonics (2003) from the University of St Andrews, United Kingdom, and a BSc in Physics (2002) from the University of Athens, Greece. Dr. Vasdekis pioneers biophysical imaging methodologies, specializing in label-free microscopy and single-cell quantitative analysis . His research integrates optofluidics , deep learning , and Raman spectroscopy to decode cellular heterogeneity, metabolic noise, and nongenetic plasticity. Key innovations include photon-sparse bioimaging, Airy light-sheet microscopy, and gradient retardance optical techniques for tissue and microbial systems. Recent publications reveal a dominant trend toward computational bioimaging , where deep learning reconstructs high-fidelity images from near-zero photon data, and multimodal light-sheet platforms enabling 3D analysis of plant-microbe symbiosis, malaria parasite dynamics, and multicellular gradient responses—all unified by a physics-driven approach to biological complexity. He actively collaborates through the Initiative for Bioinformatics and Evolutionary Studies and the Institute for Modeling Collaboration and Innovation, driving interdisciplinary convergence in biophysics, data science, and biological discovery.
Professor Albrecht Stroh is a Professor of Physiology and Director of the Institute of Physiology I at University Hospital Münster (UKM). He also serves as Research Group Leader and Head of the Mainz Animal Imaging Center (MAIC) at the Leibniz Institute for Resilience Research (LIR) in Mainz, Germany, and holds a Tenured Associate Professor position (W2) of Molecular Imaging and Optogenetics at the Institute of Pathophysiology, University Medical Center of the Johannes Gutenberg University Mainz. Professor Stroh's educational background includes: PhD studies (2002-2005) at Charité University Medicine Berlin Diploma in Biophysics (2001) from Humboldt University Berlin Studies in Biology (1994-1996) at Free University Berlin His research focuses on understanding the neural basis of resilient behavior in mouse models, particularly examining how neural networks adapt and maintain functionality despite pathological changes. His work primarily investigates the initial processing of sensory afferents in the cortex, local representation, and cortico-cortical processing in relation to resilient versus susceptible behavior. A key emphasis is placed on slow network oscillations and their role in resilience. His publications demonstrate a consistent focus on neural network dynamics, imaging techniques, and the relationship between molecular/cellular changes and functional outcomes. Professor Stroh has received numerous scientific awards and funding, including: The Kurt-Decker-Award from the German Society for Neuroradiology (DGNR) Significant DFG funding for advanced imaging equipment including a 9.4 T small animal MRI (2 M EUR) and two-photon microscopes (1.54 M EUR) A Young Investigators Award from the Roland-Ernst-Foundation A Certificate of Merit from ESMRMB 2009 His research group collaborates extensively with international partners including Stanford University, University of Washington, and Seattle Children's Research Institute. Current projects include 'Learning Resilience,' investigating neural excitability regulation, studying brain-wide resting networks in relation to resilience, and examining the role of spontaneous activity in mesoprefrontal circuits.
Gajendra Kumar is an Assistant Professor of Molecular Biology, Cell Biology, and Biochemistry at Brown University, affiliated with the Data Science Institute and Carney Institute for Brain Science. His research focuses on neurodegenerative diseases, combining electrophysiology, optogenetics, neurocomputation, and AI/ML to study motor neural circuits. Notable contributions include developing closed-loop deep brain stimulation platforms for ataxia and AI-driven tools for depression detection using EEG/facial recognition. Education: PhD (2013), MSc (2007), BSc (2005) from All India Institute of Medical Sciences. Awards include Gold Medal (IGED 2022) and Hong Kong SciTech Pioneer (2022). Collaborates with experts like Prof. Eric Morrow and Prof. Judy Liu. His work spans neural circuit modeling, neuroregeneration, and neurotechnology commercialization through ventures like AniTech Limited.
Dr. Yong Xin is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo (SUNY). His research focuses on soft matter modeling, electrospray deposition, and nanoparticle interactions with biological systems. He holds a PhD in Mechanical Engineering from Rensselaer Polytechnic Institute (2012) and a BS in Physics and Economics from Peking University (2007). His work has been recognized with awards such as the Binghamton University Watson School Early Stage Distinguished Research Award (2020) and ACS Petroleum Research Fund Doctoral New Investigator Award (2016). Dr. Xin’s research spans interdisciplinary areas including colloidal transport, hydrodynamics of microswimmers, and polymer nanocomposite design. He employs advanced computational methods like molecular dynamics and dissipative particle dynamics to study phenomena such as nanoparticle-cell interactions and vesicle dynamics. His recent projects investigate environmental impacts of nanoparticles and novel applications of Janus particles in materials science. Key contributions include developing hybrid modeling frameworks for biobased nanocomposites and pioneering studies on electrospray-driven particle assembly. His lab integrates experimental and computational approaches to tackle challenges in biomaterials, nanotoxicology, and fluid-structure interactions. Current efforts focus on advancing understanding of bacterial outer membrane behavior under environmental stressors and optimizing nanoparticle synthesis for biomedical applications. Awards: Binghamton Watson Award (2020), ACS Doctoral New Investigator Award (2016) Grants: NYS/UUP Individual Development Awards (2016-2017) Labs/Teams: Soft Matter and Biofluids Lab at UB
David B. Sanders is a Professor of Astronomy at the University of Hawaii's Institute for Astronomy (IfA) Mānoa campus, specializing in the study of Luminous Infrared Galaxies (LIRGs) and their connections to broader cosmic phenomena. His research spans cosmology, galaxy evolution, and interstellar medium dynamics, with a focus on understanding galactic mergers, star formation, and black hole growth. As a key contributor to major astrophysical surveys such as COSMOS-Web Euclid GOALS (Great Observatories All-Sky LIRG Survey) Acretion History of AGN (AHA) BAT AGN Spectroscopic Survey (BASS) , he combines observational data with theoretical modeling to unravel the universe's evolutionary timeline. Sanders' work leverages advanced technologies like JWST , Euclid's infrared detectors , and machine learning for morphological classification. His research also addresses critical cosmological questions regarding dark energy , galaxy mergers , and feedback mechanisms in starburst systems. He is deeply involved in data analysis infrastructure for future missions like WFIRST and Roman Space Telescope .
Erin Kado-Fong is a YCAA Postdoctoral Fellow at Yale University's Department of Astrophysical Sciences. Her research focuses on observational studies of galaxy structure and formation, particularly the role of environment and star formation in low-mass dwarf galaxies. She utilizes wide-field surveys like SAGA and Merian to analyze large samples of galaxies beyond the Local Volume. Education: PhD in Astrophysical Sciences from Princeton University (2022, advised by Meg Urry); B.S. in Astrophysics from Tufts University (2017). Awards include the Porter Ogden Jacobus Fellowship (Princeton) and the 2021 Ohio State CCAPP Prize. Grew up in Davis, California. Research interests include: Tracing star formation histories in dwarf galaxies Environmental effects on galaxy evolution Structural properties of low-mass galaxies Development of observational survey techniques Her recent work examines tidal interactions between dwarf galaxies and their impact on star formation, as well as mass-size relations in Milky Way analog satellite systems. She collaborates on projects like the Subaru Prime Focus Spectrograph (PFS) and contributed to the HFF-DeepSpace photometric catalogs. Awards and recognition include specialized fellowships and prizes recognizing her contributions to understanding galaxy evolution mechanisms.
