Sumit Chopra is an Associate Professor at the Grossman School of Medicine , affiliated with the Department of Radiology at New York University. His work focuses on integrating machine learning and artificial intelligence with medical imaging to enhance diagnostic accuracy and clinical decision-making. Research interests include: Deep learning applications in prostate cancer imaging and MRI reconstruction Development of open-access medical imaging datasets (e.g., FastMRI Prostate) Improving biopsy decision strategies via representation learning AI-driven Alzheimer's disease risk prediction using electronic health records Advancements in radiologic assessment for pancreatic cystic lesions Email: Sumit.Chopra@nyulangone.org
Matthew J. Graham is a Research Professor of Astronomy at the California Institute of Technology (Caltech), serving as the Project Scientist for the Zwicky Transient Facility (ZTF). His work bridges astronomy, machine learning, and data science, focusing on time-domain sky surveys that produce hundreds of thousands of public transient alerts per night. Previously, he has worked on the Catalina Real-time Transient Survey (CRTS), NOAO DataLab, Virtual Observatory, and Palomar-Quest Digital Sky Survey. Dr. Graham's primary research interests involve applying machine learning and advanced statistical methodologies to astrophysical problems, particularly the variability of quasars and other stochastic time series. His work addresses the unprecedented data volumes generated by 21st-century astronomy while expanding our ability to work with complex information systems beyond simple correlations. His current projects include real-time low latency inferencing via the NSF-funded A3D3 Institute, reinforcement learning for optimizing astrophysical follow-up campaigns, neural differential models for supermassive black hole variability, and functional analysis of multivariate time series. Analysis of Graham's recent publications reveals a strong focus on time-domain astronomy, particularly leveraging the capabilities of the Zwicky Transient Facility. His work spans multiple areas including gravitational wave counterpart identification, active galactic nuclei variability, supernova characterization, and machine learning applications for transient detection. A notable trend is the integration of artificial intelligence techniques to handle the massive data streams from modern sky surveys, enabling real-time analysis and decision-making that would be impossible with traditional methods. Dr. Graham has been instrumental in developing infrastructure for time-domain astronomy, including the alert distribution system for ZTF and data processing pipelines for handling massive transient datasets. His work on the Catalina Real-time Transient Survey established important methodologies for identifying variable and transient sources that continue to influence the field. As Project Scientist for ZTF, Graham leads a major international collaboration involving Caltech, IPAC, and numerous partner institutions worldwide. The facility represents a significant advancement in time-domain astronomy, providing unprecedented coverage of the dynamic sky and enabling discoveries across multiple areas of astrophysics.
Professor Ali Yapar is a faculty member at Istanbul Technical University in the Electronics and Communication Engineering department. His research focuses on Electromagnetics , Microwave Engineering , and Antenna Technologies , with a particular emphasis on inverse scattering problems and microwave imaging for biomedical applications. He has supervised numerous graduate students and led projects related to breast cancer treatment and rough surface imaging. PhD in Electronics and Communication Engineering from Istanbul Technical University (1997) MSc in Electronics and Communication Engineering (1995) His recent publications analyze advanced techniques for microwave hyperthermia systems, reverse time migration methods, and Newton-based solutions for electromagnetic inverse scattering. Key projects include TUBITAK-funded initiatives on microwave tomography and brain stroke imaging. He serves as a project investigator and executive for electromagnetic research programs. Research areas span Electromagnetic Wave Propagation , Green's Function Applications , and Dielectric Material Analysis . Collaborations include IEEE members and international researchers in computational electromagnetics.
Prof. Flavio Anselmetti is a Full Professor of Quaternary Geology and Paleoclimatology at the University of Bern , where he has been employed since 2012. He also serves as the Managing Director of the Institute of Geological Sciences . Prior to this, he held significant roles at ETH Zurich, including SNSF Assistant Professor, and at EAWAG Dübendorf as Head of the Sedimentology Group, while remaining a Titular Professor at ETH Zurich until 2012. His academic training includes a Diploma in Geology (1990) from the University of Basel , followed by a PhD (1994) from ETH Zurich and University of Miami (USA) . He spent three years as a Postdoctoral Fellow and Research Associate at the University of Miami between 1994 and 1997. Paleoseismology : Using lacustrine and marine sediments to reconstruct earthquake and tsunami records Climate Change Studies : Investigating Holocene and Pleistocene climate shifts through sediment cores Sediment Dynamics : Analyzing source-to-sink systems and glacial sediment properties His recent publications focus on sedimentary archives in Science Advances , Quaternary Research , and Environmental Modelling and Software , with an emphasis on deep learning applications in sediment analysis , multi-proxy paleoenvironmental reconstructions , and human impact assessments over millennia. He has contributed to major projects in the Alpine foreland and Caribbean regions . Prof. Anselmetti leads the Quaternary Geology and Paleoclimatology Research Group and collaborates with international teams on ICDP-DOVE drilling campaigns and multibeam bathymetry mapping in Swiss lakes. His work extends to geohazard assessment , carbonate-siliciclastic shelf dynamics , and subaqueous fault analysis in active tectonic zones.
Stuart Long is an associate dean of undergraduate research and faculty member at the Honors College of the University of Houston, where he serves as the academic adviser for all honors students majoring in Electrical and Computer Engineering. He teaches courses on electromagnetic waves and conducts research in antenna design and applied electromagnetics. Education: Received his doctorate from Harvard University. Stuart Long's research focuses on biomedical applications of electromagnetics, particularly MRI safety testing for implantable medical devices. His work addresses RF-induced heating, electromagnetic compatibility, and safety protocols for devices such as orthopaedic implants and active implantable systems. Recent publications emphasize computational modeling, machine learning, and historical advancements in antenna design. His scholarly contributions include the 2024 Distinguished Achievement Award, the 2018 Chen-To Tai Distinguished Educator Award, and the 2014 John Kraus Antenna Award. These honors reflect his leadership in electromagnetic safety research and engineering education. Stuart Long has actively contributed to improving pedagogy in engineering education, particularly through collaborative learning and retention workshops for diverse student populations. His academic advising role supports the integration of rigorous technical training and interdisciplinary research opportunities for honors students.
Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.
Dr Vu Minh Hieu Phan is a Research Fellow at the Australian Institute for Machine Learning , University of Adelaide. His work focuses on foundational models, multimodal learning, and medical image analysis, leveraging deep learning and large language models. Research Interests : Medical Image Analysis, Vision-Language Models, Generative AI, Semantic Segmentation, Continual Learning, Knowledge Distillation. Key Venues : CVPR, ACL, EMNLP, IJCAI, MICCAI, NeurIPS, TPAMI, and IJCV. Notable Contributions include advancements in multimodal learning for medical imaging, explainable AI frameworks, and efficient knowledge distillation techniques. He serves as a reviewer for top-tier journals and conferences. Email : vu.minhhieu.phan@adelaide.edu.au
Guo Ping is an Associate Professor of Mechanical Engineering at Northwestern University, leading the Advanced Intelligent Manufacturing Laboratory (AIM). His research focuses on precision manufacturing, intelligent metrology via deep learning, and advanced manufacturing applications. He holds a Ph.D. from Northwestern University and a B.S. in Automotive Engineering from Tsinghua University. Education: Ph.D. in Mechanical Engineering, Northwestern University, Evanston, IL B.S. in Automotive Engineering, Tsinghua University, Beijing, China Research Interests: Dr. Guo’s work emphasizes innovations in precision engineering, including ductile-regime machining, smart metrology systems, and robotics-driven manufacturing. Key areas include structural coloration, additive manufacturing, and human-robot collaboration in industrial settings. His lab explores cutting-edge techniques like ultrasonic vibration machining and machine learning for defect detection and process optimization. Publications Trends: Recent work spans AI-driven quality control (e.g., photometric stereo networks), robotic swarm patterning, and wearable fatigue monitoring systems. His research bridges machine learning, robotics, and traditional manufacturing to address scalability and precision challenges. Awards: F.W. Taylor Medal (CIRP, 2023) ASME Kornel F. Ehman Manufacturing Medal (2021) SME Outstanding Young Manufacturing Engineer Award (2020) Professional Service: Associate Editor of the Journal of Manufacturing Processes (2017–present). Active in organizing conferences and reviewing for top journals. Labs & Teams: Directs the AIM Lab, which integrates robotics, AI, and advanced materials to solve problems in precision fabrication and smart manufacturing. Current projects include structural coloration for anti-counterfeiting and fatigue prediction in industrial workers.
Brad Sutton is a Professor of Bioengineering at the University of Illinois Urbana-Champaign and Technical Director of the Biomedical Imaging Center at Beckman Institute. He holds affiliate roles in the Neuroscience Program, Department of Electrical and Computer Engineering, and is a Health Innovation Professor at the Carle Illinois College of Medicine. His roles also include fellowship positions with the National Center for Supercomputing Applications and the CZ Biohub Chicago. Education: Ph.D. in Biomedical Engineering from the University of Michigan (2003). Research Interests: Focus on advanced MRI techniques for structural and functional brain imaging, including diffusion-weighted imaging, dynamic imaging, and neuromuscular coupling studies. His work emphasizes multi-scale bioimaging to understand brain function across interventions, aging, and disease. Publications: Over 180 peer-reviewed articles in 2025-2024 highlight innovations in MRI technology and applications in neuroscience, including breakthroughs in laminar fMRI specificity, myelin development modeling, and Alzheimer’s biomarker studies. Recent work extends to clinical applications like aortic imaging automation and mixed reality training tools. Awards: AIMBE and ISMRM Fellowships (2017/2024), Abel Bliss Scholar (2014-), and over 9 patents in imaging techniques. Labs & Teams: Leads the Magnetic Resonance Functional Imaging Lab. Collaborates with interdisciplinary teams across engineering, medicine, and computational science to advance imaging technologies and their clinical translation.
Vivek Boominathan is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Rice University. He is affiliated with the GLEE lab (Geometry, Light, & Imaging lab). His research focuses on computational imaging, combining computer vision, machine learning, applied optics, and nanofabrication to develop innovative imaging systems for applications such as robotics, medical sensing, and virtual/augmented reality. He has contributed to projects like PhlatCam (a lensless camera) and NeuWS (neural wavefront shaping). His work bridges optics, algorithms, and materials science to overcome traditional limitations in imaging systems. Boominathan's research interests include lensless imaging, optical meta-devices, turbulence mitigation, and bio-inspired imaging systems. He has developed systems like Foveated thermal imaging prototypes and real-time lensless microscopes. His lab emphasizes interdisciplinary approaches, integrating hardware design with machine learning. Key projects include: NeuWS: Neural wavefront shaping for imaging through scattering media CoIR: Compressive implicit radar for sensing applications FlatCam and PhlatCam: Ultra-thin lensless imaging devices Bioluminescence imaging in marine species His work has been published in top venues like Science Advances, Optica, and IEEE TPAMI. He collaborates with institutions like NASA JPL and industry partners on applied imaging solutions. Current research trends emphasize sensor-algorithm co-design and high-speed imaging systems for AR/VR applications. Boominathan holds a PhD in Electrical Engineering and has extensive postdoctoral experience in computational imaging. He advises projects in the GLEE lab and mentors students in hardware-software co-design for imaging systems. His lab focuses on translating theoretical innovations into practical devices with commercial potential.
Dr. Lauren Emberson (she/her/hers) is an Associate Professor in the Department of Psychology at the University of British Columbia, Faculty of Arts. She directs the Baby Learning Lab, which is part of UBC's Early Development Research Group, a consortium focused on infant and child development. Prior to her position at UBC, Dr. Emberson was an Assistant Professor at Princeton University where she co-founded and co-directed the Princeton Baby and Princeton Kid Labs. Education: Postdoctoral Associate, University of Rochester (PI Aslin) Ph.D, Cornell University (PIs Amso, Goldstein, Spivey) B.Sc, University of British Columbia Dr. Emberson's research focuses on learning, perception (audition, vision, crossmodal or multisensory), language development, face/object perception, and attention in infants. She investigates these capacities using behavioral and neuroimaging techniques, particularly fNIRS (functional near infrared spectroscopy), working primarily with very young infants (birth through 1 year) and preterm/premature infants. Her work examines how infants' learning capacities contribute to rapid development of perception in ecological contexts, with implications for understanding how early life experiences affect later outcomes. Analysis of Dr. Emberson's recent publications reveals a consistent focus on infant perception, learning mechanisms, and neuroimaging methodology. Her work increasingly incorporates advanced fNIRS techniques while maintaining focus on fundamental questions about how infants learn from their environment. There's a growing emphasis on individual differences, cross-cultural comparisons, and applications to infants facing developmental challenges. Dr. Emberson serves on the editorial board of Infancy (journal of the International Congress of Infancy Studies) and is a consulting editor for the Journal of Cognitive Neuroscience . Her research has been published in top journals including PNAS, Current Biology, Psychological Science, Cognition, Developmental Science, and the Journal of Neuroscience. Dr. Emberson has secured significant research funding from prestigious organizations including the Bill and Melinda Gates Foundation, James S. McDonnell Foundation, Natural Sciences and Engineering Research Council (NSERC), Canadian Institutes of Health Research (CIHR), and the National Institutes of Health (NIH). She collaborates with clinicians at BC Women's and Children's Hospitals to understand how different early life experiences impact learning and brain development. Dr. Emberson is currently accepting graduate students into her research program. The Baby Learning Lab, under Dr. Emberson's direction, is part of UBC's Early Developmental Research Group and collaborates with multiple institutions. The lab strives to provide interactive research experiences for infants and families while advancing scientific understanding of early cognitive development. The lab acknowledges that it operates on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people.
Mohammad F. Islam is a Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. His research focuses on soft materials, nanomaterials, and their applications in energy, healthcare, and manufacturing. He holds the National Science Foundation CAREER Award, Alfred P. Sloan Research Fellowship, Kavli Frontiers Fellowship, and the George Tallman Ladd Research Award. His work spans advanced materials processing, sustainable energy systems, and self-healing materials. Education: Ph.D. in Physics, Lehigh University (2000) Research Interests: Additive manufacturing and nanofabrication techniques Development of smart materials with self-healing and shape-memory properties Energy storage systems using carbon nanotube aerogels Applications in biomedicine and environmental sustainability Grants & Recognition: Recipient of multiple grants from the Wilton E. Scott Institute for Energy Innovation Co-founder of Watson Nano, commercializing carbon nanotube technologies Labs & Teams: Islam Group at CMU investigating soft matter, nanomaterials, and interdisciplinary applications
Professor Liu Xiaogang is a Distinguished Professor in the Department of Chemistry at the National University of Singapore (NUS). He holds a B. Eng from Beijing Technology and Business University, M.Sc. and Ph.D. degrees in Chemistry from East Carolina University and Northwestern University (USA), respectively, and completed postdoctoral research at MIT. His research focuses on supramolecular coordination chemistry, catalysis, chemical sensors, optogenetics, photon upconversion, and X-ray photonics. Key achievements include pioneering work on metal-organic complexes for optoelectronics and developing advanced X-ray scintillators for medical imaging. Education: B. Eng, Beijing Technology and Business University, China M.Sc. Chemistry, East Carolina University, USA Ph.D. Chemistry, Northwestern University, USA Postdoctoral Associate, Massachusetts Institute of Technology, USA Research Highlights: Professor Liu’s lab has produced groundbreaking advancements in luminescent materials, including directive giant upconversion via supercritical bound states and real-time single-proton counting scintillators. His work bridges chemistry, materials science, and biomedical applications, with notable contributions to photon upconversion, X-ray imaging technologies, and nanotheranostics. Awards: RSC Centenary Prize (2024) President’s Science Award (2016) Advising & Grants: As Principal Investigator of the Liu Lab at NUS, he oversees a dynamic research group focused on cutting-edge nanomaterials and their applications in healthcare and photonics. His grants include support for projects on X-ray luminescence imaging and optogenetic tools. Labs & Teams: The Liu Lab operates within NUS’s Department of Chemistry, collaborating with interdisciplinary teams to advance materials innovation for biomedical and environmental challenges.
Associate Professor Sonny Pham leads research in artificial intelligence at Curtin University's School of EECMS. His work balances theoretical foundations with practical applications in computer vision, data mining, and deep learning. As head of the IAMAI research group, he collaborates with industry partners on security systems, healthcare AI, and sustainable technologies. His research explores: Computationally efficient deep learning architectures Compressed sensing for high-dimensional data Robust statistical methods for real-world problems Applications in computer vision and industrial automation Recent publications demonstrate a focus on medical imaging interpretation and efficient neural networks, with applications spanning radiology report generation, semantic segmentation for autonomous systems, and cybersecurity. His team's work consistently bridges theoretical AI advancements with industrial applications. Honors include: Multiple WANMA Awards (2021-2024) for industry-impactful research INCITE Award for social impact technology (2024) IEEE Young Author Best Paper Award (2010) Over $5M in competitive research funding including MRFF and DFAT grants He leads the IAMAI research group with 12+ graduate students and coordinates Curtin's Master of Artificial Intelligence program. Industry collaborations include Alcoa Australia, iCetana, and HyprFire.
Mary Stuart is a Lecturer in Zero Carbon at the University of Derby, affiliated with the College of Science and Engineering. Her research focuses on advancing low-cost hyperspectral imaging technologies for environmental applications, particularly in glaciology, peatland ecology, and extreme environment monitoring. She specializes in leveraging smartphone-based platforms and affordable instrumentation to democratize environmental data collection. Key research areas include developing field-deployable systems for ice sheet analysis, peat health assessment, and environmental monitoring in remote locations. Her work emphasizes practical solutions for climate change research through innovative sensor design and calibration techniques. Mary has contributed to over 8 peer-reviewed articles, with notable outputs in journals like Science of The Total Environment and Remote Sensing . Her research outputs have garnered 91 total views and 39 downloads, highlighting the growing interest in accessible environmental sensing technologies. Her current projects explore spectral calibration methods for mobile sensors and the application of low-cost systems in extreme environments. Mary’s work bridges the gap between cutting-edge technology and real-world environmental challenges, prioritizing cost-effective solutions for global sustainability efforts.
