Edward Andò is a Principal Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) , with affiliations to the IMAGING group and the College of Engineering (ENAC) . His work bridges software development, experimental geomechanics, and educational initiatives in image analysis. Principal Scientist, IMAGING-GE (EPFL) Lecturer, Sciences et Génie Civil (SGC-ENS) Lecturer, Enseignement à la Défense (EDEE-ENS) Research Interests Andò specializes in 3D image analysis , with a focus on X-ray tomography , digital volume correlation (DVC) , and micromechanical modeling of granular materials. His work addresses geomechanical failure mechanisms, soil dynamics, and open-source software tools like SPAM for practical material analysis. Publication Trends His recent articles (2025–2023) emphasize X-ray tomography for studying granular deformation , rock failure , medical imaging , and soft particle compaction . Topics span geomechanics, computational modeling, and software development for experimental validation. Labs and Teams Andò contributes to the IMAGING group at EPFL, where he co-develops the SPAM (Software for Practical Analysis of Materials) . His teaching includes courses like Fundamentals of Image Analysis and Quantitative Imaging for Engineers , which integrate hands-on training with theoretical frameworks.
Noel T. Clemens serves as a Professor and holds the prestigious Clare Cockrell Williams Centennial Chair in Engineering within the Aerospace Engineering and Engineering Mechanics Department at the University of Texas at Austin's Cockrell School of Engineering. He has been a faculty member since 1993 and served as department chair from 2012 to 2020. His research laboratory is part of the Center for Aeromechanics Research (CAR) where he directs the Flowfield Imaging Laboratory. Dr. Clemens' research focuses on experimental investigations of hypersonic flows, turbulent combustion, and advanced optical diagnostic techniques. His current work emphasizes 3D shock wave/boundary layer interactions, inlet unstart control, flashback in high-pressure combustors, turbulent combustion with non-equilibrium effects, and high-temperature ablation phenomena. He has pioneered laser-based measurement techniques for extreme environments, particularly for hypersonic flight applications where conventional measurement approaches fail. His recent publication record through 2025 demonstrates continued leadership in experimental fluid dynamics, with particular emphasis on plasma diagnostics for ablation studies, shock/boundary layer interaction physics, and advanced optical measurement techniques for extreme environments. The research spans fundamental fluid mechanics investigations to applied aerospace engineering problems relevant to hypersonic vehicle development. Elected to National Academy of Engineering (2024) AIAA Aerodynamic Measurement Technology Award (2022) Elected AIAA Fellow (2019) National Science Foundation Presidential Faculty Fellow (1996) Editor-in-Chief of Experiments in Fluids (2009-2013) Fellow of the American Physical Society Dr. Clemens has secured substantial research funding for his experimental investigations in hypersonics and combustion, leading multiple major research projects with government and industry partners. His laboratory facilities include advanced wind tunnels and state-of-the-art optical diagnostic systems for high-speed flow visualization. The Flowfield Imaging Laboratory at UT Austin serves as a national resource for advanced flow measurement techniques development. As an educator, he teaches core courses in compressible flow, viscous flow, combustion, experimental methods, and laser diagnostic techniques, training the next generation of aerospace engineers in both fundamental principles and cutting-edge measurement technologies.
Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.
Prof. Dr. med. Franz Lennard Ricklefs is a Senior Physician and Head of the Working Group at the Department of Neurosurgery, University of Hamburg Faculty of Medicine. He is a Medical Specialist in Neurosurgery with cross-disciplinary expertise in neuro-oncology, molecular pathology, and extracellular vesicle research. Affiliations: University Medical Center Hamburg-Eppendorf (UKE), European Liquid Biopsy Society (ELBS), International Consortium on Meningiomas (ICOM) Research Interests: His work focuses on neurosurgical oncology, particularly glioblastoma and meningioma pathobiology. He investigates DNA methylation patterns, extracellular vesicle biomarkers, and liquid biopsy implementation in clinical neuro-oncology. Additional interests include surgical outcomes for epilepsy and aneurysm management. Article Trends: Over the last decade, Dr. Ricklefs has published extensively on: Extracellular vesicle applications as liquid biopsy markers DNA methylation subclasses for glioblastoma and meningioma Multicenter surgical outcome benchmarking Immune evasion mechanisms in neuro-oncology Technological innovations in neurosurgical visualization Molecular characterization of rare CNS tumors Professional Contributions: He co-authored the MISEV2023 guidelines for extracellular vesicle studies and participates in international consensus reviews for meningioma classification. His collaborations span institutions across Europe and North America.
Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.
Patrick Skeba is a Teaching Assistant Professor at the University of Pittsburgh's Department of Computer Science within the School of Computing and Information. He holds a PhD in Computer Science from Lehigh University (2022) and bachelor's degrees in Cognitive Science and Computer Science from Johns Hopkins University (2017). His research focuses on internet privacy, AI ethics, and the responsible use of data. He teaches courses in machine learning and programming. Research Interests: Skeba's work bridges technology and societal impact, emphasizing privacy risks in data systems, algorithmic fairness, and user-centric privacy frameworks. His recent studies explore informational friction in data collection, community-based privacy strategies, and lay-expert disparities in understanding privacy-enhancing technologies (PETs). Publications: His articles analyze privacy dynamics in digital spaces, from pandemic-era discourse on r/privacy to methodological approaches for categorizing technology non-use. His earlier work includes breakthroughs in sleep disorder diagnostics, particularly periodic leg movement (PLM) analysis and telemedicine applications for neurological conditions. Awards: No scientific awards listed. Grants and advising details are currently unspecified. Labs/Teams: No specific lab affiliations mentioned in provided materials. His teaching and research emphasize collaboration across computational and social domains.
Teng-Fong Wong is a Research Professor in the Department of Geosciences at Stony Brook University, where he has been a faculty member since 1982. His research focuses on the intersection of rock mechanics, earthquake processes, and environmental applications, making significant contributions to understanding deformation mechanisms in geological materials. Education: Sc.B., Brown University, 1973 M.S., Harvard University, 1976 Ph.D., Massachusetts Institute of Technology, 1981 Research Interests: Professor Wong's research centers on rock mechanics with emphasis on earthquake mechanics, energy resources, and environmental applications. He investigates both phenomenological and micromechanical aspects of rock deformation and fluid flow using an integrated approach combining high-pressure deformation experiments, quantitative microstructure characterization, and theoretical analysis. His work spans brittle-ductile transitions in porous rocks, permeability evolution, strength properties of fault zone materials from SAFOD and TCDP drilling projects, and submarine groundwater discharge systems. Publication Trends: Wong's recent publications (2006-2008) demonstrate a consistent focus on strain localization mechanisms in porous rocks, particularly examining compaction bands and deformation bands in sandstones. His work integrates advanced imaging techniques (X-ray radiography, CT scanning) with mechanical testing to understand the micromechanics of rock failure. A significant thread connects his research on fault zone properties from major drilling projects (SAFOD, TCDP) with fundamental rock deformation processes. Scientific Recognition: U.S. Patent 6,874,371 for Ultrasonic Seepage Meter (2005) U.S. Patent 7,107,859 for Ultrasonic Seepage Meter (2006) Co-author of "Experimental Rock Deformation - The Brittle Field" (2nd Edition, Springer-Verlag, 2005) Professional Activities: Professor Wong maintains an active international research profile with numerous visiting appointments including at Australian National University, MIT, ETH Zurich, and institutions in China and France. His work involves extensive collaboration with USGS and international research teams on major fault zone drilling projects. He has developed specialized equipment like the ultrasonic seepage meter for measuring submarine groundwater discharge. Research Infrastructure: Wong's laboratory utilizes advanced capabilities including high-pressure deformation equipment, 3D visualization through laser scanning confocal microscopy and synchrotron microCT, and integrates these with analytic modeling and numerical simulation techniques (finite element and discrete element methods) to investigate micromechanics of dilatant and compactant failure in geological materials.
Professor Daniel Catchpoole serves as Deputy Head of School (Research) at the School of Computer Science, University of Technology Sydney (UTS), holding dual appointments at UTS and The Children's Hospital at Westmead. With over 20 years of research experience, he bridges computational sciences and pediatric cancer research through the Biomedical Data Science Lab in the Australian Artificial Intelligence Institute. His work integrates data analytics, artificial intelligence, and software development with molecular cancer biology to transform pediatric cancer treatment pathways. PhD in Cancer Cell Biology, University of New South Wales (1991-1995) Founding Fellow, Royal College of Pathologists Australasia (2010-present) Head, Children's Hospital at Westmead Tumour Bank (2001-present) Professor Catchpoole's research focuses on translational applications of genomics in childhood cancers, particularly acute lymphoblastic leukemia and neuroblastoma. His work combines high-throughput genomic technologies with advanced computational analysis to develop systems biology approaches for cancer patient assessment. Recent projects explore virtual reality applications for complex genomic data visualization and copper chelation therapies to enhance neuroblastoma immunotherapy. His research has received significant funding from Cancer Institute NSW, Sony Foundation, ARC, and NHMRC. His publication record spans biomedical data science, cancer genomics, and virtual reality applications in oncology. Recent work demonstrates leadership in 3D latent diffusion models for tumor segmentation, biobank economics, and innovative immunotherapies. His research consistently addresses the critical need for actionable knowledge from complex multidimensional biomedical data. Editorial Board Member, Cancers (2023) Associate Editor, Innovations in Digital Health, Diagnostics and Biomarkers (2019) Founding member and first President, Australasian Biospecimens Network Association Professor Catchpoole has supervised 17 Honours students (including 6 First Class Honours), 3 MSc students, and 12 PhD candidates across multiple institutions, with 6 current PhD students. His collaborative research bridges UTS's Faculty of Engineering and IT with The Children's Cancer Research Unit at The Children's Hospital at Westmead. Significant research funding includes Cancer Institute NSW grants, Sony Foundation VR projects, and ARC Discovery Projects focused on genomic data analysis and clinical decision support systems. His leadership extends to building frameworks for translational research, managing biobanks and clinical data linkages, and navigating governance requirements for cancer research. The Tumour Bank at Kids Research, CCRU, represents his long-standing commitment to pediatric cancer infrastructure development.
Dr. Bin Zhu is a Research Fellow in the School of Mechanical Engineering Sciences at the University of Surrey, affiliated with the Centre for Engineering Materials. He obtained his PhD from the same institution, focusing on multiscale residual stress evaluation and mechanical property characterization using microscopy and large-scale facilities. His research develops techniques for harsh environments to enhance material longevity by managing manufacturing-induced residual stress, with applications in nuclear fusion components. Education PhD, University of Surrey (Research focus: Multiscale residual stress evaluation and mechanical property characterization) Research Focus Dr. Zhu's research centers on three interconnected areas: 1) Multiscale residual stress evaluation using advanced techniques like plasma-focused ion beam and neutron diffraction; 2) In situ mechanical testing under extreme conditions; and 3) Computational modeling for predicting stress distributions and material behavior. His work primarily addresses nuclear fusion reactor challenges, particularly laser-welded Eurofer97 steel components, where residual stress critically impacts structural integrity. Publication Trends Dr. Zhu's recent publications (2021-2025) demonstrate three key themes: 1) Advanced residual stress analysis in nuclear materials using machine learning, neutron imaging, and synchrotron techniques; 2) High-temperature mechanical performance of welded joints for fusion reactors; and 3) Biomimetic material characterization, including bioinspired composites and biological light-diffraction mechanisms. His methodologies consistently integrate multiscale experimental approaches with computational modeling.
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.
Indrani Bhattacharya, PhD, is an Assistant Professor in the Department of Biomedical Data Science and the Center for Precision Health and Artificial Intelligence (CPHAI) at Dartmouth College's Geisel School of Medicine. Her research focuses on developing human-centered AI systems for healthcare, particularly in multimodal medical imaging and behavioral health analytics. She holds a BS in Electrical Engineering from Jadavpur University (India), and MS/PhD from Rensselaer Polytechnic Institute (USA). Postdoctoral training at Stanford University's Department of Radiology further specialized her in biomedical imaging informatics. Research interests include: Integrating imaging and non-imaging data for precision medicine AI-driven prostate cancer detection/classification Multimodal behavior estimation for doctor-patient interactions Privacy-preserving sensor systems for group interaction analysis Her work bridges computer vision, medicine, and social science, with recent breakthroughs in MRI-ultrasound fusion AI outperforming radiologist interpretations in multi-center studies. Active in AI ethics and translational research, she leads teams developing clinical decision support tools for oncology and behavioral health. Key career milestones include: Postdoctoral scholar at Stanford University School of Medicine (2016-2021) Academic research staff at Stanford Radiology (2021-2022) Founding member of Dartmouth CPHAI precision health initiatives Labs/Teams: Leads the Biomedical AI for Healthcare group at Dartmouth, collaborating with Stanford and industry partners on AI-driven diagnostic systems.
Chung-Hsing Yeh is an Associate Professor at Monash University's Faculty of Information Technology, Department of Data Science & AI. He holds a visiting professorship at National Cheng Kung University, Taiwan, and has extensive experience in academic roles including Chief Examiner and Lecturer for numerous IT and business-related courses. His research focuses on multicriteria decision analysis, applied artificial intelligence, fuzzy logic, neural networks, and sustainable operations management. He has led collaborative projects on e-waste recycling, supply chain optimization, and public health policy, funded by organizations like the Ministry of Science and Technology (Taiwan) and the Australian Research Council. Education: PhD in Operations Research/Information Systems, Monash University (1988) MSc in Management Science, National Cheng Kung University (1982) BSc (Engineering) in Industrial Design, National Cheng Kung University (1977) Research Interests: His work spans decision support systems, optimization modeling, transport research, and recycling operations. Notable contributions include algorithms for production scheduling, AI-driven solutions for healthcare, and sustainable e-waste management strategies. Awards: Listed in Marquis Who's Who in the World Listed in Who's Who in Finance and Industry Listed in Who's Who in Science and Engineering Grants & Projects: Led 6 major projects, including 'Maximizing E-waste Recycling Profitability' (2019–2020) and 'Smoke-Free Policy Effectiveness' (2007–2010). Active in grant review roles for ARC and the Netherlands Organisation for Scientific Research. Teaching: Overseeing courses such as Fundamentals of Artificial Intelligence, Business Intelligence Modelling, and Management Information Systems.
Glaucio H. Paulino holds the Margareta Engman Augustine Professorship in Civil and Environmental Engineering at Princeton University, where he also serves as a Professor at the Princeton Institute for the Science and Technology of Materials (PRISM). His work bridges computational mechanics, topology optimization, and materials science. Paulino leads a research group focused on advancing structural design methodologies, fracture mechanics, and functionally graded materials. His team has pioneered polygonal finite elements and multiresolution topology optimization techniques, addressing challenges in mesh bias and computational efficiency. He has published over 240 peer-reviewed articles and mentored 19 PhD and 11 MS students. Notable contributions include the PPR cohesive model for fracture analysis and adaptive mesh refinement for dynamic simulations. Paulino's research extends to practical applications such as high-rise building design and sustainable construction materials. Awards include election to the European Academy of Sciences and Arts and ASME’s Melville Medal. Current projects involve functionally graded cement-based materials, extrusion processing, and digital image correlation for material characterization. His lab collaborates with industry partners like Skidmore, Owings & Merrill LLP to translate topology optimization into real-world engineering solutions. Paulino’s interdisciplinary approach integrates computational modeling with experimental validation, fostering innovations in civil infrastructure resilience.
Eldrin F. Lewis, MD, MPH is the Simon H. Stertzer, MD, Professor of Medicine and Chief of the Division of Cardiovascular Medicine at Stanford University School of Medicine. He holds a University Medical Line Professorship within the Department of Medicine - Cardiovascular Medicine and is a member of the Cardiovascular Institute. Dr. Lewis earned his medical degree from the Perelman School of Medicine at the University of Pennsylvania (1995), completed his Internal Medicine Residency (1998), Cardiovascular Disease Fellowship (2001), and Heart Transplant Fellowship (2002), all at Brigham and Women's Hospital in Massachusetts. He maintains board certification in Cardiovascular Disease (2024) and Advanced Heart Failure and Transplant Cardiology (2025) through the American Board of Internal Medicine. As an internationally recognized expert in heart failure, heart transplant, and quality of life for heart failure patients, Dr. Lewis' research focuses on patient-reported outcomes, quality of life assessment, and the integration of digital health technologies into cardiovascular care. His fundamental principle is that "there is more to life than death," emphasizing that cardiovascular care should help patients not only survive but also enjoy the best possible quality of life. His extensive publication record includes nearly 200 peer-reviewed articles in top journals including the New England Journal of Medicine, Journal of the American College of Cardiology, Circulation, JAMA Cardiology, and JAMA Internal Medicine. His recent work (2024-2025) demonstrates strong focus on heart failure treatment optimization, diversity in clinical trials, pulmonary congestion assessment, and the implementation of quadruple medical therapy for heart failure patients. Joel Gordon Miller Award for community service and leadership from the University of Pennsylvania School of Medicine Minority Faculty Development Award recognizing research potential of young physicians Robert Wood Johnson Foundation grant for quality of life assessment research Fellow of the American College of Cardiology Member of the American Heart Association Research Committee Dr. Lewis serves as a Postdoctoral Faculty Sponsor for Phenesse Dunlap and has received significant research support from the National Heart, Lung and Blood Institute, National Institutes of Health, and the Robert Wood Johnson Foundation. He is deeply committed to expanding clinical research initiatives at Stanford, forming partnerships with community cardiologists, and leveraging Silicon Valley's digital technology expertise for patient monitoring and treatment optimization. His leadership includes serving on the AHA Founders Affiliate Board of Directors, chairing the Council on Clinical Cardiology, and participating in the FDA Task Force for Standardization of Definitions for Endpoint Events in Cardiovascular Trials.
Geir Selbæk is a Professor II at the University of Oslo in the Department of Geriatric Medicine . His research focuses on dementia , neurodegenerative diseases , and aging-related mental health , with extensive work on the HUNT study —a large Norwegian population-based cohort. He investigates risk factors like depression , loneliness , metabolic health , and social determinants to understand dementia progression. Key research areas: Dementia epidemiology, geriatric mental health, neurodegenerative disease, aging, cognitive impairment Recent publications analyze AI in diagnostics, genetic risk scores, social media's role in pandemic isolation, and metabolic markers Collaborations Selbæk collaborates with international teams across Alzheimer's & Dementia , Nature Genetics , PLOS ONE , and other high-impact journals. His work integrates epidemiological data , neuropsychological testing , and molecular biology to address dementia prevention and care.
