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Gintautas GrigasView profile

Associate Professor

• Programming teaching methodology
• Software localization
• Computer terminology
• +3 more

Gintautas Grigas is an Associate Professor and Affiliated Scientist at the Institute of Mathematics and Informatics , Vilnius University, Lithuania, serving within the Educational Systems Group . Since joining the Institute in 1965, he has become one of Lithuania’s most prolific authors in computer science education and software localization. Education: 1959 – Graduated, Faculty of Electrical Engineering, Kaunas Polytechnic Institute (now Kaunas University of Technology) 1970 – Candidate of Technical Sciences (post-graduate), nostrified 1992 to Doctor of Mathematical Sciences Continuing education courses in programming languages, data types and programming technology at Vytautas Magnus University, Vilnius University and Vilnius Pedagogical University Research Interests Grigas’ scientific work centres on four tightly interwoven themes: abstract data types , programming teaching methodology , computer terminology , and software localization . His contributions range from theoretical analyses of data structures to practical guidelines for localizing major software suites into Lithuanian. He has also pioneered studies on how interface defects influence national language culture and on letter-frequency analyses supporting cryptography and linguistic research. Publication Trends Across more than 150 scholarly and professional works, a clear progression is evident: early focus on programming fundamentals and Lithuanian computing history, followed by intensive investigation of localization pedagogy, and more recently, mobile-device text input and cultural impacts of internationalization errors. Collaborative volumes such as the Encyclopedic Dictionary of Computing and the textbook Fundamentals of Software Localization have become standard references in Lithuanian computer science curricula. Awards & Recognition No specific competitive awards are listed; however, his sustained output—40 books, 100+ scientific articles, 250+ popular/professional articles—is itself a testament to national academic recognition. Doctoral Advising & Grants While individual doctoral students are not named, Grigas has long contributed to doctoral education through the Institute’s doctoral committees and study guides. His research has been supported by national projects on software localization and educational technology, although explicit grant numbers are not provided in the text. Labs & Teams He carries out his work within the Educational Systems Group , a subdivision of the Institute of Mathematics and Informatics, located at Akademijos St. 4, Vilnius. The group investigates learning technologies, curriculum design, and the societal impact of digital systems, providing a collaborative environment for his ongoing projects.

Jeremy M. MikeczView profile

Researcher

• Digital Humanities
• Spatial History
• Data Visualization
• +5 more

Jeremy M. Mikecz is a Researcher at Dartmouth College's Research Data Services , where he supports computational and data-intensive research in the arts, humanities, and social sciences. His work bridges digital humanities, spatial history, and Indigenous studies, with a focus on recovering marginalized histories through data visualization and cartographic techniques. Interdisciplinary expertise in history, geography, and digital methods Specializes in GIS, narrative cartography, and critical data analysis Current research centers on early colonial Latin America and place-name studies His recent publications in Hispanic American Historical Review (2020) and International Journal for Arts and Humanities Computing (2017) examine how digital tools can reconstruct Indigenous agency during the Spanish conquest. He teaches courses on digital history, spatial analysis, and Indigenous narratives at Dartmouth and previously at USC. Scientific recognition includes the Andrew W. Mellon Postdoctoral Fellowship in Digital Humanities.

Dong ZhangView profile

Associate Professor

• Receiver Operating Characteristic (ROC) analysis
• Empirical likelihood and semi-parametric inference
• Clinical trial study
• +5 more

Dong Zhang is an Associate Professor in the Department of Mathematics, Computer Science, and Digital Forensics at Bloomsburg University, Pennsylvania, where he has taught since joining the institution in 2012. His academic role encompasses instruction in mathematics, statistics, and computer science disciplines. His educational qualifications include: B.S. in Mathematics (2004) from Nankai University M.Sc. in Statistics (2007) from Nankai University Ph.D. in Statistics from the University of Toledo Dr. Zhang's research program integrates theoretical and applied statistics with computational methodologies. His primary focus areas include ROC analysis for diagnostic testing, semi-parametric inference techniques, clinical trial design, handling incomplete datasets, experimental methodology, and modern data-intensive approaches including machine learning applications. His clinical trial work at the University of Toledo Medical Campus (2009-2011) demonstrates practical implementation of these statistical frameworks in medical research contexts. He maintains an active virtual research environment and teaches diverse courses spanning foundational mathematics to advanced computational statistics. His technical proficiency across multiple programming languages supports both research innovation and pedagogical effectiveness in data science education.

Joseph E. GonzalesView profile

Associate Professor

• Latent Variable Modeling
• Longitudinal Data Analysis
• Multivariate Dynamics
• +3 more

Joseph E. Gonzales serves as Chair and Associate Professor in the Department of Psychology at the University of Massachusetts Lowell, College of Fine Arts, Humanities and Social Sciences. He holds dual affiliations as an Associate of the Center for Women and Work (CWW) and Faculty Member of the Center for Health Statistics. Education: Ph.D. in Quantitative Psychology, University of California, Davis M.A. in Experimental Psychology, California State University, Stanislaus B.A. in Experimental Psychology, California State University, Stanislaus His methodological research spans latent variable modeling, longitudinal data analysis, and intensive multivariate dynamics, with specific focus on time series analysis of daily diary data, measurement model validity, unobserved subgroup identification, and innovative research design frameworks. He pioneers choose-your-own-adventure paradigms to study complex behavioral phenomena while advancing statistical methodologies for ergodicity and measurement invariance. Publication trends reveal strong integration of computational paradigms with psychological theory, particularly in developing novel methods for sensitive topics like sexual assault behavior and criminal decision-making, alongside foundational contributions to structural equation modeling and affective dynamics modeling. Honors include: University of Massachusetts Lowell Faculty Mentoring Graduate Students Award (2021) University of Massachusetts Lowell Teaching Excellence Award (2020) Summer Internship Program in Research for Graduate Students (2015) ETS-NAEP Testing, Evaluation, Assessment, & Measurement Fellow (2014) Society of Measurement and Experimental Psychology Graduate Student Poster Travel Award (2014) American Psychological Association Student Travel Award (2014) Western Psychological Foundation Student Scholarship Award (2012) Dean’s Medal of Excellence (2010) Graduate Student of the year (2009) As Co-PI on a $2.5M AHRQ R01 grant (2022-2027), he models moral distress impacts on nurses' health and safety culture. He previously led an American Psychology-Law Society grant (2021-2022) analyzing attention dynamics in virtual court hearings. His graduate student mentoring earned university recognition in 2021. Through the Center for Women and Work and Center for Health Statistics, he drives interdisciplinary collaboration on gender, work psychology, and health measurement systems.

Angela P. MurilloView profile

Associate Professor

• Data Curation
• Data Management
• Data Sharing and Reuse
• +3 more

Angela P. Murillo serves as an Associate Professor at Indiana University's Luddy School of Informatics, Computing, and Engineering in Indianapolis, where she directs the undergraduate Data Science program. She concurrently holds roles as Co-Principal Investigator and Student Fellowship Director for CI Compass, a National Science Foundation-funded initiative (Grant #2127548) supporting cyberinfrastructure advancement. Her academic foundation includes an undergraduate degree in geoscience and a Ph.D. in Information Science from the University of North Carolina at Chapel Hill. Her doctoral research examined data sharing and reuse within scientific cyberinfrastructures, establishing her trajectory in data-intensive scholarship. Murillo's research program critically examines data curation, preservation, and cyberinfrastructure through lenses of workforce development and educational innovation. She develops competency frameworks for data professionals while investigating diversity in LIS education and virtual reality applications for learning. Her work bridges theoretical data management principles with practical implementations across scientific domains, emphasizing FAIR data practices and cross-cultural educational frameworks. Analysis of her recent publications reveals concentrated exploration of data curation competencies (2023-2025), workforce dynamics in data fields, and cyberinfrastructure fellowship impacts. These works demonstrate evolving focus from foundational data sharing studies toward systemic educational frameworks and workforce development strategies, with increasing attention to equity and community-driven standards in data science. As Co-PI for the NSF-supported CI Compass project, Murillo secures critical infrastructure funding while directing student fellowships that cultivate next-generation cyberinfrastructure professionals. Her leadership in developing Indiana University's Data Science undergraduate program since 2017 demonstrates sustained commitment to building academic pathways, though specific student advisee details remain unpublicized. Murillo collaborates within the CI Compass project team and leverages industry experience from her tenure as Research and Development Information Scientist at Novozymes. Her current initiatives focus on expanding data science education through community-curated competency frameworks and addressing workforce gaps via innovative fellowship models, positioning her at the intersection of data infrastructure and educational transformation.

Anna MonrealeView profile

Associate Professor

• Data Analytics
• Data Mining
• Artificial Intelligence
• +5 more

Anna Monreale is an Associate Professor of Computer Science at the University of Pisa and serves as the Delegate for Masters and Continuing Education. She concurrently holds an Adjunct Professor position at Dalhousie University's Faculty of Computer Science. Her institutional leadership includes vice-coordinating the National Doctorate in Artificial Intelligence for Society and chairing the Internships Committee for the Master's in Data Science & Business Informatics. She earned her Bachelor's and Master's degrees with honors in Computer Science from the University of Pisa in 2007 and completed her PhD there in 2011. Prior to her faculty appointment, she conducted research on European projects from 2011-2014 and has been affiliated with the CNR's Institute of Information Science and Technologies since 2008. Professor Monreale's research centers on ethical AI development, with core expertise in data mining, explainable AI, and privacy-by-design methodologies. Her work bridges technical innovation with societal impact, particularly in healthcare applications, mobility data analysis, and social media systems. She emphasizes transparency and legal compliance in algorithmic decision-making. Her recent publications (2023-2025) reveal a concentrated focus on trustworthy AI frameworks, with significant contributions to federated learning privacy, trajectory data protection, and real-time explainability systems. She actively addresses emerging challenges in content moderation regulation and healthcare AI deployment. As an educator, she teaches Data Mining and Data Science Lab courses while directing major academic initiatives. She leads a PNRR-funded national project and a European research project, with over 90 publications and 3,200+ citations reflecting her scholarly impact. She is a key member of the University of Pisa's KDD Lab and maintains strong ties with CNR research units.

• Machine Learning
• Knowledge Discovery
• Data Mining
• +6 more

Manuel Filipe Vieira Torres Santos is an Associate Professor with Habilitation at the School of Engineering of the University of Minho, Portugal. He serves as a Senior Researcher at Centro ALGORITMI (Associate Laboratory in Intelligent Systems) where he coordinates the Intelligent Data Systems research lab and the Information Systems and Technologies research group. His academic career combines teaching responsibilities with extensive research activities focused on data science applications in healthcare settings. Dr. Santos' research interests center on applying advanced computational techniques to healthcare challenges. His primary areas include Machine Learning, Knowledge Discovery from Databases, Data Mining, Agent-based Systems, and Intelligent Decision Support Systems. He has pioneered work in Pervasive and Adaptive Business Intelligence specifically tailored for healthcare environments. His research spans both theoretical advancements in data science methodologies and practical implementations in real-world medical settings, particularly in intensive care units, hospital management, and precision medicine applications. Analysis of his recent publications reveals a strong trend toward integrating emerging technologies like blockchain, Internet of Things, and openEHR standards into healthcare information systems. His work demonstrates a progression from foundational data mining techniques to increasingly sophisticated architectures that support real-time decision making, predictive analytics, and personalized patient care. The publications show particular emphasis on standardization efforts, interoperability challenges, and the development of maturity models for digital transformation in healthcare institutions. Coordinator of Intelligent Data Systems research lab Coordinator of Information Systems and Technologies research group Principal Investigator for multiple funded projects including: Intelligent hospitalization management (2021-present) Data Science applied to diabetes (2021-present) Intelligent Decision Support for response times optimization (2020-present) Intelligent Hospital Infection Control (2020-present) Dr. Santos has secured significant research funding for healthcare analytics projects, demonstrating the practical value of his work. His approach combines technical expertise in data science with deep understanding of healthcare workflows and challenges, resulting in solutions that address real clinical needs while advancing the state of the art in health informatics.

• Information Systems
• Business Intelligence
• Data Mining
• +4 more

Maria Isabel Calapez Cabrita Leal Seruca serves as an Associate Professor at Portucalense University's Science and Technology Department in Porto, Portugal, where she teaches Information Systems and coordinates the Informatics Engineering and Information Systems for Management programs. She additionally holds the role of Department Coordinator for the Erasmus+ Programme. As a Senior Researcher at Universidade do Minho's Centro Algoritmi, she contributes to the IST R&D Group and ISTTOS R&D Lab, bridging academic and research domains across institutions. Her academic foundation includes a degree in Applied Mathematics and Computation from Portucalense University, complemented by an MSc and PhD in Computation from the University of Manchester Institute of Science and Technology (UMIST). This technical background underpins her research trajectory in digital systems and organizational transformation. Her research focuses on Information Systems with specialized expertise in Business Intelligence, Data and Web Mining, Digital Transformation, and Reuse and Patterns. She has pioneered models like E-swim (Enterprise Semantic Web Implementation) and ToOW (Training of Organizations in the Web) to address organizational challenges through technology adoption. Her work consistently integrates practical applications in educational settings and industry contexts, particularly through international Erasmus collaborations. Analysis of her 2019-2025 publications reveals a dominant emphasis on Digital Transformation in educational mobility systems (notably Erasmus+ projects), alongside applied Data Mining in domains including tourism, pharmaceutical distribution, and waste management. Her scholarship demonstrates a trajectory from foundational semantic web research toward contemporary organizational digitalization challenges, with increasing focus on sustainable development frameworks. While no major individual scientific awards are documented in the source material, her research output includes 41 publications with an h-index of 7 and 164 citations, reflecting steady scholarly impact in Information Systems journals and conferences. She actively leads Erasmus+ initiatives including the KA220 project 'Data Science We Teach Data' and has coordinated multiple Erasmus Intensive Programmes on Sustainable Digital Transformation (2022-2023), Business Intelligence (2008-2010), Web Mining (2011-2013), and Data Science (2014). Her grant work consistently targets curriculum development for digital competencies in higher education. Within research infrastructure, she contributes to the Digital Transformation and Innovation in Organisations (DTIO) Group at REMIT (Portucalense University) and the ISTTOS Lab at Centro Algoritmi (Universidade do Minho), focusing on organizational agility through technology adoption and pattern-based solutions.

• Sports Science
• Exercise Physiology
• Environmental Physiology
• +2 more

Takashi Ichinose is a Professor at Waseda University's Center for Research Strategy and Faculty of Sport Sciences. He has held progressive roles at Waseda since 2004, including his current position in the Center for Research Strategy since 2008, where he manages research strategy while maintaining his academic focus on sports science. His educational background includes: Doctor of Philosophy in Medical Science from Shinshu University Graduate studies in Life Sciences from Waseda University's Graduate School of Human Sciences Undergraduate degree in Sport Sciences from Waseda University's School of Human Sciences Professor Ichinose's research spans exercise physiology, environmental physiology, and sports science, with particular focus on substrate metabolism during exercise, thermoregulation, and plasma volume changes. He has made significant contributions to understanding how nutritional interventions like tea catechins affect fat metabolism. He is also a leading figure in Tee-ball development, a modified baseball sport officially adopted into Japan's elementary school curriculum. His publication record shows an evolution from fundamental exercise physiology research to broader applications in sports education and inclusion. Early work focused on metabolic responses to exercise, while more recent publications address sports education, disability inclusion, and international sports initiatives like the Olympics. Professor Ichinose has received research funding from the Japan Society for the Promotion of Science and collaborated with industry partners including Kao Corporation. His presentations at numerous research meetings demonstrate his active role in promoting research funding opportunities at Waseda University. He has been deeply involved with the Japanese Tee-ball Association, contributing to Tee-ball's adoption in Chinese and Korean educational systems. His work bridges academic research with practical applications in sports education and physical activity promotion across Asia.

Deming KongView profile

Researcher

• Optical Communications
• Photonics
• Signal Processing
• +2 more

Deming Kong is a Researcher at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), affiliated with the Photonic Integrated Circuit based Systems Centre of Excellence for Silicon Photonics for Optical Communications. His research spans optical communications, silicon photonics, and optical signal processing, with expertise in photonic integrated circuits for optical neural networks and wireless communication systems. Key focus areas include optical matrix multiplication processors, digital precoding for multi-mode fiber transmission, nonlinear channel equalization using silicon microring modulators, and ultrafast terahertz wireless communication leveraging Kerr frequency combs. Recent publications (2024-2025) demonstrate significant contributions to optical computing hardware for neural networks and advanced optical communication techniques, integrating silicon photonics with machine learning and communication theory to address challenges in high-speed data transmission and neural network acceleration. Dr. Kong actively supervises PhD research at DTU, including: Digital Optical Computing Platform for Neutral Networks (2024-2027) Silicon Photonics for Optical Neural Networks (2024-2027) Silicon Photonic Integrated Circuits for Optical Processing aided Artificial Intelligence (2019-2023) These projects focus on optical neural networks and silicon photonics for AI applications. He operates within DTU's Photonic Integrated Circuit based Systems Centre of Excellence, a leading hub for silicon photonics research targeting optical communications and neural network hardware acceleration.

Liz Ritchie-TyoView profile

Professor

• Tropical Cyclones
• Tropical Meteorology
• Extreme Weather
• +12 more

Liz Ritchie-Tyo is a Professor in the School of Earth, Atmosphere & Environment and the Department of Civil Engineering at Monash University, where she conducts cutting-edge research on tropical cyclones, extreme weather, and climate impacts. She is actively involved in interdisciplinary research, supervision of PhD students, and leadership in national and international scientific organizations. Education: PhD in Atmospheric Science, Monash University (awarded 1 October 1995), focusing on mesoscale aspects of tropical cyclone genesis. Her research spans both fundamental and applied domains, including the physical understanding of tropical cyclogenesis, intensity change, extratropical transition, and landfall impacts. She also investigates the remote sensing of tropical cyclones and their societal and environmental consequences. Her recent publications reflect strong trends in integrating satellite remote sensing, climate modeling, and public health outcomes, particularly in assessing post-cyclone disease risks and radiative effects of storm clouds. This interdisciplinary approach underscores her contributions to climate science and disaster resilience. Scientific Awards and Honors: Fellow of the American Meteorological Society Prof. Ritchie-Tyo has secured significant research funding from prestigious bodies including the Australian Research Council (ARC), the US Office of Naval Research, the National Science Foundation, NOAA, NASA, and the Australian Bureau of Meteorology. She currently leads multiple projects, such as the ARC Centre of Excellence for the Weather of the 21st Century and a project on the contribution of tropical cyclones to Earth's energy budget. She has supervised numerous students and contributes extensively through editorial roles, including as editor of Weather and Forecasting and former editor of Monthly Weather Review . Research Labs and Teams: She is a key member of collaborative research teams working on tropical cyclone dynamics and climate impacts, including international networks under the World Meteorological Organization (WMO) and partnerships with institutions in the US and Australia. Her work is highly collaborative, involving experts in meteorology, oceanography, public health, and remote sensing.

Saptarshi DebroyView profile

Associate Professor

• Distributed Systems
• Cloud Computing
• Edge Computing
• +6 more

Dr. Saptarshi Debroy is an Associate Professor of Computer Science at Hunter College, City University of New York (CUNY), and a member of the Doctoral Faculties at The Graduate Center, CUNY. He founded and directs the Distributed Systems and Security (DiSSect) Laboratory and is affiliated with the Computer Simulation, Stochastic Modeling and Optimization (CoSSMO) Laboratory. Previously, he served as Assistant Professor at CUNY (2016-2024) and Postdoctoral Fellow at the University of Missouri (2014-2016). Education: Ph.D. in Computer Engineering, University of Central Florida (2014) Master of Technology, Jadavpur University, India (2008) Bachelor of Technology, Institute of Engineering and Management, West Bengal University of Technology (2006) Research focuses on distributed systems innovation with emphasis on: Distributed Computing & Data Science : Resilient management of distributed systems, AI/ML for resource provisioning, IoT/CPS applications Usable Cyber Security : End-to-end security assurance, trustworthy edge/cloud computing, SDN vulnerability analysis Core methodologies include software-defined networking, deep learning algorithms, and federated system architectures for disaster response and scientific workflows. Publication analysis reveals strong emphasis on edge computing optimizations (67% of recent works), cybersecurity innovations (22%), and AI/ML integration (11%). Dominant trends include latency-quality tradeoffs in real-time systems, DNN inference optimization, and moving target defense strategies, with applications spanning disaster response, healthcare, and scientific computing. Scientific Awards & Honors: NSF CAREER Award 2020 (Cyber-Physical Systems) Best Paper Award, IEEE PIMRC 2011 Jadavpur University Gold Medal 2008 National Scholarship Merit Certificate (2000, 2002) 3 NSF Travel Grants, 2 IEEE Comsoc Travel Grants Advises 8 PhD students in cloud/edge computing and cybersecurity. Secured $2.37M+ in research funding as PI, including: NSF CAREER: Resilient Fog Framework for Disaster Response ($523K) NSF CICI: Trusted Volunteer Edge-Cloud Workflows ($600K) Google Research: DNN Model Extraction Threats ($100K) NSF: Health Incident-Response Framework ($612K) Leads the DiSSect Lab focusing on distributed systems security and co-directs the CoSSMO Lab. Research collaborations include FDNY for disaster response systems and Lawrence Berkeley National Lab through Sustainable Research Pathways.

Simona TurcoView profile

Assistant Professor

• Medical Imaging
• Biomedical Signal Processing
• Angiogenesis
• +4 more

Simona Turco is an Assistant Professor at the Electrical Engineering department at Eindhoven University of Technology. She is affiliated with the Biomedical Diagnostics (BM/d) Lab and the Eindhoven MedTech Innovation Center (e/MTIC). Her research focuses on model-driven analysis of medical images and biosignals for oncology and perioperative care. MSc Biomedical Engineering, University of Pisa (2012) Professional Doctorate in Engineering (PDEng), TU/e (2015) PhD, TU/e (2018) Her research integrates pharmacokinetic modeling and machine learning to advance diagnostics in cancer (angiogenesis imaging) and perioperative risk prediction. She utilizes contrast-enhanced ultrasound and MRI for multi-scale functional and molecular analysis. Key subfields include microbubble dynamics, dispersion imaging, and biosignal modeling. Recent publications highlight applications in prostate cancer radiogenomics, renal cell carcinoma, adenomyosis, and patient-ventilator asynchrony. She combines mathematical modeling with data-driven approaches for clinical translation, supported by collaborations with healthcare partners. Scientific Awards: Academy Van Leersum Grant (2019) Best Paper (3rd place), IEEE-EMBS (2017) Best Poster Awards at European Symposium on Ultrasound Contrast Imaging (2019) and IEEE-EMBS Benelux (2017) Hanart Fonds Fellowship (2019) She contributes to projects like MEDICAID (cardiovascular diagnostics) and PISANO SPS (perioperative innovation), with over 86 research outputs and 5 datasets. Her work aligns with the UN Sustainable Development Goals for healthcare innovation.

• Additive Manufacturing
• Composite Materials
• Fatigue Behavior
• +5 more

Andrea Tridello is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the Polytechnic University of Turin, Italy, and a member of the Interdepartmental Center J-Tech@PoliTO. His academic work focuses on mechanical design, fatigue analysis, and additive manufacturing technologies with significant contributions to Very High Cycle Fatigue (VHCF) research. Dr. Tridello's research spans multiple areas including Additive Manufacturing , Composite Materials , Fatigue Behavior , and Machine Learning applications for predicting material responses. His work addresses critical challenges in non-destructive testing methodologies for damaged composites, fatigue analysis of additively manufactured components, and the mechanical behavior of lattice structures. He has developed machine learning algorithms specifically for predicting mechanical behavior of structural materials. His recent publications demonstrate a strong focus on the intersection of additive manufacturing and fatigue analysis, with particular attention to defect characterization, statistical modeling of fatigue data, and development of predictive algorithms. His research also extends to composite materials, exploring impact damage assessment and non-destructive evaluation techniques across aerospace and automotive applications. Young Researcher Award from VHCF8 conference (2021) Steering Committee member of European Structural Integrity Society (ESIS) (2024-) Steering Committee member of Italian Fracture Group (2023-) Editorial Board member of FRACTURE AND STRUCTURAL INTEGRITY (2023-) Editor-in-Chief of MATERIAL DESIGN & PROCESSING COMMUNICATIONS (2023-) Dr. Tridello actively supervises PhD students Alessio Centola (39th cycle) and Rujun Li (38th cycle). He leads and participates in significant research projects including ML4FatigueDesign (statistical software for fatigue data analysis), FAAST (Fatigue on Additive Alloys Speedy Technique), and PLEIADES (advancing aerospace composites). His work has practical applications across aerospace, automotive, and industrial sectors. He is a key member of the Mechanics of Materials and Joints research group within DIMEAS, focusing on fatigue, impact, and testing methodologies for advanced materials and structures, contributing to both theoretical frameworks and practical engineering solutions.

Patrik VagovicView profile

Researcher

• X-ray Imaging
• Phase Retrieval
• Coherent Diffractive Imaging
• +4 more

Patrik Vagovic is a Staff Scientist at the European XFEL GmbH, affiliated with the Center for Free-Electron Laser Science (CFEL), a collaborative research center between DESY, the University of Hamburg, and the Max Planck Society. He leads research in the Coherent Imaging Team, focusing on advanced X-ray imaging techniques using X-ray free-electron lasers. His work bridges the gap between fundamental physics and practical applications in materials science, biology, and fluid dynamics. Dr. Vagovic's research interests center around developing and applying cutting-edge X-ray imaging methodologies, particularly high-speed and phase-sensitive techniques. His work encompasses X-ray phase contrast imaging, coherent diffractive imaging, tomography, and advanced data processing methods. He has pioneered MHz frame rate X-ray imaging capabilities at the European XFEL, enabling unprecedented observation of ultrafast phenomena previously impossible to capture with conventional X-ray sources. Analyzing his recent publication record reveals a strong focus on pushing the temporal and spatial boundaries of X-ray imaging. His work demonstrates a consistent progression from developing fundamental imaging techniques to applying them to complex scientific problems across multiple disciplines. The research shows increasing sophistication in both hardware development (optical systems, detectors) and computational methods (phase retrieval, machine learning). Dr. Vagovic actively collaborates with international research teams across Europe and beyond, contributing to numerous high-impact publications in top journals including Optics Express, Journal of Synchrotron Radiation, and Nature Communications. His work on MHz X-ray microscopy has particularly advanced the field of time-resolved imaging of irreversible phenomena. As part of the Coherent Imaging Team at European XFEL, Dr. Vagovic works with state-of-the-art instrumentation including the SPB/SFX instrument, where he has developed pump-probe capabilities and advanced diagnostics for megahertz pulse trains. His research group utilizes advanced computational approaches alongside experimental innovations to solve complex imaging challenges.