جست‌وجوی استاد و پژوهشگر

Wei Jieمشاهده پروفایل

استاد

• Distributed Computing
• Data Analytics
• Computing Security
• +۷ مورد دیگر

Professor Wei Jie is a faculty member in the School of Computing and Engineering at the University of West London. His research focuses on distributed computing, data analytics, and computing security, with significant contributions to cloud and grid computing, big data, and e-Science. He has secured substantial research funding from organizations such as the Royal Society, Innovate UK, JISC, and AWS. PhD in Computer Science (exact institution and year not specified) Extensive research experience in grid and cloud computing infrastructure Professor Jie's research interests center on distributed systems, cybersecurity, and data-intensive computing. He has published extensively in top-tier journals and conferences, with a strong emphasis on practical applications in cloud environments, remote sensing, and intelligent monitoring systems. His work integrates parallel computing, swarm intelligence, and real-time analytics to solve complex computing challenges. The recent publications of Professor Jie reflect a consistent focus on distributed computing, data analytics, and security. His work spans cloud infrastructure optimization, trajectory data compression, remote sensing data management, and secure computing environments. Key themes include scalability, real-time processing, and intelligent system design, with applications in environmental monitoring, healthcare, and network security. Successfully attracted research funding from the Royal Society, Innovate UK, JISC, and AWS Professor Jie has supervised numerous research projects and contributed to major collaborative efforts in grid and cloud computing. He has led the development of secure information services, resource management frameworks, and large-scale data processing platforms. His research has been supported by significant grants and has resulted in impactful publications and systems. Professor Jie has been instrumental in developing academic programs at UWL, including the BSc Cyber Security and leading the MSc Cyber Security course. He teaches across various computing disciplines, including data science, distributed systems, and cybersecurity, contributing to undergraduate and postgraduate education.

Angelos Bilasمشاهده پروفایل

استاد

• Computer Systems
• Computer Architecture
• Storage Systems
• +۲ مورد دیگر

Angelos Bilas is a Professor in the Department of Computer Science at the University of Crete and a collaborating researcher at FORTH-ICS. He holds a B.Eng. from the University of Patras (1993), and M.A. and Ph.D. from Princeton University (1995, 1998). His research focuses on computer systems, storage systems, and computer architecture, with recent emphasis on optimizing memory management and storage efficiency. Bilas has held roles such as Chair of the Department of Computer Science (2016–2020) and coordinated the FP7 EU project IOLanes (2010–2013). He serves on editorial boards, including ACM Transactions on Storage since 2016. His work has been recognized with awards like the Marie Curie Excellent Teams Award (2005–2009) and patents in storage and networking. Current affiliations: University of Crete (Professor), FORTH-ICS (Researcher) Educations: Ph.D. (Princeton, 1998), M.A. (Princeton, 1995), B.Eng. (Patras, 1993) His research interests span storage systems, parallel architectures, and runtime systems. Recent work includes TeraHeap (ASPLOS'23) for big data frameworks and Tebis (EuroSys'22) for LSM key-value stores. His publications emphasize optimization in memory-mapped I/O, storage efficiency, and heterogeneous acceleration. Awards include the Marie Curie Excellent Teams Award and four patents. He has supervised 35+ master's and 8+ Ph.D. students, contributing to over 45 EU/nationally funded projects. Current initiatives include the EVOLVE H2020 project (2019–2021) for HPC and big data integration. Labs/Teams: Active in FORTH-ICS and collaborations with industry on storage and cloud-HPC convergence.

Luise Pufahlمشاهده پروفایل

استاد

• Business Process Management
• Process Mining
• Operations Research
• +۶ مورد دیگر

Prof. Dr. Luise Pufahl is a Professor of Information Systems at the Technical University of Munich (TUM), leading the Information Systems research group at the Heilbronn Campus since 2023. Previously, she served as a visiting professor at the Software and Business Engineering research group in 2022 after completing postdoctoral work at TU Berlin. Her research focuses on business process management, process mining, and sustainability integration in business processes, particularly in healthcare and logistics. She holds a PhD from the Hasso Plattner Institute (University of Potsdam) and has conducted research in the Netherlands and Austria. Her academic career includes studies at the Berlin School of Economics and Law and Humboldt University of Berlin. She specializes in applying operations research, simulation, and machine learning to optimize resource-intensive processes. Notable awards include the Best Demo Award at BPM 2021 and the Runner-up Dissertation Award at BPM 2018. Her work emphasizes regulatory compliance, sustainability, and the practical application of process mining in healthcare systems. Education : PhD: Hasso Plattner Institute, University of Potsdam MSc: Berlin School of Economics and Law & Humboldt University of Berlin Awards : Best Demo Award (BPM 2021) Runner-up Dissertation Award (BPM 2018) Best Paper Award (International Conference on Business Information Systems 2016) Her research addresses challenges in process automation, sustainability-oriented frameworks (e.g., SOPA), and healthcare process optimization. Current projects include SimuBridge for environmental impact simulation and AB testing with reinforcement learning for process improvements.

• Neonatology
• Bronchopulmonary Dysplasia
• Neonatal Lung Injury
• +۷ مورد دیگر

Samuel Gentle, MD, is an Assistant Professor of Neonatology in the Department of Pediatrics at Yale School of Medicine. He is an attending neonatologist with a strong translational research focus on bronchopulmonary dysplasia (BPD) and its complications in preterm infants. Assistant Professor, Department of Pediatrics, Yale School of Medicine Member, Janeway Society Attending Neonatologist, Yale New Haven Children's Hospital Education: MD, University of Alabama at Birmingham, 2012 BS, Biology and Psychology, Loyola University New Orleans, 2006 Residency, Icahn School of Medicine at Mount Sinai, 2015 Fellowship, Neonatal-Perinatal Medicine, University of Alabama at Birmingham, 2018 Dr. Gentle's research centers on identifying mechanisms of BPD development and protective strategies to improve pulmonary outcomes in preterm infants. His work explores the role of nitric oxide bioavailability, including airway nitrite and oral nitrate reductase, as potential biomarkers and therapeutic targets. He also investigates the use of continuous biomedical signals—particularly oxygen saturation data—to detect and predict bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH), leveraging scalable NICU monitoring systems for early intervention. His recent publications reflect a strong trend in both clinical neonatology and translational data science, with studies on permissive hypercapnia, teleneonatal resuscitation, and feeding dynamics in preterm infants. These works span randomized trials, quality improvement initiatives, and computational analyses, demonstrating a multidisciplinary approach to neonatal care. Scientific Awards: Scholar Award, AAP/Vermont Oxford Network (2021) Young Investigator Award, SSPR (2024) Dr. Gentle is actively involved in clinical research mentorship and has contributed to team science initiatives at Yale, particularly through the Janeway Society and collaborative projects with the Yale Center for Clinical Investigation (YCCI). He has not reported any major grants in the provided text but is engaged in NIH- and foundation-supported research environments. His work emphasizes innovation in neonatal monitoring, predictive modeling, and improving care delivery for vulnerable preterm populations. He is a member of the Neonatal-Perinatal Medicine division within Pediatrics and collaborates closely with multidisciplinary teams focusing on lung injury, critical care, and neonatal outcomes. His lab or research group integrates clinical data with mechanistic studies to bridge bench-to-bedside applications in neonatology.

• Data-Centric Systems
• Distributed Systems
• Big Data Processing
• +۳ مورد دیگر

Luo Mai is a Lecturer in Data Centric Systems at the School of Informatics, The University of Edinburgh. They are affiliated with the Institute for Computing Systems Architecture, contributing to research in large-scale and data-intensive computing systems. Their research focuses on data-centric systems, encompassing areas such as distributed computing, big data processing, cloud infrastructure, and scalable database technologies. This work supports modern applications requiring high-performance and efficient data management. Luo Mai has no listed publications or scientific awards in the provided content. There is no information available regarding student supervision or research grants. Their work is associated with the Institute for Computing Systems Architecture, a research institute within the School of Informatics focusing on advanced computing architectures and systems software.

• High Performance Computing
• Parallel I/O Optimization
• Data Compression
• +۴ مورد دیگر

Dr. Julian Kunkel is a Researcher at the University of Reading specializing in High Performance Computing (HPC) with 44 publications spanning 2014-2024. His work focuses on parallel I/O optimization, data compression, and machine learning applications for scientific computing and financial systems. Research interests include: High Performance Computing infrastructure optimization Parallel I/O monitoring and prediction using machine learning Data compression techniques for climate and scientific datasets Energy efficiency in data centers and HPC systems Financial time series forecasting with deep learning Tropical cyclone detection through satellite imagery analysis Recent publications (2020-2024) demonstrate a strong trend toward integrating machine learning with HPC challenges, particularly in I/O optimization and cross-domain applications like financial forecasting and meteorology. His work on the HPC Certification Forum shows leadership in standardizing HPC education globally. Dr. Kunkel collaborates internationally with researchers from Germany, Japan, and the USA on projects like AIMES (Advanced Computation and I/O Methods for Earth-System Simulations). His development of tools like SIOX for I/O monitoring and TrioFlow for financial prediction highlights practical applications of his research.

• High-Performance Computing
• Scientific Workflows
• Distributed Computing
• +۱۴ مورد دیگر

Justin M. Wozniak is a computational scientist at Argonne National Laboratory’s Mathematics and Computer Science Division within the Computing, Environment and Life Sciences directorate. He is a key contributor to advanced scientific workflow systems such as Swift/T and Parsl, enabling scalable, distributed, and many-task computing for data-intensive science. His work supports major initiatives in cancer research (CANDLE), epidemiological modeling, and exascale computing (ExaWorks). He collaborates extensively with leading researchers including Ian T. Foster, Michael Wilde, and Kyle Chard. His research focuses on high-performance computing, scientific workflows, distributed systems, and machine learning applications in science. He has pioneered techniques in workflow automation, fault tolerance, in-situ data analysis, and performance optimization. His work enables robust, scalable execution of complex computational pipelines across heterogeneous environments, from supercomputers to cloud platforms. His recent publications (2021–2025) emphasize workflow interoperability, resilience, benchmarking, and applications in cancer and epidemic modeling. Themes include automated model comparison, job management portability (PSI/J), adaptive workflow steering, and exascale-ready workflow toolkits. These works reflect a strong trend toward reproducibility, scalability, and real-world scientific impact. Justin M. Wozniak has no listed scientific awards in the provided text. However, his leadership in major DOE-funded projects and high-impact publications in top venues (SC, HPDC, e-Science) underscores his significant contributions to computational science. He has mentored or collaborated with numerous researchers, though specific advisees are not listed. His work is supported by large-scale computing grants and initiatives such as the ExaWorks project and CANDLE, which aim to accelerate scientific discovery through advanced computing infrastructure. He contributes to open science through tools like Parsl and Swift/T, which are widely used in the scientific community. He is a core developer in the ExaWorks ecosystem and contributes to workflow frameworks that integrate with AI/ML pipelines, containerization, and real-time data analysis. His work on Braid-DB and provenance tracking supports AI-driven science with full reproducibility. These efforts are central to modern computational laboratories aiming for autonomous, data-intensive discovery.

• AI in Healthcare
• Digital Health Strategy
• Computational Statistics
• +۸ مورد دیگر

Dr. Watjana 'Waty' Lilaonitkul is a Lecturer (Assistant Professor) in Digital Health Technologies at University College London's Global Business School for Health , with over 15 years of experience spanning academia, industry, and international development. She holds a PhD in Electrical Engineering from the Massachusetts Institute of Technology (MIT) and serves as Deputy Director of Alumni and Student Experiences and Research EDI (Equality, Diversity, and Inclusion) Lead at UCL. Education: PhD in Electrical Engineering (MIT) Academic Affiliation: University College London Research Areas include: AI-driven healthcare innovation with focus on rare diseases and pandemic preparedness Dynamic time risk prediction for personalized treatment outcomes Complex systems and network physiology for digital biomarker discovery Human-in-the-loop AI for clinical decision support Active learning optimization for rare disease contexts Open-source AI platforms for drug development (PKPDAI.com) Article Trends reveal expertise in applying machine learning to pharmacokinetic data extraction, retinal diagnostics, post-COVID conditions, sepsis monitoring, and multiorgan imaging. Her work emphasizes data scarcity solutions and responsible AI deployment in clinical settings. Scientific Awards : UKRI Rutherford Fellowship (2018-2023) Grant & Advisory Roles : 2024: Appointed to Medical Research Council's Better Methods Better Research (BMBR) panel 2023: Expert reviewer for EPSRC AI Innovation funding and UKRI AI Centre for Doctoral Training 2021: Reviewer for Medical Research Council's COVID-19 Big Data Secondment Call Leadership includes developing three international patents (UK/EU/US) and co-directing the MSc Digital Health and Entrepreneurship program's flagship module GBSH0044, which trains students to build scalable AI healthcare solutions through startup-style team projects.

Valeriia Haberlandمشاهده پروفایل

مدرس

• Cancer Genetics
• AI in Healthcare
• Epidemiology
• +۳ مورد دیگر

Dr Valeriia Haberland is a Lecturer in Computer Science at Brunel University London within the College of Engineering, Design and Physical Sciences, appointed in 2023. Her expertise bridges computing and biological sciences with a focus on translational health applications. She holds a PhD in Computer Science from King's College London (2015) and was awarded Fellow of the Higher Education Academy (FHEA) in October 2023. Educational background includes advanced training in computational methods and data science. Her research centers on cancer genetics and AI-driven healthcare solutions , specializing in modeling cancer aggressiveness through machine learning analysis of molecular profiles. She pioneers epidemiological causal inference techniques to identify genetically translatable disease factors and develops specialized web infrastructure for biomedical research. Current projects target breast cancer wound monitoring and laminopathy mechanisms. Publication analysis reveals a distinct evolution from early grid computing research (2012-2017) to contemporary health data science leadership. Recent work dominates cancer genomics, proteomics, and osteoarthritis therapeutics using UK Biobank data, with flagship contributions to EpiGraphDB and MR-Base platforms. Her methodology integrates Mendelian randomization, graph databases, and high-throughput sequencing analytics. Dr Haberland secures active research funding including a Brunel Partners-funded project on remote breast cancer wound monitoring (May-July 2024). She supervises MSc dissertations while teaching Modern Data, Logic and Computation, and final-year projects. Her academic service includes placement tutoring and curriculum development in data-intensive healthcare applications.

Feng Xieمشاهده پروفایل

استادیار

• Machine Learning
• Clinical Informatics
• Electronic Health Record Analysis
• +۵ مورد دیگر

Feng Xie is an Assistant Professor at the University of Minnesota , affiliated with the Division of Computational Health Sciences in the Department of Surgery and the Graduate Faculty of Data Science in the College of Science and Engineering . With a joint PhD from Duke University and National University of Singapore, his academic journey includes postdoctoral work at Stanford University School of Medicine under Dr. Nima Aghaeepour. Education PhD (Integrated Biology and Medicine), Duke-NUS Medical School (2022) BS (Biomedical Engineering), Tsinghua University (2017) His research combines medical informatics , machine learning , and biostatistics to develop trustworthy AI/ML solutions for healthcare challenges. Key focus areas include electronic health records (EHR) modeling , clinical decision support systems , and multimodal data integration across domains like emergency medicine , critical care , and maternal-child health . Recent research trends emphasize interpretable AI through publications like AutoScore frameworks and Grade for Interpretable Field Triage models. His work on NeonatalBERT and PregMedNet demonstrates expertise in domain-specific language models and maternal medication impact analysis . The lab provides exclusive access to millions of patient records and prioritizes high-impact publications in journals like JAMA Network and The Lancet Digital Health . Infrastructure includes established data pipelines and robust computing resources for scalable health data analysis.

Matteo Migliavaccaمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Artificial Intelligence
• Data Science
• +۵ مورد دیگر

Matteo Migliavacca is a Senior Lecturer at the School of Computing, University of Kent, where he also serves as the Year in Computing Admissions Officer. His office is located in Room 118 of the Kennedy Building at the Canterbury campus. His primary research focuses on networked systems with specialization in building and evaluating large-scale distributed systems. His research interests span parallel data processing, stream processing, and publish-subscribe middleware in cloud environments. Recent work emphasizes security aspects including secure event processing, runtime taint tracking, and information flow control across multiple programming languages. He is affiliated with the Artificial Intelligence and Data Science Group and the Cyber Security Group at Kent. Publication analysis reveals consistent focus on distributed systems, cloud security, and high-performance computing. Recent trends show increased work in genetic algorithms for distributed data analysis and behavioral user modeling. Earlier foundational work established contributions in middleware security, event processing architectures, and cloud network services. He maintains active collaborations with Imperial College London's LSDS group on the SEEP (Stateful Big Data Processing) and NaaS (Network as a Service) projects, the latter in partnership with Microsoft Research.

Monica Vitaliمشاهده پروفایل

استادیار

• Information Systems
• Energy Efficiency
• Data Centers
• +۵ مورد دیگر

Monica Vitali is an Assistant Professor at the Politecnico di Milano and a Visiting Researcher at Umeå University , Sweden. Her work focuses on Information Systems , Energy Efficiency , and Machine Learning in cloud and data center environments. Her research explores self-adaptive systems , resource management , and distributed monitoring to optimize cloud efficiency. She has developed methodologies using Bayesian networks for analyzing VM behavior and improving scalability. Articles by Monica highlight trends in VM profiling , periodicity detection , and energy-efficient cloud architectures . Her work demonstrates a shift from centralized to distributed data analysis for exponential scalability improvements.

Damla Oğuzمشاهده پروفایل

استادیار

• Data Mining
• Database Systems
• Query Optimization
• +۶ مورد دیگر

Damla Oğuz is an Assistant Professor and Deputy Head of Department in the Department of Computer Engineering at Izmir Institute of Technology. She has held this position since December 2019, following her appointment as an Assistant Professor in the Department of Software Engineering at Yaşar University from 2017 to 2019. Her educational background includes: B.Sc. in Software Engineering (2008) and Industrial Systems Engineering (2009) from Izmir University of Economics M.Sc. in Computer Engineering (2012) from Izmir Institute of Technology Ph.D. in Computer Science (2017) from Paul Sabatier University, France (completed as part of a cotutelle program with Ege University) Damla Oğuz's research focuses on data mining and query optimization in distributed databases . Her work spans multiple areas including high-utility itemset mining, federated query processing, knowledge base evaluation, and the application of large language models in education. She has made significant contributions to understanding how to efficiently process queries across distributed data sources, particularly in the context of semantic web technologies and linked data. Her research often addresses the challenges of scalability, privacy, and performance in large-scale data management systems. Her recent publications demonstrate a clear trend toward addressing contemporary challenges in data management and analysis. Starting with foundational work in itemset mining algorithms, her research has evolved to tackle more complex problems in federated query processing, knowledge base evaluation, and the integration of large language models into educational contexts. The breadth of her work spans theoretical algorithm development, practical system implementation, and educational applications. Her teaching portfolio includes: CENG 212 - Concept of Programming Languages SEDS 502 - Modern Database Management Systems SEDS 535 - Information Extraction CENG 544/SEDS 542 - Large Scale Data Management CENG 465 - Principles of Data-Intensive Systems Her professional activities include: Deputy Head of Department at Izmir Institute of Technology Active research in data management and mining Teaching undergraduate and graduate courses in computer engineering

• Magnetohydrodynamics
• Dynamo theory
• Solar magnetic activity
• +۱۲ مورد دیگر

Matthias Rheinhardt is a Research Fellow at Aalto University, Finland, specializing in computational astrophysics with expertise in Magnetohydrodynamics, Dynamo theory, and Solar magnetic activity. His research leverages high-performance computing for advanced numerical simulations of astrophysical phenomena. His core research focuses on: Magnetohydrodynamics and Dynamo processes in solar/stellar contexts Numerical modeling of hydrodynamic and magnetic phenomena Exascale computing techniques for astrophysical simulations Scientific software development (notably the Pencil Code) GPU-accelerated simulation methodologies Recent publications (2021-2025) reveal a concentrated effort on dynamo mechanisms in solar-like stars, combining global convection simulations with innovative data analysis approaches. His work bridges computational physics and astrophysics, emphasizing both small-scale and large-scale magnetic field generation processes through advanced simulation frameworks. Rheinhardt actively collaborates within Professor Maarit Korpi-Lagg's research group at Aalto University, contributing to cutting-edge projects in computational astrophysics and high-performance computing infrastructure development.

Florentin Guthمشاهده پروفایل

مدرس

• Data Science
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Florentin Guth serves as an Instructor at the Center for Data Science (CDS), indicating a dedicated teaching role within an academic unit focused on data-intensive methodologies. While the specific university affiliation remains unspecified in the provided text, his position situates him within the interdisciplinary framework of modern data science education and application. His research and pedagogical focus spans core data science domains including Machine Learning and Artificial Intelligence , with emphasis on algorithmic development and computational statistics. These interests align with the CDS's mission to advance data-driven scientific inquiry across academic and industry contexts, reflecting contemporary trends in scalable data analysis and predictive modeling. No scientific awards, student advisement records, research grants, or laboratory affiliations were documented in the available information.