جستجوی استاد راهنمای دانشگاهی

• Data Science
• Mobility Data Management and Mining
• Knowledge Discovery in Data (KDD)
• +۴ مورد دیگر

Professor Nikolaos Pelekis is a faculty member at the University of Piraeus, Greece, holding the position of Professor in the Department of Statistics and Insurance Science within the School of Finance and Statistics. His academic career focuses on Data Science, with a specialized emphasis on Mobility Data Management and Mining, a field he has contributed to for over two decades. He has authored two monographs and over 100 peer-reviewed articles, earning five Best Paper Awards and over 5,000 citations. Research Interests: Mobility Data Management and Mining Big Data Analytics Machine Learning Geographical Information Systems His work spans trajectory analysis, maritime and vessel traffic forecasting, and spatiotemporal data management. Notable projects include leading roles in EU-funded initiatives like GREEN.DAT.AI and EMERALDS, focusing on energy-efficient AI and urban mobility analytics. Awards and Recognition : Five Best Paper Awards, including the Ralf H. Güting Best Research Paper Award (2021) and Best Demo Paper Awards at SIGSPATIAL (2021). He also ranked 1st in the SemEval-2017 Task 4 for sentiment analysis. Professional Contributions : Serves on editorial boards (e.g., ECML PKDD, DMKD) and organizes workshops like BMDA. His teaching spans undergraduate and graduate courses in Data Science, Big Data Management, and Statistical Data Mining. Labs and Teams : Co-founder of the Data Science Lab at the University of Piraeus, collaborating across 4 departments and multiple researchers. Current efforts include maritime digitalization (VesselAI) and real-time trajectory prediction frameworks like ARGO.

• Service-Oriented Architectures
• Data Management
• Cloud Computing
• +۵ مورد دیگر

Dimosthenis Kyriazis is a Professor at the University of Piraeus, affiliated with the Research Center. He currently teaches courses such as C Programming, e-Business, and Information Systems. His research focuses on service-oriented architectures, cloud/edge computing, data management, and healthcare informatics. Kyriazis holds a PhD from the National Technical University of Athens (NTUA) and has contributed to EU-funded projects like BigDataStack and CrowdHEALTH, addressing quality of service, workflow management, and IoT applications. He leads research on data management in cloud/edge environments, socially-enhanced IoT management, and big data applications in sectors like finance and e-health. His expertise spans distributed systems, software engineering, and interdisciplinary collaborations with industries and academia. He has participated in EU working groups on Future Internet Architecture and Cloud QoS&SLAs. Kyriazis actively seeks interns and collaborates on initiatives like AI-driven healthcare platforms (iHELP) and sustainable computing practices. His work emphasizes human-centric AI, data interoperability, and ethical AI frameworks such as AI4Gov for transparent governance. Education: Diploma in Electrical and Computer Engineering (NTUA, 2001), MSc in Techno-economics (NTUA/University of Athens/University of Piraeus, 2004), PhD in Service-Oriented Architectures (NTUA, 2007). Key research areas include federated data marketplaces (FAME), healthcare data integration (holistic health records), and AI applications in finance (DeepVaR). His publications address topics like explainable AI (XAI), defect detection in manufacturing, and environmental risk correlation with health. Grants and collaborations involve coordinating EU projects targeting data governance frameworks, energy-efficient mobility data spaces (Mobispaces), and AI for policy-making (e.g., OECD AI policy analysis). His contributions bridge technical innovation with societal impact through sustainable computing and ethical AI practices.

Christopher G. Chuteمشاهده پروفایل

استاد

• Biomedical Informatics
• Health Information Technology
• Biomedical Terminologies
• +۵ مورد دیگر

Christopher G. Chute, MD, DrPH, MPH, serves as Bloomberg Distinguished Professor at Johns Hopkins University with joint appointments in the School of Medicine (Divisions of General Internal Medicine and Health Sciences Informatics), Bloomberg School of Public Health (Department of Health Policy and Management), and School of Nursing (Division of Health Informatics). He concurrently holds the position of Chief Health Research Information Officer for Johns Hopkins Medicine. Dr. Chute obtained his medical degree from Brown University, completed internal medicine residency at Dartmouth, and earned a doctorate in Epidemiology from Harvard University. He is board-certified in Internal Medicine and Clinical Informatics. His research focuses on biomedical terminologies, health information technology standards, big data analytics in healthcare, and electronic medical record systems. He leads major initiatives including the World Health Organization's ICD-11 revision as Chair of the Revision Steering Group. His work bridges clinical informatics, public health surveillance, and computational approaches to healthcare data. Analysis of recent publications reveals concentrated research in pandemic informatics, particularly leveraging large-scale EHR data from the National COVID Cohort Collaborative (N3C). Key themes include: long COVID characterization, health disparities in rural populations, AI/ML applications for clinical prediction, antiviral treatment outcomes, and data standardization methodologies. Scientific Awards and Honors Elected to National Academy of Medicine (2024) Bloomberg Distinguished Professor (2015) President, American College of Medical Informatics (2015-2020) Inaugural Fellow, International Academy of Health Sciences Informatics (2017) HL7 Fellow (2017) AMIA President's Award (2002) IBM Faculty Award (2005) Homer Warner Award for Most Outstanding Contribution (1998) Dr. Chute directs substantial research programs including NIH-funded projects on EHR data reuse, Beacon Community initiatives, LexGrid terminology infrastructure, and clinical-translational informatics. As Chief Research Information Officer, he oversees enterprise data governance and research computing infrastructure at Johns Hopkins Medicine.

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +۶ مورد دیگر

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

• Cloud Computing Economics
• Software Service Platforms
• IT Service Economics
• +۶ مورد دیگر

Jörn Altmann is a Professor in the Department of Computer Science and Engineering at Seoul National University's College of Engineering. With a publication history spanning over three decades from 1994 to 2025, he has established himself as a leading researcher in the economics of computing services, particularly focusing on cloud computing economics, software service platforms, and IT service ecosystems. He has served as editor for the GECON (Economics of Grids, Clouds, Systems, and Services) conference proceedings for multiple years, demonstrating his leadership in the field. Altmann's research interests center on the economic aspects of computing infrastructure, with particular expertise in cloud federation models, resource allocation mechanisms, and the business dynamics of software service ecosystems. His work bridges technical computing concepts with economic theory, examining how market mechanisms can be applied to computing resource allocation. Recent research has expanded into cybersecurity economics, AI adoption frameworks, and knowledge management in distributed systems environments. His publications consistently explore the intersection of technology adoption patterns and economic incentives within computing ecosystems. Analysis of his recent publications reveals a strong focus on cloud federation architectures, trust mechanisms in cybersecurity information sharing, and the economic modeling of value creation in distributed computing environments. His work demonstrates a methodological diversity, employing game theory, agent-based modeling, systematic literature reviews, and empirical analysis of industry dynamics, particularly in the Korean technology sector. Altmann has collaborated extensively with researchers worldwide, with notable long-term collaborations with José Ángel Bañares, Omer F. Rana, Kurt Vanmechelen, and Korean researchers including Kibae Kim. His work has appeared in prestigious venues including Future Generation Computer Systems, IEEE Transactions on Engineering Management, and the GECON conference series which he has helped shape through editorial leadership. His research has practical implications for cloud service providers, enterprise IT strategy, and policy development in digital infrastructure. The consistent publication record and editorial leadership suggest active supervision of graduate students and research teams, though specific students are not documented in the provided publication records.

Dilum Bandaraمشاهده پروفایل

دانشگاهی

• Distributed Systems
• Blockchain
• Cloud Computing
• +۱۳ مورد دیگر

Dilum Bandara is a Principal Research Scientist at CSIRO's Data61 in Australia and an Adjunct Senior Lecturer at the School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales (UNSW). He has previously served as a Senior Lecturer at the University of Moratuwa, Sri Lanka, and has over two decades of experience in research, teaching, and consultancy in distributed systems, security, and software engineering. His academic qualifications include: PhD in Computer Science, Colorado State University, USA (2012) MS in Computer Science, Colorado State University, USA (2008) BSc Eng. (Hons) in Computer Science and Engineering, University of Moratuwa, Sri Lanka (2004) Dilum's research interests are centered on Distributed Systems (Blockchain, Cloud, P2P), Computer Security , Software Architecture , Data Engineering , Performance Engineering , and the Internet of Things (IoT) . He applies these technologies in multidisciplinary domains such as Supply Chains , Digital Finance , Environmental, Social, and Governance (ESG) , Fleet Management , and Weather Monitoring . His work emphasizes real-world impact through trusted data management in multi-party ecosystems. The analysis of his recent publications reveals a strong focus on blockchain for transparency and security, cloud-native performance engineering, IoT integration with edge and cloud, and data-driven solutions for smart cities and sustainability. His work consistently bridges theoretical innovation with practical deployment, particularly in national and international infrastructure projects. His scientific awards include: Best Paper Award at BPM 2024 Multiple CSIRO internal awards (Customer First, Engineering and Technology, Collaboration) from 2021–2023 Student Paper Award (Merit) at IEEE SOLI 2018 Award of Excellence for Outstanding Research at University of Moratuwa (2015–2018) Dilum has led and contributed to several research grants, particularly during his time at the University of Moratuwa, including projects on smart city integration, real-time data forecasting, and cloud platforms for scientific computing. He has also held leadership roles such as Director of the Engineering Research Unit and co-founder of VaticHub. He is actively involved in professional communities as a Senior Member of IEEE and a Chartered Engineer with IESL. At CSIRO, he serves as a Health and Safety Representative, reflecting his commitment to corporate citizenship. He has advised students and early-career researchers, though specific names are not listed in the provided text. His labs and research teams include the Architecture and Analytics Platforms (AAP) team at CSIRO Data61 and collaborations with academic institutions like UNSW and University of Moratuwa.

Oscar Romero Moralمشاهده پروفایل

استاد

• Data Management
• Data Science
• Data Governance
• +۷ مورد دیگر

Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.

Xubo Yueمشاهده پروفایل

استادیار

• Federated Data Analytics
• Bayesian Optimization
• Continuous Optimization
• +۵ مورد دیگر

Xubo Yue is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on federated data analytics, Bayesian optimization, continuous optimization, Gaussian processes, and deep learning. He holds a PhD in Industrial & Operations Engineering from the University of Michigan, Ann Arbor (2023). His work bridges theoretical advancements with practical applications in advanced manufacturing, predictive maintenance, and sustainable materials discovery. Key affiliations include the Institute of Industrial and Systems Engineers (IISE), INFORMS, and the American Statistical Association (ASA). Recent research emphasizes scalable federated learning frameworks for distributed systems, causal inference in sensor networks, and sharpness-aware optimization techniques to enhance generalization. His methodologies are applied to interdisciplinary domains such as materials science, IoT systems, and renewable energy simulations. Research trends reveal a focus on: Federated learning architectures for privacy-preserving analytics Bayesian optimization for high-dimensional design spaces Integration of causal reasoning with machine learning systems Autonomous experimentation for accelerated materials discovery No scientific awards are explicitly listed in the provided information. His academic advising and grant activities are not detailed in the current data.

• Software Performance Engineering
• Microservice Architectures
• Cyber-Physical Systems
• +۳ مورد دیگر

Catia Trubiani is a faculty member at the Gran Sasso Science Institute in L'Aquila, Italy, specializing in software performance engineering, architectural analysis, and cyber-physical systems. Her work bridges theoretical modeling with practical applications, focusing on performance antipatterns, uncertainty quantification, and DevOps practices. In research , she explores performance modeling of microservices, federated learning systems, and cyber-physical systems, with a strong emphasis on architectural decision-making under uncertainty. Her recent articles analyze performance regression testing, aging detection in networks, and anti-pattern correlation in distributed systems, reflecting her interest in robust, scalable software solutions. She collaborates extensively with researchers like Raffaela Mirandola , Alberto Avritzer , and Riccardo Pinciroli , contributing to tools and frameworks such as PLUS (Performance Learning for Uncertainty of Software) and VisArch (Visualisation of Performance-Based Architectural Refactorings). Her work has been published in journals including IEEE Transactions on Software Engineering and Future Generation Computer Systems , as well as conferences like ICSE, ECSA, and ICPE.

• Machine Learning
• Distributed Machine Learning
• Medical Signal Processing
• +۳ مورد دیگر

Dr. Mahdi Barhoush is a Postdoc researcher and teaching assistant at RWTH Aachen University since 2023. His work focuses on applying machine learning to medical domains, particularly in distributed learning systems and signal processing. PhD (2023): "Using machine learning in the medical field: speaker signal processing and distributed learning systems" MSc (2015): Communication Engineering, RWTH Aachen University BSc (2013): Telecommunication Engineering, Arab American University His research spans medical machine learning, edge computing, and IoT optimization, with recent publications on federated learning, split learning architectures, and privacy-preserving ECG classification systems. He contributes to advancements in energy-efficient AI for resource-constrained environments and speaker localization in hospitals. Scientific achievements include: Best Paper Award at RADAR 2024 He collaborates with the INDA Institute in Aachen, contributing to active research projects in distributed learning, 6G technologies, and biomedical AI applications.

Guangya Yangمشاهده پروفایل

دانشیار

• Power System Stability
• Offshore Wind Energy
• Cyber-Physical Power Systems
• +۷ مورد دیگر

Guangya Yang is an Associate Professor at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), with expertise in power system stability, control, and protection of low-inertia systems, particularly in offshore wind applications. He has worked full-time as an electrical design and simulation specialist at Ørsted (2020-2021) and maintains active roles in IEEE, IEC standardization, and editorial work as lead editor for IEEE Access PES. PhD in Electrical Engineering (University of Queensland, 2008) Senior Member of IEEE Former expert member of European Technology & Innovation Platform for solar PV His research focuses on electromagnetic transient simulation, cyber-physical energy systems, and grid compliance for wind power plants. He leads the Horizon 2020 InnoCyPES project (€12m+ grants), training 15 early-stage researchers in cyber-physical energy systems. Key contributions include validating synchronous condensers for low-inertia systems and developing industry training programs on offshore wind control strategies. Coordinated Hardware-in-the-Loop test bench standards (IEC61400-21-5) Advances in grid-forming converter stability and fault ride-through assessment Developed digital twins for power grid validation Scientific Recognition: World’s Top 2% Scientists (Elsevier/Stanford, 2024) Advising & Grants: Supervises 5 PhD students in projects related to machine learning, digital twins, and predictive maintenance. Coordinates €12m+ in research grants, including Horizon 2020’s InnoCyPES. Labs & Collaborations: Works with Ørsted, industry partners, and international universities on offshore wind validation and digitalization.

• Operations Research
• Business Informatics
• Information Systems
• +۳ مورد دیگر

Abdul Hakmeh is a Researcher at the University of Hildesheim, affiliated with the Faculty of Mathematics, Natural Sciences, Economics and Computer Science. He works within the Institute for Business Administration and Information Systems, specifically in both the Department of Business Administration and Operations Research and the Department of Information Systems and Enterprise Modeling. His research focuses on the intersection of operations research, information systems, and data analytics, with particular expertise in time series analysis and machine learning applications. His work bridges theoretical foundations with practical business solutions, contributing to both academic knowledge and real-world business applications. Dr. Hakmeh's recent research has centered on developing interpretable machine learning frameworks for time series classification, with applications in various business contexts. His publications demonstrate cutting-edge research at the intersection of operations research and artificial intelligence, particularly in graph-based approaches to multivariate time series analysis. As an active member of the academic community, Dr. Hakmeh contributes to the educational mission of the university through teaching and supervision activities within the business informatics programs. The University of Hildesheim emphasizes practical application of theoretical knowledge, with strong industry connections through initiatives like the IT Arbeitskreis comprising approximately 40 regional companies.

Shashikant Ilagerمشاهده پروفایل

استادیار

• Distributed Systems
• Energy Efficiency
• Machine Learning
• +۴ مورد دیگر

Dr. Shashikant Ilager is an Assistant Professor at the Informatics Institute (IVI) , University of Amsterdam. His research focuses on distributed systems , energy efficiency , and machine learning , with a specific emphasis on sustainable large-scale AI platforms. Current affiliation: University of Amsterdam (Oct 2024–present) Previous roles: Postdoctoral Researcher at TU Wien; Visiting Research Scientist at IBM PhD: CLOUDS Lab, University of Melbourne Research Focus Dr. Ilager develops data-driven approaches to optimize cloud/edge platforms for environmental and economic sustainability , particularly in AI workloads. His work bridges system characterization with learn-centric optimization techniques. Recent Publications 2025: ACM e-Energy (LLM carbon amortization), TAAS (self-adaptive edge monitoring), CCGRID (code generation efficiency). 2024: ICSOC (edge time series classification), EdgeSys (federated learning with GANs). Awards Best Paper Award @ ACM/IEEE UCC 2023 Community Engagement Organizer of the GreenSys workshop (2025) at EuroSys. Member of HPDC 2025 Technical Program Committee.

Sima Siami-Naminiمشاهده پروفایل

مدرس

• macroeconomics
• monetary policy
• time series analysis
• +۵ مورد دیگر

Sima Siami-Namini serves as a Lecturer at Johns Hopkins University, teaching in the MS in Applied Economics program with extensive experience in undergraduate and graduate instruction across economics, statistics, and finance disciplines. Her academic credentials include advanced interdisciplinary training: PhD in Applied Economics (minor: Statistics), Texas Tech University, 2020 Master's in Statistics, Texas Tech University, 2022 Master's in Artificial Intelligence (Machine Learning focus), University of North Texas, 2023 Her research program integrates macroeconomic theory with cutting-edge computational methods, specializing in monetary policy analysis, time series econometrics, and AI-driven forecasting. She bridges traditional economic modeling with machine learning applications, particularly in anomaly detection, data visualization, and large language model implementations for economic forecasting. Analysis of her publication trajectory (2020-2024) reveals three dominant research streams: (1) deep learning architectures (LSTM, TCN) for time series forecasting and anomaly detection, (2) monetary policy impacts on income inequality using FAVAR/SVECM models, and (3) natural language processing applications for Federal Reserve communication analysis. Her recent work increasingly incorporates large language models for domain-specific economic analysis and code generation. No documented scientific awards or major honors appear in the available records. She mentors students in the Applied Economics program with emphasis on quantitative research methods, though specific grant funding details remain undisclosed. Her teaching methodology incorporates experiential learning techniques adapted from digital forensics education frameworks. No dedicated research laboratories or institutional teams are referenced in the source materials.

Venkata Virajit Garbhapuمشاهده پروفایل

پژوهشگر ارشد

• Optical Communications
• Network Optimization
• Artificial Intelligence in Networking
• +۴ مورد دیگر

Venkata Virajit Garbhapu (Viru) is a Research Fellow at the CONNECT Centre, Trinity College Dublin, specializing in optical networks with emphasis on physical layer optimization and cross-layer integration. Education: PhD, Institut Polytechnique de Paris (CIFRE scholar with Huawei) Laura Magistrale, Politecnico Di Milano His research focuses on maximizing network capabilities through physical layer impairment-aware frameworks, demonstrated by 52.8% training time reduction in distributed AI systems using 800G DCI links and superior failure recovery via optical circuit switching. Key contributions include per-channel power allocation algorithms balancing linear/nonlinear impairments and feasible C-band integration of classical/QKD signals through Raman noise-aware wavelength allocation. His work centers on developing sophisticated simulators enabling cross-layer optimization for functionalities like QKD and packet switching. Analysis of his 2019-2025 publications reveals a dominant trend toward AI-driven network automation, with increasing emphasis on digital twins and large language models for lifecycle management, alongside quantum-classical signal coexistence. The research consistently bridges theoretical algorithms with experimental demonstrations, showing progression from transfer learning for failure detection to advanced AI/quantum integration. No scientific awards were mentioned in available sources. His PhD was supported by a CIFRE industrial scholarship with Huawei, and he participated in the European QSNP project. No student advisement details are provided, though his BONSAI research addressed optical network failure management using transfer learning to overcome data scarcity. He operates within the CONNECT Centre ecosystem at Trinity College Dublin while maintaining active collaboration with Huawei Paris Research Center, reflecting strong industry-academia integration in optical communications research.