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Rahul SharmaView profile

Associate Professor

• Control Systems
• Renewable Energy
• Power Systems
• +5 more

Associate Professor Rahul Sharma is affiliated with the School of Electrical Engineering and Computer Science at The University of Queensland (UQ), within the Faculty of Engineering, Architecture and Information Technology. His primary research focuses on control systems applications, particularly in solar farm fault detection, grid-connected inverter control, and demand side management. His work emphasizes system modeling, control development, and model-based fault diagnosis, with significant industrial relevance in renewable energy and smart grid technologies. Research Interests: - Solar farm fault detection and diagnosis via control theory to identify underperforming panels. - Advanced control methods for grid-connected inverters to enhance power quality and stability. - Demand side management through novel control algorithms for distributed energy resources. His experimental work includes field trials at Gatton Solar Farm and collaborations with industry partners. Articles Trends: Recent publications emphasize network-aware control of distributed energy resources, dynamic operating envelopes for demand response, and smart grid optimization. Key themes include low-voltage distribution networks, prosumer P2P trading, and fault diagnosis in power systems. Advising & Labs: He supervises prospective PhD students in control systems and renewable energy. Experimental setups at UQ and field trials at Gatton Solar Farm support his research on fault detection and grid systems.

Evaggelia PitouraView profile

Professor

• Data Management
• Distributed Databases
• Database Systems
• +7 more

Evaggelia Pitoura is a Professor in the Department of Computer Engineering and Informatics at the University of Ioannina, Greece, and a Lead Researcher at the Archimedes Research Unit, ATHENA Research Center. Her work spans data management, distributed systems, and mobile computing, with a strong focus on database personalization and diversity. Her research interests include data diversity, contextual preferences, XML data management, peer-to-peer systems, and mobile computing . She has pioneered work in result diversification, preference-aware querying, and distributed data indexing, publishing extensively in top venues such as PVLDB, ICDE, SIGMOD, and IEEE TPDS. The recent publications highlight a consistent trend in personalized, context-aware database systems , with emphasis on improving user experience through intelligent query processing, recommendation, and efficient data organization in distributed environments. Best Paper Award, SIGMOD 1999 Best Paper Award, PVLDB 2013 She has advised several PhD students, including Georgia Koloniari, Kostas Stefanidis, Marina Drosou, and Konstantinos Semertzidis, who now hold positions in academia and industry. She teaches courses in Databases and Information Retrieval and actively contributes to the academic community through program committee roles in major conferences like ICDE, VLDB, SIGMOD, and WWW. She leads the Distributed Management of Data Group , fostering innovation in scalable and intelligent data systems.

Nikolaos PaterakisView profile

Assistant Professor

• Power System Optimization
• Electricity Markets
• Renewable Energy Integration
• +5 more

Nikolaos Paterakis is an Assistant Professor of Power System Optimization and Electricity Markets with the Electrical Energy Systems research group at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). He is the founder and principal investigator of the Electricity Markets & Power System Optimization Laboratory (EMPSOLab) established in 2019, and a member of the Cyber-Physical Systems Center Eindhoven (CPSe). His research focuses on applying optimization and machine learning techniques to power system and electricity market problems, particularly regarding renewable energy integration and smart grid technologies. Dr. Paterakis received his Dipl.Eng. from Aristotle University of Thessaloniki in 2013, followed by a PhD in Industrial Engineering and Management (cum laude) from the University of Beira Interior in 2015. After serving as a post-doctoral fellow at TU/e from 2015-2017 and working as a consultant for the Energy Market Regulatory Authority of Turkey, he was appointed Assistant Professor at TU/e in April 2017. His research spans power system optimization, electricity market design, renewable energy integration, and the application of machine learning techniques to grid management problems. Recent work emphasizes distributed energy resource integration, local electricity markets, congestion management in low-voltage grids, and real-time grid control using advanced optimization techniques. His publications demonstrate a clear trajectory toward increasingly sophisticated methods for managing grid constraints while enabling market participation of distributed energy resources. Dr. Paterakis has received several prestigious awards including IEEE SEGE'15, SEST 2019, and SEST 2020 Best Paper Awards, and recognition as a Best Reviewer for IEEE Transactions on Smart Grid (2015, 2017) and IEEE Transactions on Sustainable Energy (2016). He serves as Associate Editor for multiple journals including IET Renewable Power Generation, IEEE Systems Journal, IEEE Transactions on Intelligent Transportation Systems, and Elsevier's e-Prime. He leads multiple research projects including MEGAMIND (NWO-funded), P2P-TALES (NWO-funded), and the Electricity Markets Game series (TU/e BOOST!-program). His educational contributions include teaching courses on power system analysis and optimization, electricity markets modeling, and developing innovative educational tools for power systems education. In 2021, he was elevated to Senior Member of the IEEE Power & Energy Society.

• Data Management
• Knowledge Graphs
• Machine Learning
• +3 more

Zoi Kaoudi is a researcher at the IT University of Copenhagen , specializing in Data Management , Knowledge Graphs , and Machine Learning . Her work focuses on cross-platform data processing, query optimization, and scalable systems for graph analytics. She has published extensively in venues like SIGMOD , VLDB , and ISWC , with recent contributions to Apache Wayang , DORIAN , and Space-Efficient Graph Algorithms . Her research bridges theoretical advancements with practical frameworks for data science pipelines. Collaborations include Volker Markl, Jorge-Arnulfo Quiané-Ruiz, and Ioana Manolescu. She has explored topics such as Parameter Servers , Knowledge Graph Embeddings , and RDF Data Management in the cloud. Her work emphasizes open science and system integration.

Jussi KangasharjuView profile

Professor

• Edge Computing
• Information-Centric Networking
• Content Distribution
• +6 more

Jussi Kangasharju is a Professor in the Department of Computer Science at the University of Helsinki and leads the Collaborative Networking research group . He is also a supervisor for the Doctoral Programme in Computer Science and a member of IEEE and ACM . Research Interests: Edge Computing Information-Centric Networking Content Distribution Green Networking Future Internet Development Scientific Awards: Best Paper Award (2015) Winner of 9th Helsinki Science Slam Competition (2016) Best Paper Award (2004) Key Projects: He has led initiatives like ELLIS-instituutti (2025–2032) and University Profiling Funding (2025–2030), focusing on transdisciplinary networks and future Internet sustainability. Academic Visits: Notable visits include Seoul National University (2012) and the International Computer Science Institute (2013), further enriching his collaborative work.

Sajjad Fattaheian DehkordiView profile

Researcher

• Power Systems Engineering
• Smart Grids
• Renewable Energy Integration
• +3 more

Sajjad Fattaheian Dehkordi is a Postdoctoral Researcher in the Department of Electrical Engineering and Automation at Aalto University, Espoo, Finland. His research focuses on advanced energy management systems for modern power grids, with emphasis on distributed and transactive control approaches for resilient and efficient grid operations. His core research interests include: Power system resilience under high renewable penetration Microgrid and energy community optimization Distributed energy resource integration Electric vehicle-grid interaction and V2G systems Real-time congestion and ramping management Analysis of his 2022-2024 publications reveals a dominant trend toward multi-agent transactive frameworks addressing grid stability challenges. His work consistently targets voltage regulation, asymmetrical power flows, and ramping events in distribution systems, leveraging optimization techniques like MILP while incorporating flexibility concepts for renewable integration. Key innovations include distance-driven P2P/P2G transactions and incentive-based congestion management. No scientific awards are documented in the available records. Information regarding student advising, research grants, or leadership roles is not provided in current documentation.

Jun DaiView profile

Assistant Professor

• Artificial Intelligence in Accounting
• Blockchain Applications
• ESG Assurance
• +4 more

Jun Dai is an Assistant Professor of Accounting Information Systems at Michigan Technological University's College of Business and a Richard and Joyce Ten Haken Faculty Fellow. Her research focuses on applying blockchain, AI, and Industry 4.0 technologies to enhance accounting efficiency and transparency. She holds a PhD from Rutgers University and a BS from Southwestern University of Finance and Economics in Chengdu, China. Dr. Dai serves as an associate editor of the Journal of Emerging Technologies in Accounting and contributes to editorial boards of Journal of Information Systems and International Journal of Accounting Information Systems . She chairs the Strategic and Emerging Technologies Research Workshop at the American Accounting Association Annual Meetings. Her work has been published in top journals such as Accounting and Finance , Accounting Horizons , and CPA Journal . Her research interests include ESG assurance, audit analytics, and blockchain-based accounting systems. Notable achievements include the 2023 Outstanding Researcher Award from the American Accounting Association, the 2021 Notable Contributions Award, and the 2017 Bright Idea Award for innovative research. She has also explored topics like AI ethics in managerial accounting, smart contracts in P2P lending, and satellite-based GHG emissions auditing. Teaching focuses on accounting systems, analytical methods, and database management. Her interdisciplinary approach bridges technology and accounting, addressing modern challenges in audit processes, fraud prevention, and sustainable reporting.

Abdul-Rahman Mawlood-YunisView profile

Associate Professor

• Artificial Intelligence
• Android Mobile Application Development
• Software Engineering
• +8 more

Abdul-Rahman Mawlood-Yunis is an Associate Professor in the Department of Physics and Computer Science at Wilfrid Laurier University. His research focuses on Artificial Intelligence, Android Mobile Application Development, Software Engineering, Distributed Systems, and P2P Networking with an emphasis on fault-tolerant systems and semantic web technologies. He has contributed to frameworks for live streaming apps and machine learning algorithms for feature selection. Research interests include: Chatbots and Natural Language Processing (NLP) Ontology engineering and knowledge representation Algorithm design for distributed systems Mobile agent performance analysis Fault-tolerant semantic P2P networks His recent work (2022-2024) emphasizes machine learning applications in feature selection and real estate price estimation, reflecting a shift towards data-driven solutions. Earlier contributions (2003-2013) explored foundational aspects of mobile agents and semantic interoperability in P2P networks. Teaching responsibilities include courses on Android development and Java programming, with associated open-source materials and courseware. His book Android for Java Programmers provides foundational resources for students and instructors. Languages spoken: English, Kurdish, Arabic, Farsi.

• Cloud Computing
• Distributed Systems
• Data Processing
• +4 more

Luis Antunes Veiga is an Associate Professor and Senior Researcher at INESC-ID Lisbon, affiliated with the Distributed Systems Group and the Computing Systems and Communication Networks Laboratory. His research focuses on Cloud Computing, Edge Computing, Distributed Systems, and Big Data processing. He teaches courses such as 'Cloud Computing and Virtualization' and 'Operating Systems, Virtualization and Cloud Computing.' His work emphasizes scalable systems, network-aware workflows, and resource-efficient data processing. Research Interests: His primary areas include distributed systems architectures, edge computing frameworks, graph processing algorithms, and software-defined systems. He explores topics like latency-aware network design, resource auction mechanisms for edge environments, and interoperable service workflows. His contributions span theoretical frameworks and practical implementations, such as the RATEE system for edge resource trading and the VeilGraph incremental graph processing framework. Publications: His recent work addresses challenges in distributed systems, such as elastic scaling of stream processing, efficient graph processing in Spark, and latency optimization in internet-scale workflows. These publications reflect a trend toward integrating software-defined approaches with edge and cloud infrastructures. Notable Awards: Best Young Researcher INESC-ID, Excellence in Teaching (IST 2012), and a Best-Paper Award at ACM/IFIP/Usenix Middleware 2007. Labs & Teams: Active in the Computing Systems and Communication Networks Laboratory, leading projects on edge computing and distributed systems.

Anne-Marie KermarrecView profile

Researcher

• Gossip-based networking
• P2P systems
• Social Networks
• +6 more

Anne-Marie Kermarrec is a Senior Researcher at INRIA (France), with prior roles at Microsoft Research (UK) and the University of Rennes 1 (France). Her work focuses on decentralized systems, including gossip protocols, peer-to-peer networks, social networks, and collaborative filtering. PhD in Computer Science (Rennes, 1996) Her research spans epidemic algorithms, content-based search in large-scale networks, and scalable group communication. She pioneered gossip-based peer sampling and multicast infrastructures like Scribe and SplitStream. Her publications highlight applications in decentralized news recommendation (AllYours), network coding, and privacy-preserving social platforms (Gossple). Key article trends include gossip protocols , P2P systems , social network analysis , decentralized storage , and collaborative filtering . She received the Michel Monpetit Award (2011), ERC Starting Grant (GOSSPLE, 2008-2013), and an ICDCS Best Paper Award (2010). Michel Monpetit Award (2011) ERC PoC AllYours (2013) ERC Starting Grant GOSSPLE (2008-2013) ICDCS Best Paper Award (2010) Kermarrec led program committees for major conferences (e.g., EuroSys, Middleware) and chaired the ACM Software System Award. Her software contributions include AllYours (news recommender), GossipLib (gossip library), and GossipPeer (P2P platform maintenance).

• Peer-to-Peer Overlay Networks
• Home Automation
• IP Telephony
• +3 more

Dr. Mario Kolberg is a Senior Lecturer in the Department of Computing Science at the University of Stirling. He holds a PhD from the University of Strathclyde and has held roles at institutions including Humboldt University in Berlin and the University of Strathclyde. His research focuses on Peer-to-Peer (P2P) overlay networks, Home Automation, and IP Telephony, with contributions to areas like distributed feature interaction management and network protocol optimization. Key roles include co-investigator in ESRC’s Interlife and MATCH projects, exploring P2P networks in virtual worlds and healthcare integration. He leads initiatives such as the Sysnet Knowledge Transfer Partnership, developing P2P overlays for mobile devices. Kolberg is an IEEE Senior Member and Series Editor for the IEEE Communications Magazine’s Consumer Communication and Networking series, previously serving as TPC Chair for IEEE CCNC 2011. His research spans P2P multicast, home automation systems, and network efficiency, with recent work addressing IoT protocols (e.g., 6TiSCH), mobile network optimization, and integrating social media data for transport prediction. Over 79 publications highlight his technical depth, including influential papers on feature interaction detection and hybrid multicast systems. Education: PhD in Computer Science from University of Strathclyde Key Projects: Interlife (P2P in 3D virtual education), MATCH (healthcare networks), Sysnet KTP (mobile P2P) Awards: IEEE Senior Member, Series Editor role His work bridges academia and industry, addressing real-world challenges in networking and distributed systems.

• Artificial Intelligence
• Machine Learning
• Internet of Things
• +13 more

Edoardo Patti is an Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He is actively involved in research and teaching, focusing on artificial intelligence, machine learning, IoT, smart grids, energy communities, co-simulation, and digital twin technologies. He is a member of the EDA (Electronic Design Automation) research group and the Interdepartmental Center IAM@PoliTo for Integrated Additive Manufacturing. His research interests span Artificial Intelligence, Machine Learning, Internet of Things, Smart Grids, Energy Communities, Co-simulation, Distributed Software Infrastructure, Digital Twin, Physics-Informed Neural Networks, and Reinforcement Learning . His work is closely aligned with Sustainable Development Goals such as Affordable and Clean Energy (Goal 7), Sustainable Cities (Goal 11), and Climate Action (Goal 13). The latest publications reveal a strong trend in AI-driven modeling for energy systems, including digital twins for wave energy converters, urban electric mobility simulation, real-time battery health estimation, and co-simulation platforms for integrated energy systems. His work combines machine learning with physical models and large-scale distributed systems to address sustainability challenges. He supervises several PhD students, including Maria Adelaide Loffa, Matia Torlini, Rafael Natalio Fontana Crespo, Pietro Rando Mazzarino, Claudia De Vizia, and Marco Massano. He has led numerous research projects funded by EU (H2020), PNRR, regional initiatives, and corporate contracts, particularly in additive manufacturing, smart grids, and IoT platforms. He is involved in multiple research groups and centers: EDA - Electronic Design Automation (DAUIN) Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing

Dimitrios TsoumakosView profile

Associate Professor

• Big Data Management
• Distributed Systems
• Cloud Computing
• +5 more

Dimitrios Tsoumakos serves as an Associate Professor of Big Data Management Systems at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) where he also directs DBLab, the Knowledge and Database Systems Laboratory. His academic career spans over two decades with significant contributions to large-scale data management and distributed systems. Diploma in Electrical and Computer Engineering from NTUA (1999) M.Sc. in Computer Sciences from University of Maryland (2002) Ph.D. in Computer Sciences from University of Maryland (2006) Professor Tsoumakos' research focuses on the intersection of big data management, cloud computing, and distributed systems. His work addresses fundamental challenges in large-scale data processing, including wavelet synopses for data summarization, vector embedding frameworks for analytics operators, multi-engine analytics systems, and cloud application deployment with failure recovery mechanisms. His research consistently bridges theoretical algorithms with practical implementations for real-world data challenges. His publication record demonstrates consistent innovation from early work on P2P data management systems through to current research on vector embeddings and deep reinforcement learning for cloud autoscaling. Recent work shows a clear progression toward content-based analytics, multi-dataset integration, and intelligent resource management in heterogeneous environments. Best Paper Runner Up Award at SSDBM 2019 Best Paper Award at CCGrid 2013 Professor Tsoumakos has secured substantial research funding through multiple European projects including RELAX (2023-2027), HiDALGO2 (2023-2026), DAPHNE (2021-2024), and previous initiatives like HiDALGO, TraMOOC, ASAP, CELAR, ARCOMEM, and GREDIA. These projects reflect his leadership in big data analytics, cloud computing, and distributed systems research. As director of DBLab, Professor Tsoumakos oversees research on the Knowledge and Database Systems Laboratory, which has produced significant work on analytics operators, multi-engine resource scheduling (IReS platform), cloud elasticity (TIRAMOLA), and RDF data management (H2RDF+). The lab maintains strong industry connections and has developed multiple open-source tools for big data analytics.

• Networked Systems
• Machine Learning for Networks
• Data Center Networking
• +7 more

Philip Brighten Godfrey is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC) and Technical Director at VMware (formerly Broadcom). He co-founded network verification startup Veriflow, which was acquired in 2019. His research focuses on networked systems, blending theoretical and practical approaches to low-latency networking, software-defined architectures, microservices communication, and machine learning for network optimization. Education: Ph.D. in Computer Science (UC Berkeley, 2009), B.S. in Computer Science (Carnegie Mellon, 2002) His work spans data center design, network verification (e.g., VeriFlow), congestion control (PCC Vivace), and innovative projects like cISP (Speed-of-Light Internet). Recent publications address microservice tracing (TraceWeaver), fault localization (Flock), and XR device offloading (XRgo). Notable awards include the ACM SIGCOMM Rising Star Award, NSF CAREER Award, Sloan Research Fellowship, and multiple best paper recognitions. He has chaired SIGCOMM and HotNets, and his teaching excellence in courses like CS 538 Advanced Computer Networks has been repeatedly recognized. Scientific Honors ACM SIGCOMM Rising Star Award NSF CAREER Award (2012) Sloan Research Fellowship (2014) Best Paper Awards (SIGCOMM, HotSDN, CoNEXT) IEEE ComSoc Data Storage Best Paper Engineering Council Outstanding Advisor Award (2015) Godfrey leads research in the Coordinated Science Laboratory (CSL) and contributes to the LDOS NSF Expeditions project. His group advises Ph.D. students on topics ranging from network verification to XR systems optimization, with alumni now at Meta, Google, and academic institutions like ETH Zurich.

Emrah AhiView profile

Assistant Professor

• Fixed Income Securities
• Derivatives
• Computational Finance
• +7 more

Dr. Emrah Ahi is an Assistant Professor of Finance at Özyeğin University's Faculty of Business Administration and a researcher at the Center for Financial Engineering. With over 15 years of experience in finance, he previously served as a fund manager at HSBC Global Asset Management. PhD in Finance (Özyeğin University, 2010-2016) MS in Computational Science and Engineering (Koç University, 2004-2007) BSc in Mathematics (Middle East Technical University, 2000-2004) His research spans fixed income securities, derivatives, asset pricing , and machine learning , with cross-disciplinary contributions to peer-to-peer network protocols and distributed systems . Key themes include risk modeling, volatility analysis, and robust computational methods in finance, alongside technical innovations in gossip-based data dissemination. Recent publications focus on volatility smile modeling in currency options, term structure estimation in emerging markets, and applying machine learning to credit risk analysis. Earlier work on buffer management in epidemic algorithms demonstrates methodological rigor in distributed computing. He teaches courses in Applied Financial Economics, Derivatives, Fixed Income Markets, and Investments. His affiliations include Özyeğin University's Center for Financial Engineering and collaborations in computational finance research.