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Eleni StaiView profile

Assistant Professor

• Optimization
• Communications Networks
• Data Analytics
• +4 more

Eleni Stai is an Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), affiliated with the Division of Communication, Electronic and Information Engineering. She holds advanced degrees in Electrical Engineering, Mathematics, and Applied Mathematical Sciences from NTUA and the National and Kapodistrian University of Athens. Her academic credentials include: Diploma in Electrical and Computer Engineering, NTUA (2009) B.Sc. in Mathematics, National and Kapodistrian University of Athens (2013) M.Sc. in Applied Mathematical Sciences, NTUA (2014) Ph.D. in Electrical Engineering, NTUA (2015) Dr. Stai's research integrates advanced optimization techniques with communications networks and energy systems. She develops stochastic and deterministic optimization frameworks for network resource allocation, data analytics on complex topologies, and smart-grid control applications. Her work bridges theoretical foundations with practical implementations in energy-harvesting networks, network slicing, and reinforcement learning for distributed systems. Analysis of her recent publications reveals dominant research thrusts in AI-driven network management (particularly O-RAN and network slicing), energy-integrated communications, and optimization of energy communities. A significant portion of her work addresses the convergence of 5G/6G networking with power systems, emphasizing real-time control and sustainability. Her scientific contributions have been recognized through prestigious awards: Chorafas Foundation Best Ph.D. Thesis award Thomaidis Foundation Best M.Sc. Thesis award Best Paper Award at ICT 2016 Best Presenter Award at IEEE ENERGYCON 2022 Dr. Stai serves on technical program committees for major international conferences and has co-authored the book "Evolutionary Dynamics of Complex Communications Networks". She teaches undergraduate courses in Queuing Systems, Computer Networks, and Social Network Analysis, reflecting her expertise in network theory and applications. Her research trajectory demonstrates continuous evolution from fundamental network optimization to AI-enhanced solutions for next-generation communication-energy systems. Her work builds upon her postdoctoral experience at EPFL (2016-2020) and ETH Zurich (2020-2023), where she developed advanced frameworks for communications networks and energy systems.

David NaumannView profile

Professor

• formal methods
• software security
• relational verification
• +7 more

David Naumann is Professor and Department Chair of Computer Science at Stevens Institute of Technology. His leadership in the department and active research program positions him as a key figure in programming languages and formal methods research. Naumann's research focuses on formal methods and software security, with particular emphasis on relational and hyperproperty verification , fine-grained confidentiality/integrity policies , and program analysis and verification . His work bridges theoretical foundations with practical applications in security-critical systems. He has developed novel program logics and verification techniques that enable precise reasoning about information flow and security properties. His recent publications (2022-2025) reveal a strong trend toward modular relational verification techniques with applications to pointer programs, distributed systems, and concurrent applications. The research spans theoretical foundations (algebraic structures for alignment) to practical tools (WhyRel prototype), demonstrating both depth and breadth in addressing verification challenges. Naumann has served as program committee co-chair for IEEE Computer Security Foundations Symposium (2021-2022) and has been active on committees for POPL, CCS, CSF, ECOOP, and other top venues. His editorial service includes ACM Transactions on Programming Languages and Systems, Formal Aspects of Computing, and Journal of Object Technology. He leads the Cypress research group at Stevens Institute of Technology and has secured significant funding from NSF, Microsoft Research, and Siemens. His mentoring extends to numerous PhD students who have gone on to successful careers in academia and industry.

Laxman DhulipalaView profile

Assistant Professor

• Parallel Algorithms
• Graph Processing
• Parallel Graph Algorithms
• +7 more

Laxman Dhulipala serves as an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, while also working as a research scientist at Google Research with the Graph Mining team. Dr. Dhulipala earned his Ph.D. from Carnegie Mellon University under Guy Blelloch's supervision and completed a postdoctoral fellowship at MIT with Julian Shun. His research centers on efficient parallel algorithms, particularly for parallel clustering and graph processing, along with developing computational models for emerging hardware technologies. His scholarly output demonstrates significant expertise across parallel computing domains, with particular emphasis on scalable graph algorithms, dynamic data structures, and computational geometry. Dr. Dhulipala's work bridges theoretical computer science with practical systems implementation, producing algorithms that achieve both theoretical optimality and real-world performance. His research group has made substantial contributions to benchmarking frameworks including the Graph Based Benchmark Suite (GBBS) and ParClusterers Benchmark Suite, establishing standardized evaluation methods for graph processing systems. The collective work shows progression from theoretical foundations to practical implementations that handle massive-scale datasets. Best Paper Award at SPAA 2022 Best Paper Runner Up at VLDB 2022 Distinguished Paper Award at PLDI 2019 Memorable Paper Award Finalist at NVMW'20 CMU's SCS Dissertation Award Honorable Mention As an educator, Dr. Dhulipala mentors numerous graduate students while teaching advanced courses in algorithm design and parallel computing. His research collaborations span multiple institutions including Carnegie Mellon University, MIT, and Google Research, reflecting his position at the intersection of academia and industry research.

Nicholas V. SarlisView profile

Professor

• Solid State Physics
• Seismology
• Geophysics
• +3 more

Nicholas V. Sarlis is a Professor of Experimental Solid State Physics at the Department of Physics , National and Kapodistrian University of Athens since 2017. With an h-index ≥36, he has authored 2 monographs, >155 peer-reviewed publications, and 80 conference communications. B.Sc. in Physics (1991), National and Kapodistrian University of Athens Ph.D. in Physics (1997), National and Kapodistrian University of Athens His research applies Natural Time Analysis to: Earthquake precursor identification Space weather and cosmic ray studies Climate phenomenon prediction (El Niño) Fracture mechanics and complex systems Seismicity and geoelectric field correlations Non-extensive statistical mechanics applications Recent work includes analyzing seismic entropy changes under time reversal and developing earthquake nowcasting systems. Publications span 2025's Statistical mechanics in geophysical contexts to 2024's cross-disciplinary disaster prediction tools.

Yannis TheodoridisView profile

Professor

• Moving Objects Databases
• Location-Based Services
• Data Mining for Spatial Objects
• +3 more

Yannis Theodoridis is a Professor at the Department of Informatics, University of Piraeus, leading the Information Systems Laboratory (InfoLab). He specializes in spatiotemporal databases, mobility analytics, and maritime data science. His research focuses on trajectory analysis, location-based services, and big data frameworks for transportation and maritime surveillance. Key projects include the MOD (Moving Objects Databases) initiative, the ARGOS framework for real-time trajectory prediction, and contributions to the HERMES trajectory database engine. He has advised 7 PhD students and co-authored numerous papers in IEEE and ACM venues. His work addresses challenges like vessel collision risk assessment, urban mobility optimization, and maritime event detection. He serves on the editorial board of the International Journal of Data Warehousing and Mining and contributes to conferences like PCI and ECML PKDD. His labs emphasize interdisciplinary approaches to mobility data science, integrating machine learning with domain-specific analytics.

• Artificial Intelligence
• Decision Support Systems
• Knowledge Management
• +3 more

Professor Gregoris Mentzas is a faculty member at the National Technical University of Athens, School of Electrical and Computer Engineering, where he directs the Division of Industrial Electric Devices and Decision Systems. His research focuses on AI-enabled decision systems, knowledge management, and semantic technologies applied to digital enterprises and e-government. With over 350 publications, he ranks among the top 2% most cited scientists globally. Research Interests: Artificial intelligence for decision augmentation, big data analytics in personalized health and smart mobility, semantic web technologies, and industrial internet of things. Current projects investigate trustworthy AI frameworks and hybrid intelligence systems for Industry 5.0. Teaching: Leads courses in Digital Enterprise Management, Strategic Information Systems, and Project Management at undergraduate and postgraduate levels, incorporating industry case studies and experiential learning approaches. Awards & Leadership: Top 2% Highly Cited Scientist (PLOS Biology 2021) 5 Best Paper Awards in international conferences Director of Information Management Unit (1997-present) Board Member of Institute of Communication and Computer Systems (2006-2009) Projects & Funding: Secured over €18 million in research grants through 60+ European projects with industry partners including SAP, IBM, and Siemens. Research outcomes led to three technology spin-offs.

Kostas MagoutisView profile

Professor

• Distributed computer systems
• Cloud computing
• Scalable data stores and stream processing
• +3 more

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete , and a collaborating researcher at FORTH-ICS . His research focuses on scalable distributed systems, cloud computing, IoT, and quantum-enhanced control. Education and Career: Ph.D. in Computer Science, Harvard University (2003) Research Staff Member, IBM T. J. Watson Research Center (2003-2009) Assistant Professor (2014-2019, tenured 2017) and Associate Professor (2020-2024), University of Crete Professor and Chair, University of Crete (2024-present) Research Interests: His work spans distributed computer systems , cloud computing , scalable data stores and stream-processing engines , Internet of Things , and the emerging area of quantum-enhanced control . Representative projects include the H.F.R.I.-funded QUADS (2025-2028) on quantum-enhanced adaptive systems, STREAMSTORE (2020-2023) on elastic stream processing, SmartCityBus on IoT-driven public transport, and the EU FP7 PaaSage project on model-based cloud lifecycle management. Awards and Honors: Best Paper Awards: USENIX ATC 2002, USENIX BSDCon 2002, IEEE SRDS 2014 (Best Student Paper), IoT 2024 (Runner-up) Grand Challenge Audience Award, ACM DEBS 2022 Best Poster Award, ACM EuroSys 2022 EU Marie Curie IEF Fellow (2009-2011) Alexander S. Onassis Fellow (1994-1995) and J. William Fulbright Scholar (1993-1994) Students and Mentoring: He has supervised or co-supervised more than 30 Ph.D., M.Sc. and undergraduate students, including Antonis Papaioannou (Ph.D. 2021), Efthimios Papageorgiou (current Ph.D.), and numerous M.Sc. graduates now in industry and academia. Labs and Teams: At FORTH-ICS he leads activities within the Distributed Systems and Storage Laboratory, coordinating research on scalable storage, stream processing, and IoT data management. The lab collaborates closely with European and national initiatives, hosting visiting researchers and industry partners.

• Informatics Law
• Cyber Law
• Data Protection
• +9 more

Ioannis Iglezakis is an Associate Professor at the School of Law, Aristotle University of Thessaloniki (AUTH), specializing in the intersection of law and technology. His academic career spans over three decades with continuous contributions to informatics law, cyber law, data protection, and privacy law. At AUTH, he teaches courses including Law of Informatics, Computers and Law I (Cyberlaw I), Computers and Law II (Privacy and Security in the Information Society), Legal Aspects of Cybercrime, Internet-Law, and Law and Information Technology. Dr. Iglezakis earned his undergraduate degree in Law from AUTH in 1987, followed by a Magister Legum Europae from Hannover University in 1993. He completed his postgraduate studies in History, Philosophy and Sociology of Law at AUTH in 1990 and earned his PhD in Law from AUTH in 1999. His educational background combines Greek legal training with European legal perspectives. His research interests focus on the legal challenges posed by digital technologies, with particular emphasis on data protection, privacy law, cybercrime regulation, digital rights, and the application of GDPR in various contexts. He has published extensively on topics including the right to be forgotten, digital identity management, workplace surveillance in the digital age, hate speech online, and the legal implications of emerging technologies like blockchain and artificial intelligence. His work bridges theoretical legal analysis with practical applications in the rapidly evolving digital landscape. The trends in his publications reveal a consistent focus on the evolving relationship between law and technology, with increasing attention to GDPR implementation, health data protection, and the challenges of algorithmic decision-making. His work spans multiple disciplines including legal theory, privacy studies, cyber security, and digital governance, reflecting the interdisciplinary nature of modern informatics law. Dr. Iglezakis has supervised over 50 master's theses at AUTH, mentoring the next generation of legal scholars in areas including cloud computing contracts, personal data protection, social media law, intellectual property in digital environments, and cybercrime. His administrative roles have included membership on the European Educational Programs Committee (2011-2012) and the Legal Committee of AUTH (2013-2014). His research projects include the 2018-2019 study on 'Legal Issues of Mobile Applications Development, Distribution and Use,' the 2014 '6th International Conference on Informatics Law,' and the 2013-2015 Center of Excellence for Cybercrime for Education, Research and Training in Greece. These projects highlight his commitment to addressing practical legal challenges in the digital domain while contributing to academic discourse.

Jovan StojkovicView profile

Assistant Professor

• Cloud Computing
• Datacenters
• Cloud-Native Workloads
• +7 more

Jovan Stojkovic is an incoming Assistant Professor at the Department of Computer Science at the University of Texas at Austin, set to join in Fall 2026. Prior to his appointment at UT Austin, he will spend a year at Meta working with the AI and Systems Co-design group. His research focuses on cloud computing and datacenters, with particular emphasis on cloud-native workloads and machine learning inference. Education: PhD in Computer Science from the University of Illinois at Urbana-Champaign, advised by Professor Josep Torrellas Undergraduate studies at the School of Electrical Engineering, University of Belgrade, Serbia, where he was recognized as the best student of the Computer Engineering and Information Theory Department every year from 2017-2020 Research Interests: Jovan's research focuses on cloud computing and datacenters , with two primary domains: Cloud-native workloads , such as microservices and serverless computing. He investigates how to co-design novel hardware platforms and software systems that deliver orders-of-magnitude improvements in performance, energy efficiency, and resource utilization for these emerging workloads. Machine Learning (ML) inference , particularly large language models (LLMs). His work addresses the challenges of ML inference through smart scheduling, workload placement, and system-level configuration tuning to reduce energy, power, and thermal overheads while maintaining performance and accuracy guarantees. Publication Trends: Jovan's publications demonstrate a strong focus on optimizing cloud infrastructure for emerging workloads. His research spans across serverless computing, microservices, and large language model inference. A clear trend emerges in his work: addressing the performance, energy efficiency, and resource utilization challenges of modern cloud workloads through innovative hardware-software co-design approaches. His most recent work shows increasing focus on LLM inference optimization, particularly in the areas of thermal management, power efficiency, and scheduling for many-adapter environments. Awards and Honors: HPCA Best Paper Award (2025) IEEE MICRO Top Picks Honorable Mention (2024) 6 patents with IBM and Microsoft on: Serverless systems, Processor overclocking in the cloud, and Energy-efficient LLM inference W. J. Poppelbaum Memorial Award (2025) for hardware and architecture innovation Mavis Future Faculty Fellowship (2024–2025) Invited to present at 11th Heidelberg Laureate Forum (2024) Kenichi Miura Award (2022) for excellence in High Performance Computing Multiple student travel grants to ISCA, MICRO, ASPLOS, and HPCA Advising and Grants: Jovan is actively seeking prospective PhD students for his research group at UT Austin. His research has been supported through collaborations with major tech companies including IBM, Microsoft, and Meta. His six patents with IBM and Microsoft demonstrate the practical impact of his research in serverless systems, processor overclocking, and energy-efficient LLM inference. His work on serverless computing (MXFaaS, EcoFaaS) and LLM inference optimization has received significant recognition in top-tier computer architecture conferences. Research Groups: During his PhD at UIUC, Jovan worked with Professor Josep Torrellas on cloud infrastructure research. He has collaborated extensively with researchers at IBM Research (particularly Hubertus Franke) and Microsoft (particularly Íñigo Goiri and Ricardo Bianchini). His upcoming position at UT Austin will establish his independent research group focused on cloud computing and datacenter systems. His year at Meta working with the AI and Systems Co-design group will further strengthen his expertise in AI infrastructure.

• Multibody dynamics
• Control of nonlinear systems
• Model predictive control

Michael Mühlebach is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zurich, affiliated with the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He received Bachelor (2010) and Master (2013) degrees from ETH Zurich, awarded the Outstanding D-MAVT Bachelor Award and Willi-Studer Prize for top performance. His research specializes in multibody dynamics , control of nonlinear systems , and model predictive control , with applications in robotics and aerospace systems. Publications demonstrate consistent focus on theoretical control frameworks validated through experimental platforms like ducted fans and reaction-wheel systems. Awards: Outstanding D-MAVT Bachelor Award Willi-Studer Prize (Best Master in Robotics, Systems, and Control)

Gerhard WeikumView profile

Researcher

• Computer Science
• Database Systems
• Information Retrieval
• +3 more

Gerhard Weikum is a renowned computer scientist and Director at the Max Planck Institute for Informatics in Saarbrücken, Germany. He holds the title of Scientific Member of the Max Planck Society. Previously, he served as a Professor at ETH Zurich (1990–1994) and Saarland University (1994–2003). His career includes roles as a Research Associate and Postdoc at TU Darmstadt, MCC in Austin, and ETH Zurich. Born in 1957, he earned his doctoral degree in computer science from TU Darmstadt in 1986. Education: Bachelor/Master studies in Computer Science at TU Darmstadt Doctoral Dissertation: TU Darmstadt (1986) His research interests span foundational areas of computer science, including database systems, information retrieval, and distributed computing. While specific publications are not listed here, his work aligns with the institute’s focus on cutting-edge informatics research. Grants and Advising: Details on grants, advising, or lab affiliations are not explicitly mentioned in the provided text. Labs/Teams: As a Director at the Max Planck Institute for Informatics, he likely contributes to interdisciplinary teams and research groups within the institute’s computational science initiatives.

• Data Science
• Data Management
• Big Data Analytics
• +3 more

Ioannis Theodoridis is a Professor at the Department of Informatics, University of Piraeus, and Director of the Data Science Laboratory under the School of Information and Communication Technologies. His research focuses on data science, particularly large-scale data management and analysis, with applications in maritime informatics, spatiotemporal data, and mobility patterns. He holds editorial roles at ACM Computing Surveys and has contributed to numerous international conferences and journals. As a project leader in Horizon 2020 initiatives, he has coordinated research on data-driven solutions for maritime safety and urban mobility. He earned his Diploma (1990) and PhD (1996) in Electrical and Computer Engineering from the National Technical University of Athens (NTUA). His work includes developing scalable systems for maritime route forecasting (e.g., GMSA), frameworks for vessel trajectory prediction (e.g., VesselVision), and platforms like i4sea for fisheries monitoring. Contact: ytheod@unipi.gr .

• Power Systems
• Renewable Energy
• Smart Grids
• +7 more

Dr. Aris Dimeas is a Researcher at the National Technical University of Athens in the Department of Electric Power and Industrial Applications . He holds a diploma and PhD in Electrical and Computer Engineering from NTUA and has extensive experience in power systems operations, renewable energy integration, and smart grid technologies. Specialized in AI applications for power systems Developed control software for demand side management Consultant for PPC (2007-2012) Research Focus : Smart grids and digital twin implementations Renewable energy market dynamics Microgrid optimization and control algorithms Collaborations : Active participant in EU research projects, collaborating with HEDNO and other energy grid operators on electronic meters and intelligent network deployments. Teaching : Instructs courses on electric energy systems, power system analysis, and energy management.

• Database Systems
• Distributed Systems
• Machine Learning Systems
• +3 more

Tim Kraska is an Associate Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) within the School of Engineering. His research spans foundational areas in modern data systems: Database Systems Distributed Systems Machine Learning Systems Learned Indexes Cloud Databases Dr. Kraska pioneers the integration of machine learning into core database components, developing "learned systems" that replace traditional algorithms with ML models for significant performance gains. His work on learned indexes redefined indexing paradigms, while recent research extends this approach to garbage collection, query optimization, and concurrency control. His publication trends reveal a strategic shift toward applying ML to low-level systems problems, particularly in memory management and runtime optimization as demonstrated by his PLDI 2020 paper on Learned Garbage Collection. Award highlights include: ACM SIGMOD Jim Gray Doctoral Dissertation Award VLDB Early Career Research Contribution Award Marie Curie Fellowship At MIT, he leads a research group focused on next-generation data systems, securing grants from NSF and industry partners to explore the theoretical and practical boundaries of learned components in database engines. His team collaborates extensively with industry research labs on real-world deployment challenges. As a core member of CSAIL's distributed systems group, he contributes to MIT's leadership in reimagining data infrastructure for the AI era through cross-lab initiatives on scalable machine learning systems.

Brandon LuciaView profile

Professor

• Intermittent Computing
• Energy-harvesting Systems
• Concurrency
• +7 more

Brandon Lucia is a Full Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He leads the abstract research group focusing on the intersection of computer architecture, systems, and programming languages. His work bridges theoretical foundations with practical implementations in energy-constrained environments. Lucia's research centers on intermittent computing systems and edge computing in extreme environments. His work on energy-harvesting systems has established fundamental principles for batteryless computing, while his orbital edge computing research pioneers computational intelligence for nanosatellite constellations. These research thrusts address critical challenges in reliability, efficiency, and programmability for systems operating under severe power constraints. His publication record shows a clear evolution from foundational work on intermittent computing models to sophisticated applications in space computing and edge intelligence. Recent publications demonstrate increasing integration of dataflow architectures with energy-harvesting constraints, particularly in satellite constellations where computational resources must be managed across distributed, power-constrained platforms operating in extreme environments. NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS Best Paper Awards (2018, 2020) OOPSLA Distinguished Paper and Artifact Awards (2015) Lucia actively mentors numerous PhD students including Brad Denby, Zhuo Cheng, and Emily Ruppel, many of whom contribute significantly to his research program. His abstract research group maintains strong industry connections while pursuing fundamental advances in computing systems. The group has developed multiple open-source tools including Legerdemain for program analysis and MultiCacheSim for cache coherence simulation. His laboratory work spans from theoretical foundations of intermittent computing to practical implementations in space systems. Current projects include computational nanosatellite constellations, energy-minimal dataflow architectures, and secure edge computing systems that operate reliably despite frequent power failures.