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Daniel JunView profile

Professor

• Structural Analysis
• Structural Mechanics
• Finite Element Methods
• +5 more

Daniel Jun is a Professor of Structural Mechanics & Structural Analysis at Ruhr West University of Applied Sciences (Hochschule Ruhr West) in Mülheim an der Ruhr, Germany, where he also serves as Dean of Faculty 3 and Head of the Institute of Civil Engineering. With over a decade of experience in academia since joining the university in 2012, Professor Jun has established himself as a leading expert in structural engineering, particularly in the areas of cooling tower design, power plant construction, and reinforced concrete structures. Professor Jun's academic journey began at Ruhr-Universität Bochum, where he earned his Dipl.-Ing. (Master's degree) in Civil Engineering with a specialization in Structural Engineering in 1997. He continued his studies at the same institution, completing his Dr.-Ing. (PhD) in 2002 with a dissertation titled "Adaptive Simulationsstrategien für nichtlineare Finite-Element-Simulationen von Schalentragwerken" (Adaptive Simulation Strategies for Nonlinear Finite Element Simulations of Shell Structures). 2002: Dr.-Ing., Ruhr-Universität Bochum 1992-1997: Dipl.-Ing. in Civil Engineering, Ruhr-Universität Bochum Professor Jun's research spans structural analysis, structural mechanics, and computational methods with particular emphasis on cooling tower stability, power plant construction, and reinforced concrete structures. His recent work increasingly addresses sustainability aspects in construction, including carbon emission analysis. His publications consistently focus on practical engineering challenges, especially in power generation infrastructure, where he has contributed significantly to cooling tower design, maintenance, and lifecycle management standards. Over his career, Professor Jun has supervised numerous Bachelor's and Master's theses covering diverse topics from structural reinforcement techniques to AI applications in civil engineering. His professional activities include serving as a consulting engineer registered with Ingenieurkammer-Bau NRW and active participation in industry working groups. Member of the VDI advisory board "Structural Analysis" Deputy Chairman of the Structural Engineering Committee Consulting Engineer registered with Ingenieurkammer-Bau NRW Active participant in VGB Power Tech working groups Member of Bauforumstahl e.V. (Steel Construction Forum) Professor Jun teaches Structural Mechanics I & II, Structural Analysis I & II, and Numerical Methods in Engineering. His teaching combines theoretical foundations with practical applications, preparing students for real-world engineering challenges in construction and infrastructure development.

• adaptive systems
• altruism
• applied empirical methods
• +12 more

Nikos Askitas serves as Coordinator of Data and Technology at the Institute of Labor Economics (IZA) in Bonn, Germany, where he leads both the Research Data Center (IDSC) and ICT unit since joining in 2000. His dual role bridges technical infrastructure management and cutting-edge research in labor economics and social science. His research spans causal machine learning , epidemic disease modeling , big data analytics , and behavioral macroeconomics , with notable contributions to forecasting methodologies and web-based data applications. He develops novel approaches in opinion dynamics, game theory, and adaptive systems while addressing real-world challenges like pandemic response and technology-labor interactions. Recent publications reveal a strong trend toward AI-driven economic analysis , particularly examining generative AI's impact on scientific communication and labor markets. His work consistently integrates web data and real-time indicators with traditional economic modeling, emphasizing practical policy applications in consumption tracking, referendum forecasting, and mobility pattern analysis. His scientific recognition includes: CESifo Research Fellow in Economics of Digitisation Academic Editor for PLoS ONE (Economics section) As infrastructure leader, he oversees IZA's research data ecosystem and technological operations, enabling large-scale empirical studies while advancing methodological frontiers through hands-on machine learning pedagogy for social scientists. His current projects focus on GenAI implications, nowcasting techniques, and causal inference frameworks.

• Distributed Database Design
• Query Optimization
• Evolutionary Algorithms
• +8 more

Prof. Ahmet Cosar is a faculty member in the Department of Computer Engineering at the Middle East Technical University (METU), Ankara, Turkey. He holds a PhD in Computer Science from the University of Minnesota (1996), an MS in Computer Engineering from Bilkent University (1988), and a BS in Computer Engineering from METU (1986). His research focuses on distributed database design, query optimization, evolutionary algorithms, computer networks, and cloud computing. He leads the Intelligent Data Analysis Group (IDAG) and teaches courses such as CENG 240, CMPE 275, and CENG 280. Education: PhD, Computer Science, University of Minnesota, 1996 MS, Computer Engineering, Bilkent University, 1988 BS, Computer Engineering, METU, 1986 Research Interests: Query optimization in distributed databases Evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) Cloud computing and data warehouse design Machine learning applications in cybersecurity Wireless sensor networks and data fusion Publications: Over 50 peer-reviewed articles in journals like Computers & Industrial Engineering and Neurocomputing , focusing on optimization algorithms, cloud resource allocation, and machine learning techniques. Recent work includes island-parallel metaheuristics for graph coloring and reinforcement learning for adaptive interventions. Advising & Grants: Supervised over 30 graduate students (PhD and MS) in areas like evolutionary algorithms, cloud databases, and sensor networks. Active in research grants related to distributed systems and big data. Labs/Teams: Leads the Intelligent Data Analysis Group (IDAG), collaborating on projects in optimization, machine learning, and cloud computing.

Hui LiView profile

Assistant Professor

• Artificial Intelligence and Deep Learning
• Clinical and Translational Research
• Image Processing
• +3 more

Hui Li, Ph.D., is an Assistant Professor in the Department of Bioinformatics and Systems Medicine at the McWilliam School of Biomedical Informatics, UTHealth Houston. His academic role includes leading the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI). He holds a PhD in Computer Science from Beijing University of Technology (2009) and an MS in Computer Science from Jilin University (2004). Dr. Li’s research focuses on leveraging AI, big data analytics, and computational infrastructure to advance precision medicine, clinical decision-making, and biomedical informatics. Key areas include genomic data analysis, living systematic reviews for real-world evidence, automated clinical guideline updates via AI, and multi-modal patient data integration. His work emphasizes solution architecture, project management, and translational research applications. His publications span bioinformatics tools (e.g., VONC for genomic variant assessment), proteomic analysis techniques, and AI-driven approaches to protein interaction networks. Recent work addresses splice site detection in tumors, EGFR mutation analysis, and cancer pathway prediction using machine learning. Dr. Li’s expertise bridges computational methods and clinical practice, aiming to develop standardized protocols for genomic biomarker identification and improve diagnostic workflows through informatics solutions. His current projects include a learning precision medicine platform integrating AI with multi-source patient data for dynamic clinical decision support.

• Cloud Computing Reliability
• Storage Technology
• Distributed Systems
• +2 more

Haryadi S. Gunawi is an Associate Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group focusing on Availability, Reliability, and Efficiency in computing systems. He received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009 and was a postdoctoral fellow at UC Berkeley. His research improves dependability of storage and cloud computing systems in three contexts: performance stability, reliability/scalability, and ML-systems interactions. Education: Ph.D. Computer Science, University of Wisconsin-Madison (2009) B.S. Computer Engineering, University of Wisconsin-Madison Research focuses on building dependable systems that are robust to hardware variability ('limping hardware'), combating concurrency/scalability bugs in distributed systems, and exploring ML-storage system interactions. Current projects investigate latency tail tolerance, fail-slow hardware detection, and ML-enhanced storage systems. Publication trends show consistent work in systems reliability with recent expansion into ML-enhanced systems. The 15 most recent publications demonstrate progression from hardware failure analysis to ML-optimized storage systems, with consistent focus on real-world cloud system challenges. Awards and Honors: Provost's Global Faculty Award (2022, 2024) Best Paper Awards (SYSTOR 2022, FAST 2018) Facebook/Google/NetApp Faculty Awards NSF CAREER Award (2014) ACM Dissertation Honorable Mention (2009) Advising and Grants: Leads the UCARE research group with 15+ PhD students. PI on multiple NSF grants including the $5M ScaleStuds project for massive computation foundations. Manages collaborations with Chameleon testbed and CERES Center for Unstoppable Computing. Labs and Groups: UCARE Lab: Focuses on cloud/storage dependability Chameleon Project: Configurable cloud/edge research environment Systems Group: Collaborative systems research community CERES Center: Reducing computing system fragility

• Sustainable Orchestration in Edge-Cloud Environments
• Multi-Cloud Workload Distribution
• Large Language Models (LLMs) Deployment
• +3 more

Dr. Hadi Tabatabaee Malazi is an Assistant Professor at the University of College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). He holds an Orcid identifier (0000-0002-2960-6896) and has extensive academic experience across institutions like Maynooth University, Shahid Beheshti University, and Trinity College Dublin. His research focuses on sustainable computing, edge-cloud continuum orchestration, AI/ML deployment (e.g., LLMs), and energy efficiency. He is a Senior Member of IEEE, serves as an Associate Editor for IEEE Access, and chairs the COST Action CA22151 (CYPHER) on decarbonizing energy-intensive industries. Education: PhD (2012): University of Isfahan (Computer Engineering) MSc (prior to 2012): University of Isfahan Research Visit (2010-2011): Delft University of Technology Research Interests: Sustainable edge-cloud systems, AI-driven workload optimization, carbon-aware resource management, distributed LLMs, and IoT interoperability. Key contributions include over 20 journal articles in IEEE, Elsevier, and Springer venues, with topics ranging from vehicular network security to smart city event processing. Professional Activities: Committee member of COST Action CA22151, IEEE Access Associate Editor, and Technical Program Committee member for IEEE conferences. Authored certifications on research leadership and research integrity from Epigeum. Labs/Teams: SOC² Lab (UCD), leading projects on edge-cloud sustainability and AI application deployment.

Rahul TelangView profile

Professor

• Digital Media Industry
• Economics of Information Security and Privacy
• Copyright and Intellectual Property Policy
• +7 more

Rahul Telang is a Professor of Information Systems at Heinz College, Carnegie Mellon University, with a courtesy appointment at the Tepper School of Business. He has been at Heinz College since 2002 and is currently the director of the PhD program at the Heinz College. Professor Telang is also co-director of the Initiative for Digital Entertainment Analytics (IDEA) and part of Cylab and the Institute for Infrastructure Protection (I3P). Professor Telang's research broadly focuses on how Information and Communication Technologies (ICTs) and digitization impact consumers, businesses, and policies. His work spans two major domains: Digital Media Industry and Economics of Information Security and Privacy. In the Digital Media domain, he examines how digitization and piracy affect content providers, distributors, and users, with the aim of shaping optimal copyright and intellectual property policy. In the security domain, he investigates the incentives of various parties (users, firms, and hackers), market failures, and policy frameworks for information security. His recent research shows a strong focus on bug bounty programs, vulnerability disclosure economics, data breach impacts on consumer behavior, digital media consumption patterns, and the economic implications of AI on copyright policy. His work combines empirical analysis with economic modeling to provide actionable insights for both industry and policymakers, often using randomized field experiments and large-scale data analysis. Sloan Foundation Industry Study fellowship Multiple Google Faculty awards NSF CAREER award for work on economics of information security Senior editor positions at Management Science, Information Systems Research, and MIS Quarterly Professor Telang has secured extensive external funding from the National Science Foundation, National Security Agency, and industry partners for his research. He has worked extensively with industry and policymakers on media digitization issues, consulting with major entertainment companies like Disney and Warner Brothers on streaming strategies. As deputy director of the "living analytics" (LARC) project, he directs research on digital and social media analytics in collaboration with Singapore Management University. Professor Telang co-directs the IDEA center for digital entertainment analytics and contributes to Cylab's research on cybersecurity economics. His work on the Security Behavior Observatory has created infrastructure for long-term monitoring of client machines to understand security behavior, providing valuable insights into user security practices and vulnerabilities.

• Artificial Intelligence
• Machine Learning
• Quantum Computing
• +3 more

Ruchir Puri is Chief Scientist at IBM Research, an IBM Fellow, and Vice President of IBM's Technical Community. He holds adjunct professor roles at Columbia University and was a visiting scientist at Stanford University. His expertise spans AI, quantum computing, and semiconductor technologies. He led IBM Watson as CTO and Chief Architect (2016-2019) and has authored over 120 papers and holds 70+ patents. Education: Recipient of the Distinguished Alumnus Award from IIT Kanpur. His research focuses on AI-driven systems, code intelligence, and hardware acceleration. Notable contributions include the CodeNet dataset and Granite code models. Awards include IEEE Fellow status and the 2014 Asian American Engineer of the Year. Publications emphasize AI applications in quantum computing, code generation, and analog hardware. His work bridges theoretical research with practical implementations in enterprise systems. Grants and projects include leadership in IBM's AI and research divisions, with a focus on scalable AI solutions and hybrid cloud architectures. Key Labs/Teams: IBM Research - Yorktown Heights Current Roles: IBM Fellow, Corporate Technology Lead

• Machine Learning
• Randomized Algorithms
• Artificial Intelligence
• +3 more

Zhaozhuo Xu is an Assistant Professor of Computer Science at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. He joined Stevens in 2024 after earning his Ph.D. in Computer Science from Rice University. His research focuses on machine learning, randomized algorithms, and efficient computing for large-scale AI systems. Xu has been recognized with the AAAI 2025 New Faculty Highlights award. His work emphasizes scalable and sustainable AI, including compression techniques for large language models (LLMs), efficient inference methods, and ethical considerations in AI deployment. Xu has served as an Area Chair for the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) and is a member of IEEE and AAAI. Key research trends in Xu's articles include optimizing LLM efficiency through sparsity, quantization, and adaptive architectures; probing ethical boundaries in AI (e.g., copyright compliance); and developing multi-agent systems for financial decision-making. His contributions span theoretical foundations (e.g., randomized algorithms) and applied systems (e.g., on-device LLM execution). Awards: AAAI 2025 New Faculty Highlights Grants/Advising: No explicit grants listed; advising status unclear due to no student listings Labs/Teams: No dedicated lab/team named in provided texts

• Internet of Things
• Edge Computing
• Digital Twin Networks
• +7 more

Yan Zhang is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences, with a dual affiliation at Simula Research Laboratory in Oslo, Norway. Zhang leads cutting-edge research at the intersection of networking, artificial intelligence, and next-generation communication systems, with particular focus on digital twin networks, 6G technologies, and intelligent edge computing. Zhang's research interests span Internet of Things , Edge Computing , Digital Twin Networks , Wireless Communications , Vehicular Networks , and Federated Learning . Their work bridges theoretical foundations with practical implementations, addressing critical challenges in network architecture, resource optimization, and AI integration for future communication systems. Recent projects have focused on applying diffusion models to network optimization, developing secure federated learning mechanisms resistant to data poisoning, and creating energy-efficient solutions for maritime IoT networks. Analysis of Zhang's recent publication portfolio reveals a strong trend toward integrating artificial intelligence with next-generation networking infrastructure. The research demonstrates significant contributions to digital twin technology for 6G networks, with increasing focus on practical implementation challenges including location uncertainties, imperfect prediction conditions, and energy efficiency constraints. Zhang's work consistently addresses real-world deployment scenarios across multiple domains including intelligent transportation, maritime networks, and UAV swarms. Zhang has served as guest editor for special issues on Digital Twin for 6G Internet of Everything and Empowering Future Mobile Networks With Large Models, reflecting leadership in these emerging research areas. Current research directions include applying generative AI techniques like diffusion models to network optimization problems, developing robust security mechanisms for federated learning in IoT environments, and creating seamless service migration frameworks for mobile edge computing systems. Zhang's work frequently addresses the practical challenges of implementing theoretical concepts in real-world networking scenarios, with growing emphasis on energy efficiency and reliability under uncertain conditions.

Abdelmounaam RezguiView profile

Assistant Professor

• Big Data Management
• Cloud Computing
• Data Analytics
• +2 more

Abdelmounaam Rezgui is an Assistant Professor in the Department of Computer Science and Engineering at New Mexico Tech, where he directs the Cloud Computing and Big Data (C2BD) Lab. He holds a Ph.D. from Virginia Tech and specializes in developing efficient cloud computing solutions for big data applications. His research focuses on cloud computing optimization, big data management, and distributed systems design. Key areas include: Efficiency improvements for cloud platforms supporting big data workloads Fault-tolerant architectures for distributed systems Social computing and network analysis techniques Service-oriented computing paradigms Recent publications demonstrate strong emphasis on cloud infrastructure optimization, distributed computing frameworks, and AI-assisted system maintenance. Research consistently addresses practical challenges in large-scale data processing across federated environments. Awards and recognitions include: Best student paper award at IEEE ISCC 2016 Best paper award at ICICS 2016 He has advised multiple graduate students including 9 PhD candidates and 2 MS students, with research often supported through cloud computing infrastructure grants. As director of the C2BD Lab, he leads projects investigating volunteered federated clouds, hardware failure prediction systems, and efficient data processing frameworks. The lab collaborates with industry partners on practical cloud computing implementations.

Bram AdamsView profile

Associate Professor

• Release Engineering
• DevOps
• Software Analytics
• +5 more

Bram Adams is an Associate Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. He holds a Ph.D. in Computer Science Engineering from Ghent University (2008) and is an adjunct professor at Polytechnique Montreal. His research focuses on release engineering, software analytics, and human affect in software engineering, with contributions to build systems, software modularity, and green software practices. He leads the MCIS Lab, which studies software maintenance, construction, and intelligence. Research Interests: Release Engineering and DevOps Build Systems and Continuous Integration Software Analytics and Empirical Studies Human Affect in Software Engineering (sentiment/emotion analysis) Green Software and Energy Consumption Software Modularity and Concern Mining Awards: 2021 MSR Foundational Contribution Award Multiple Distinguished Paper and Best Paper Awards at ICSE, FSE, and MSR Most Influential Paper Awards for SCAM 2010 and AOSD 2009 Academic Service: General Chair of Mining Software Repositories (MSR) 2025 Editorial roles at IEEE TSE, Empirical Software Engineering, and Information and Software Technology Organizer of workshops on Software Health, Release Engineering, and Mining Software Repositories Labs/Teams: Heads the MCIS Lab at Queen's University, collaborating with researchers on release engineering, build systems, and software analytics.

Torsten HoeflerView profile

Professor

• High-Performance Computing
• Parallel Computing
• Computer Networks
• +3 more

Prof. Torsten Hoefler is a Full Professor at the Department of Computer Science, ETH Zürich. His research focuses on High-Performance Computing (HPC), parallel systems, networking, and AI-infrastructure. He leads projects on scalable interconnects, network topology design, and cloud computing benchmarks like SeBS. His work bridges theoretical foundations with practical implementations in distributed systems. Research Interests : High-Performance Computing & Networking Parallel Algorithms & Architectures AI Infrastructure & Distributed Systems Chiplet Interconnects & Topology Optimization Key Contributions : Developed tools like ATLAHS (AI/HPC network simulation) Advanced RDMA-based communication protocols (SDR-RDMA) Benchmarks for serverless computing (SeBS-Flow) His recent articles (2025) emphasize adaptive networks, low-precision AI models, and energy-efficient HPC systems. He also explores ethical computing and sustainability in supercomputing through initiatives like Core Hours & Carbon Credits.

Manfred HauswirthView profile

Professor

• Distributed Systems
• Internet of Things (IoT)
• Stream Processing
• +4 more

Prof. Manfred Hauswirth is the Managing Director of Fraunhofer Institute for Open Communication Systems (FOKUS) and holds the Chair for Open Distributed Systems at Technical University of Berlin. His research focuses on distributed systems, IoT, stream processing, quantum computing, and blockchain. He has held roles including Vice Director at Digital Enterprise Research Institute (DERI) and professor at National University of Ireland, Galway. He leads multiple strategic initiatives, including the Fraunhofer Quantum Technologies Research Field and the Weizenbaum Institute. His work bridges academia and industry, emphasizing digitalization, quantum computing, and IoT. Education: Dipl.-Ing. (1993), Dr. techn. (1999) in Computer Science from Vienna University of Technology. Postdoctoral work at École Polytechnique Fédérale de Lausanne (EPFL). Research Interests: Prof. Hauswirth’s work spans distributed systems, semantic web technologies, quantum algorithms, and IoT edge computing. He emphasizes real-world applications like smart cities, autonomous driving, and secure data management. Recent trends in his publications include quantum programming frameworks (e.g., Qrisp), scalable graph distillation, and edge-based AI systems. Awards: Not explicitly listed, but his work has been recognized through leadership roles in IEEE, ACM, and Fraunhofer committees. Advising & Grants: Active in funding initiatives like the Berlin Institute for Learning and Data (BIFOLD) and Einstein Center Digital Future (ECDF). Leads projects on quantum benchmarking, energy flexibility markets, and semantic stream processing. Labs/Teams: Directs the Fraunhofer High Performance Center for Digital Networking and chairs the Quantum Computing Competence Network, integrating interdisciplinary teams across quantum computing, IoT, and AI domains.

• Distributed Systems
• High-Performance Computing
• Fault Tolerance
• +5 more

Ozalp Babaoglu is a Full Professor at the Department of Computer Science and Engineering, University of Bologna. He has held this position since 1987, following a PhD in Computer Science from the University of California, Berkeley in 1981. His research focuses on distributed systems, High-Performance Computing (HPC), fault tolerance, self-organization, and machine learning applications in data-driven autonomics. He has led numerous European research projects like BROADCAST, CABERNET, ADAPT, BISON, and DELIS, contributing to foundational work in biology-inspired distributed algorithms and gossip-based systems. He co-founded the Bertinoro International Center for Informatics (BiCi) in 2001 and the IEEE SASO conference series in 2007. Babaoglu is a Fellow of the ACM (2002) and has received prestigious awards, including the Sakrison Memorial Award (1982) and the USENIX Lifetime Achievement Award (1993). His contributions to BSD Unix and open standards are seminal, including virtual memory extensions during his PhD. Babaoglu has served on editorial boards for ACM Transactions on Autonomous and Adaptive Systems, and previously for ACM Transactions on Computer Systems and Distributed Computing. His recent work addresses HPC fault classification, data-driven resource allocation, and energy-efficient computing in hybrid systems. He actively advises on European projects and explores cognitive paradigms in distributed systems.