جست‌وجوی استاد و پژوهشگر

Randal Burnsمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Cloud Storage Systems
• +۴ مورد دیگر

Randal Burns is a Professor and the Bill and Lisa Stromberg Head of the Department of Computer Science in the Whiting School of Engineering at Johns Hopkins University. He is also affiliated with the Data Science and AI Institute and co-founder of NeuroData. His research focuses on scalable data systems for scientific applications, spanning storage technologies, cloud infrastructure, and graph/sparse-matrix engines for machine learning. Key themes include data-intensive science, neuroscience imaging analysis, and adaptive performance management. Recent publications highlight trends in edge-parallel graph encoders masked matrix operations for sparsity decentralized foundation model training storage optimization for evolving AI models batch-parallel data structures turbulence data analysis These reflect his work on scalable systems for machine learning and scientific computing. Scientific awards include NSF CAREER Award DOE Early Career Principal Investigator Kavli Fellowship IBM Outstanding Innovation Award Advising: Mentors PhD students in scalable data systems and machine learning. Grants: Supported by NSF, DOE, and DARPA initiatives. Labs: Core member of the Johns Hopkins Turbulence Database Group and NeuroData team, developing open platforms for neuroscience and turbulence research.

• Distributed Systems
• Database Systems
• Cloud Computing
• +۵ مورد دیگر

Joe Hellerstein serves as the Jim Gray Professor of Computer Science at the University of California, Berkeley and is Co-Director of the RISELab. He is also Founder and Chief Strategy Officer of Trifacta, demonstrating his strong bridge between academic research and industry application. His research interests focus on distributed systems , database technologies , and scalable data infrastructure . Hellerstein has made significant contributions to understanding distributed consistency through the CALM Theorem, developed high-performance database systems like Anna, and created open-source platforms including Clipper, Ground, and Ray through the RISELab. Analysis of his recent publications reveals a consistent theme of building practical systems that address fundamental challenges in data management at scale. His work spans theoretical foundations of distributed computing to production-ready open-source implementations, with particular emphasis on balancing performance, consistency, and scalability. Jim Gray Professorship (endowed chair) Hellerstein has mentored numerous researchers in the database and systems community, though specific student names aren't mentioned in the provided text. His work on the Postgres project as a graduate student demonstrates his long-standing engagement with foundational database technology. The RISELab under his co-direction has released multiple open-source systems that are actively used in both research and industry settings. His laboratory work focuses on building the next generation of data-intensive systems through the RISELab, with particular attention to real-time intelligent software environments that can harness diverse data sources efficiently.

• Machine Learning
• Computational Statistics
• Biomedicine
• +۴ مورد دیگر

Sach Mukherjee is a Professor and Group Leader at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany, affiliated with the University of Bonn. His research bridges computational statistics, machine learning, and biomedical science with a focus on neurodegenerative diseases. His work centers on developing high-dimensional statistical methods for predictive modeling, disease stratification, and systems-level analyses of complex biomedical data. Key research areas include building scalable molecular networks specific to biological contexts and integrative analyses of diverse high-dimensional datasets to advance understanding of neurodegenerative conditions. Prof. Mukherjee collaborates extensively across DZNE's fundamental, clinical, and population research divisions as well as with international partners to pioneer data-intensive approaches that transform next-generation biomedicine through computational innovation.

Alexander Michelsمشاهده پروفایل

استادیار

• Geospatial Information Science
• Spatial Data Science
• Spatial Modeling
• +۴ مورد دیگر

Alexander Michels serves as an Assistant Professor in the School of Economic, Political and Policy Sciences at The University of Texas at Dallas, where he focuses on applying geospatial data science and cyberGIS to address societal challenges. His research bridges advanced computational methods with practical implementations to solve real-world problems in disaster management, public health, and spatial accessibility analysis. Dr. Michels earned his PhD in Informatics and MS in Geography from the University of Illinois Urbana-Champaign in 2025 and 2024 respectively. His academic background combines spatial sciences with computational expertise, preparing him for interdisciplinary research at the intersection of geography, computer science, and data-intensive methods. His research program spans two primary domains: Spatial Data Science : Developing novel algorithms, software frameworks, and theoretical foundations to advance spatial analysis capabilities for real-world problem solving CyberGIS : Leveraging high-performance computing and cyberinfrastructure to overcome computational barriers in geospatial research Analysis of his publication trajectory reveals a strong emphasis on methodological innovation with immediate societal applications. Recent work demonstrates increasing sophistication in handling large-scale spatial problems, particularly through scalable algorithms for accessibility analysis and disaster vulnerability assessment. His research consistently addresses the challenge of making advanced geospatial computation accessible to domain scientists through open-source tools like the Vulnerable Neighborhood Explorer (VNE) and CyberGIS-Vis. Dr. Michels maintains active engagement with the research community through multiple professional platforms including Google Scholar, GitHub, ResearchGate, and ORCID. He is currently accepting undergraduate and graduate students for research collaboration, indicating an expanding research program. His professional activities include significant contributions to the CyberInfrastructure & Geospatial Information Laboratory (CIGI) and CyberGIS Center for Advanced Digital and Spatial Studies. His GitHub profile shows extensive collaboration with @cybergis and @I-GUIDE organizations, with 44 repositories and 32 starred projects reflecting his active role in geospatial open-source development. Notable projects include redesigning CyberGISX and CyberGIS-Jupyter for Water, and developing the WhereCOVID-19 Spatial Accessibility Explorer during the pandemic.

Xipeng Shenمشاهده پروفایل

استاد

• Programming Systems
• Machine Learning
• High-Performance Computing
• +۱۲ مورد دیگر

Xipeng Shen is a Professor in the Department of Computer Science at North Carolina State University and Director of the High-Performance Intelligent Computing (HiPIC) lab. He joined NC State in 2014 as part of the Chancellor's Faculty Excellence Program after serving as the Adina Allen Term Distinguished Associate Professor at the College of William and Mary. Shen's research spans programming systems and machine learning, with a focus on enabling extreme-scale, data-intensive, and intelligent computing through innovations in compilers, runtime systems, and ML algorithms. His work has significantly influenced the development of heterogeneous computing and modern AI systems. His research areas include High-Performance Machine Learning & Real-Time AI, Heterogeneous Massively Parallel Computing, and Foundations of Programming Systems & Languages. His recent publications demonstrate strong trends in optimizing AI deployment across various platforms including mobile devices, serverless environments, and edge computing systems, with particular emphasis on efficiency, power consumption, and real-time performance. His research increasingly integrates compiler techniques with machine learning approaches to solve complex optimization problems. ACM Distinguished Member (2018) University Faculty Scholar (2017-18) ACM Distinguished Speaker (2016-2019) IEEE Senior Member (2016) IBM CAS Faculty Research Fellow (2010-2016) Google Faculty Research Award (2015) DOE Early CAREER Award (2011) NSF CAREER Award (2010) Shen has successfully advised 15 PhD students who have gone on to positions at top universities and companies including UC Santa Cruz, UMass Amherst, Google, Meta, and Huawei. His research has been funded by numerous grants from NSF, DOE, and NIH totaling over $4 million. He co-founded CoCoPIE Inc. to commercialize his research on efficient AI deployment and serves as a consultant to major technology companies including Intel, Microsoft, Huawei, Cisco, and Meta. He leads the PICTure research group and teaches courses including Compiler Construction (CSC412/512), Code Optimization for Scalar and Parallel Programs (CSC766), and Real-time AI and Machine Learning Systems (CSC591/791).

• computer vision
• machine learning
• image processing
• +۲ مورد دیگر

Fabio Ayres serves as an Adjunct Professor at Insper, actively shaping engineering curriculum development with his dual expertise in academia and industry. His career bridges theoretical research and practical software engineering applications across global institutions. His academic credentials include: Bachelor's degree in Electrical Engineering from Escola Politécnica da USP Master's degree in Electrical Engineering from Escola Politécnica da USP Ph.D. in Electrical Engineering from the University of Calgary, Canada Research focuses on computationally intensive domains where machine learning intersects with visual data systems. His work in computer vision and high-performance computing targets efficient algorithm development for large-scale image processing and information retrieval, reflecting both biomedical engineering applications from his postdoctoral work and industrial scalability demands from his Google tenure. Prior appointments demonstrate a trajectory from academic research to tech industry leadership: Postdoctoral Fellow in Biomedical Engineering at the University of Calgary, Research Associate at Canada's National Research Council, and Software Engineer roles at Opal-RT Technologies and Google.

• Distributed Systems
• Information Retrieval Systems
• Knowledge Mining and Machine Learning
• +۳ مورد دیگر

Vasilios Tabakas is a Professor at the Department of Electrical and Computer Engineering, University of Peloponnese (based in Patras), where he also directs the Distributed Intelligent Systems and Data (DISyD Lab) . He holds a PhD in Computer Engineering and Informatics from the University of Patras, with a thesis focused on Operational and Distributed Systems. Research Interests: Distributed Systems, Information Retrieval, Knowledge Mining, Machine Learning, Big Data Management Teaching: Operating Systems, Distributed Systems, Data Mining, Business Intelligence at undergraduate level; Big Data Management and Advanced Data Mining at postgraduate level Leadership: Former Vice President of the Technical University of Patras (2005–2008), Department President, and Dean His research focuses on distributed systems control algorithms, information retrieval, and big data, with over 100 publications and 1800+ citations. He has led numerous R&D projects and authored three university textbooks. His lab investigates scalable solutions for distributed computing and data-intensive applications.

• Software Verification
• Cyber-Physical Systems
• Formal Methods
• +۶ مورد دیگر

Claudio Menghi serves as an Adjunct Assistant Professor in the Department of Computing and Software within McMaster University's Faculty of Engineering. His academic profile demonstrates a strong focus on bridging theoretical formal methods with practical software engineering challenges, particularly in safety-critical cyber-physical systems. Dr. Menghi's research spans multiple interconnected domains in software engineering and formal verification. His primary interests include model-based testing techniques for cyber-physical systems, assurance case development for safety-critical applications, and the practical application of formal methods in industrial settings. He has made significant contributions to trace checking methodologies that bridge the cyber-physical gap in verification processes. His work frequently addresses automotive systems, robotics, and medical devices, with numerous publications demonstrating his expertise in requirements validation, model checking, and specification patterns for complex systems. Dr. Menghi has been actively involved in the ARCH-COMP verification competition, contributing to the advancement of hybrid systems verification techniques through multiple category reports. His recent scholarly output shows an evolving research trajectory that now intersects with artificial intelligence, particularly exploring how large language models can enhance traditional software engineering tasks like change impact assessment. This represents a natural progression of his longstanding commitment to making verification techniques more accessible and practical for industrial applications. Dr. Menghi's teaching portfolio includes courses in software development and safe software-intensive medical devices, reflecting his dual expertise in foundational software engineering principles and specialized safety-critical applications.

Farhad Shokranehمشاهده پروفایل

پژوهشگر

• Health Informatics
• Biomedical Informatics
• Information Science

Dr. Farhad Shokraneh, PhD, serves as a Senior Research Associate in Information Science at the Bristol Medical School (Population Health Sciences) within the University of Bristol. His research-intensive role focuses on advancing health informatics through data-driven methodologies, contributing to the university's expertise in medical information systems and public health innovation from his base at Canynge Hall, 39 Whatley Road, Bristol BS8 2PS. His primary research interests encompass Health Informatics, Biomedical Informatics, and Information Science, with emphasis on applying computational analytics to healthcare delivery and population health management. This interdisciplinary work bridges computer science, medicine, and epidemiology to develop scalable solutions for complex health challenges, particularly in medical data systems and digital health interventions. Available for media engagement as a subject-matter expert via the University of Bristol's Press Office (+44 117 428 2489), Dr. Shokraneh maintains active academic collaborations and can be contacted directly at farhad.shokraneh@bristol.ac.uk for research inquiries and professional partnerships within the global health informatics community.

• Machine Learning
• Deep Learning
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Prof. Dr. Jasmina Bogojeska is a Professor for Artificial Intelligence and Machine Learning at ZHAW School of Engineering, where she leads the Explainable Artificial Intelligence Group since March 2024. She previously held roles as Senior Principal Data Scientist at Roche (2022-2024) and Research Staff Member at IBM Research Zurich (2013-2022), with postdoctoral work at Max Planck Institute for Informatics (2011-2013). Her affiliations include ZHAW Datalab and Digital Health Lab. PhD in Computer Science (2011), Saarland University/Max Planck Institute MSc in Computer Science (2007), Saarland University BSc in Computer Science (2004), University Ss. Cyril and Methodius Her research bridges Explainable AI with applications in Healthcare , IT Infrastructure Management , and Critical Care Time-Series Analysis . Key focus areas include domain-specific foundation models , conversational data systems , and low-resource NLP solutions . Recent work explores multi-modal medical AI for chest X-ray interpretation and large-scale clinical time-series datasets . Notable contributions include GIT-CXR for automated radiology reports and domain-specific protein language models in immunology. Her publications span IEEE, BMC, and Nature journals, emphasizing practical AI deployment in healthcare and IT operations. Scientific Recognition: Edelman Prize Finalist (2020) IBM Research Accomplishment Award (2019) IBM Corporate Award (2017) Best Paper Award at CNSM (2013) She actively develops reliable AI systems for server incident reduction (PASIR) and child injury monitoring, while advancing automated medical analytics through projects like Antibiotika-Resistenz Tracker. Her teaching covers Machine Learning , Data Mining , and Safer AI at bachelor and master levels.

• High Performance Computing
• Grid Computing
• Cloud Computing
• +۴ مورد دیگر

Giovanni Aloisio serves as full professor of Information Processing Systems at the University of Salento's Department of Innovation Engineering, where he leads the HPC laboratory. Concurrently, he directs the Supercomputing Center and Scientific Computing and Operations (SCO) Division at the Euro-Mediterranean Center on Climate Change (CMCC), holding key roles in CMCC's Governance bodies, Strategic Council, and Executive Committee. His research spans high performance computing, grid/cloud systems, and distributed data management with strong climate science applications. A co-founder of the European Grid Forum (Egrid), he has driven major EU initiatives including EGEE, IS-ENES1/2, and EESI/EESI2 projects while chairing the Weather, Climate and solid Earth Sciences (WCES) European Working Group. His work focuses on integrating HPC, big data, and machine learning for climate modeling and environmental analysis. Recent publications reveal a convergence of computational techniques addressing climate science challenges, featuring end-to-end workflows, climate data spaces in the European Open Science Cloud, and AI applications for tropical cyclone tracking and epidemiological modeling. The research demonstrates systematic integration of simulation, analytics, and machine learning across climate and public health domains. No specific scientific awards are documented in the source material. Professor Aloisio has secured substantial European research funding through leadership roles in critical infrastructure projects: EU-FP7 IS-ENES1/IS-ENES2 projects as CMCC responsible EU-FP7 EESI/EESI2 projects as ENES responsible Chair of WCES European Working Group Key expert in International Exascale Software Project (IESP) He operates at the intersection of academic and research institution leadership, directing both the University of Salento's HPC laboratory and CMCC's Supercomputing Center while collaborating with the ENES HPC Task Force and European Grid Initiative to advance computational climate science infrastructure.

• Internet of Things (IoT)
• Network Security
• Wireless Sensor Networks
• +۳ مورد دیگر

Dr. Francois Carrez is a Senior Research Fellow at the University of Surrey, focusing on Internet of Things (IoT), network security, and wireless communication systems. His work emphasizes IoT security through machine learning-based intrusion detection, IoT architecture design, and 6G network innovations. He contributes to projects like DEDICAT 6G for dynamic coverage extension and FIESTA-IoT for federated IoT infrastructure. His research spans IoT datasets, network intrusion detection systems (NIDS), and service-oriented architectures for distributed systems. Key technical areas include: IoT Security: Developing ensemble learning techniques for detecting IoT-specific attacks (e.g., Bot-IoT dataset analysis). Wireless Networks: Designing architectures for 5G/6G networks, dynamic scheduler management, and mobile web service distribution. Data Analytics: Real-time probabilistic fusion of IoT streams using Bayesian networks and adaptive machine learning. Semantic Interoperability: Creating ontologies and mediated gossiping mechanisms for large-scale sensor networks. He collaborates on EU-funded initiatives like DEDICAT 6G, exploring coverage extension via mobile access points and human-centric applications. His work bridges theoretical advancements with practical implementations in smart cities, transportation systems, and industrial IoT.

Xuhao Chenمشاهده پروفایل

استادیار

• High-Performance Computing
• Artificial Intelligence/Machine Learning
• Cybersecurity
• +۵ مورد دیگر

Xuhao Chen is an Assistant Professor in the Department of Computer Science and Engineering at the College of Engineering, Michigan State University. His research focuses on high-performance software systems and hardware architectures, with applications in AI/ML, cybersecurity, scientific computing, and finance. Prior to his current role, he held positions including Research Scientist at MIT CSAIL (working with Professors Charles Leiserson and Arvind), Research Fellow at the University of Texas at Austin, and Visiting Scholar at the University of Illinois Urbana-Champaign. Education: Ph.D. in Computer Science from National University of Defense Technology (2014). Research interests emphasize interdisciplinary systems research, blending hardware-software co-design principles. His work appears in top-tier venues such as ISCA, MICRO, VLDB, and OSDI. He explores scalable computing solutions for data-intensive applications across diverse domains. No specific grants or awards are listed in the profile. Advising information is not provided here. Previous affiliations include MIT CSAIL (201X-201X), UT Austin (201X-201X), and UIUC (201X-201X).

Y. Richard Yangمشاهده پروفایل

استاد

• Computer Networks
• Wireless Networking
• Mobile Computing
• +۲ مورد دیگر

Y. Richard Yang is a Professor of Computer Science and Electrical Engineering at Yale University, where he leads research in computer networks, wireless systems, and distributed computing. He is a core member of Yale's Computer Systems Lab and has pioneered Internet standards like ALTO (RFCs 7285/9439/9569). His work on massive MIMO (Argos) laid foundations for 5G cellular networks. Education: Ph.D. in Computer Science, University of Texas at Austin (2001) M.S. in Computer Science, University of Texas at Austin (1998) B.E. in Computer Science, Tsinghua University (1993) Research Interests: Yang's work spans network architecture (ALTO/P4P), wireless innovations (beamforming, superposition coding), and network verification (Flash, Trident). His recent focus includes deep application-network integration and scalable data plane verification for cloud/CERN-scale systems. Publication Trends: Recent articles emphasize SDN programmability (Maple, Trident), Internet-scale optimization (ALTO extensions), and high-speed verification (Flash). His works consistently bridge theory (game-theoretic models) with practical deployment (RFCs, CERN integration). Awards: ACM SIGMobile Test of Time Award (2022) ACM SIGCOMM NAI Best Paper Award (2022) NSF CAREER Award Facebook/Google/Microsoft Faculty Research Awards Students & Funding: Advised recent graduates including Jackie Dong and Lily Zhou. Secured grants from U.S. NSF, Army, U.K. MoD, Facebook, Google, and Microsoft. Labs: Directs projects in the Computer Systems Lab at Yale , collaborating with CERN, Microsoft Research, and Tsinghua/Peking Universities.

• Distributed Systems
• Cloud Computing
• Big Data Processing
• +۴ مورد دیگر

Ignacio Martín Llorente is a Full Professor (Catedrático) at the Universidad Complutense de Madrid (UCM), leading the Distributed Systems Architecture Research Group and the Data-intensive Cloud Lab . He holds visiting positions at Harvard University as a Visiting Professor in the John A. Paulson School of Engineering and Applied Sciences (SEAS) and as a Visiting Scholar in FAS Research Computing. He earned a Ph.D. in Computer Science from UCM and an Executive MBA from IE Business School. His research focuses on distributed systems, cloud computing architectures, big data processing, edge computing, and federated networking. He pioneered projects like OpenNebula (a cloud management system) and contributed to EU initiatives such as RESERVOIR (cloud virtualization), StratusLab (grid-to-cloud integration), and BEACON (federated networking). He has authored over 169 publications and serves on editorial boards for IEEE Transactions on Cloud Computing and Journal of Grid Computing . He has secured over €100M in EU grants, leading 10 major projects. His work emphasizes transferring research into open-source technologies and commercial products. He advises OpenNebula Systems and has been a consultant for governments and companies like Microsoft. His teaching spans 67 courses in distributed systems, cloud computing, and high-performance computing.