جست‌وجوی استاد و پژوهشگر

Panos Chrysanthisمشاهده پروفایل

استاد

• Big Data
• Database Management
• Data Stream Management
• +۵ مورد دیگر

Panos Chrysanthis is a Professor of Computer Science and Director of the Advanced Data Management Technologies (ADMT) Lab at the University of Pittsburgh. He holds adjunct roles at Carnegie Mellon University and the University of Cyprus. His academic journey includes a BS in Physics/Computer Science from the University of Athens (1982), and MS/PhD in Computer and Information Sciences from the University of Massachusetts Amherst (1986/1991). He joined Pitt in 1991 and has since been a key contributor to mobile data management, databases, and distributed systems. Research Interests: Big data, database systems, data stream management, workflow optimization, power-aware computing, and mobile/pervasive systems. His work emphasizes user-centric and energy-efficient data processing solutions. Awards: NSF CAREER Award (1995), Pitt Provost’s Mentoring Award (2015), ACM Distinguished Scientist, IEEE Senior Member. His contributions span editorial roles for VLDB Journal, IEEE TKDE, and DAPD. Teaching: Courses include database systems, distributed computing, and short courses on big data, stream management, and cloud computing. His pedagogical focus integrates research and industry trends. Labs/Teams: Directs the ADMT Lab, which explores big data analytics, IoT, and sustainability. Collaborates on projects like ASTRO (pandemic exposure reduction) and CAPRIO (urban mobility).

• Data Science
• Parallel Algorithms
• Graph Analytics
• +۳ مورد دیگر

David Bader is the Distinguished Professor of Data Science at New Jersey Institute of Technology (NJIT). He is a leading expert in high-performance computing, parallel algorithms, and large-scale graph analytics. His work focuses on developing scalable frameworks for big data problems, including graph processing, anomaly detection, and quantum computing methods. He has held continuous federal grants since 2000, with recent projects involving cyber-infrastructure for community detection, streaming data science frameworks, and gravitational wave research. Bader leads the development of open-source tools like Arachne and Arkouda, which are widely used for large-scale graph analytics. His research interests span parallel algorithms, graph theory, and high-performance computing applications. Notable contributions include the GraphBLAS initiative and the Einstein Toolkit. Bader was inducted into the Hall of Fame in 2025 for pioneering modern supercomputing innovations.

Bo Zhaoمشاهده پروفایل

استادیار

• Distributed Systems
• Machine Learning Systems
• Large-Scale Data Processing
• +۲ مورد دیگر

Bo Zhao is an Assistant Professor in the Department of Computer Science at Aalto University, leading the Aalto Data-Intensive System group (ADIS). His research focuses on building efficient data-intensive systems across multiple layers, from scalable machine learning systems to distributed data management systems. Research Focus: Scalable machine learning systems Distributed data processing Hardware-software co-design Quantum computing systems High-performance computing Current Projects: AthenaRL (scalable RL systems), LARA (quantum ML algorithms), FlexMoE (efficient mixture-of-experts systems). His group develops systems enabling ML deployment across hardware from quantum computers to mobile devices. Education & Career: PhD from Humboldt-Universität zu Berlin. Previously at Queen Mary University of London, Imperial College London, and Amazon Web Services. Research published in SOSP, VLDB, USENIX ATC.

Mohammad Siddiquiمشاهده پروفایل

استادیار

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

Dr. Mohammad Siddiqui is an Assistant Professor of Computer Science at the College of Engineering, West Texas A&M University. He previously served as a Lecturer at the University of North Carolina at Greensboro from 2018 to 2019. He holds a B.Tech in Computer Engineering (2005), an M.Tech in Software Engineering (2012), and a Ph.D. in Computer Science Engineering (2019) from Aligarh Muslim University and the University of Toledo, respectively. B.Tech in Computer Engineering, Aligarh Muslim University (2005) M.Tech in Software Engineering, Aligarh Muslim University (2012) Ph.D. in Computer Science Engineering, University of Toledo (2019) His research focuses on Multi-modal Human-Computer Interaction , Computer Vision , Deep Learning , and Augmented Reality . Current projects explore emotion-aware computing using deep learning to enhance user-device interactions. He also investigates immersive educational technologies, such as VR/AR tools for mathematics and engineering education. Dr. Siddiqui’s recent work spans VR applications for calculus visualization (2024), deep learning in remote sensing (2024), and multimodal emotion recognition frameworks (2020-2022). Earlier contributions include data stream management systems (2008-2012), genetic algorithm-based data fusion (2017), and cybersecurity applications like real-time fraud detection (2011). Labs/Teams: Human-Machine Teaming Laboratory (HMT Lab), Immersive Technologies Lab, and Robotics Playground Teaching includes courses on Operating Systems, Computer Architecture, Software Engineering, and Senior Capstone Projects. He actively contributes to the ECORE Lab and collaborates on the Water Working Group’s interdisciplinary projects.

Lauritz Thamsenمشاهده پروفایل

مدرس

• Sustainable Computing
• Carbon-Aware Systems
• Distributed Systems
• +۶ مورد دیگر

Dr. Lauritz Thamsen is a Lecturer in Computer Systems at the University of Glasgow's School of Computing Science, leading the Glasgow Carbon-Conscious Computing (GC3) lab. He specializes in sustainable computing systems, focusing on reducing the environmental footprint of data-intensive applications through carbon-aware resource management. His work spans distributed systems, cloud/edge computing, and energy-efficient infrastructure. He holds a PhD from TU Berlin and has collaborated with institutions like TU Berlin, Hasso Plattner Institute, and Humboldt University. Affiliations: GC3 Lab, Systems Section, Low-Carbon and Sustainable Computing Theme Key Research: Renewable energy integration, carbon-optimized scheduling, and adaptive resource allocation for big data analytics Recipient of the 2025 Early-Career Teaching Excellence Award and Best Paper Awards at ICWS'22 and PerCom'21. Active in organizing workshops like LOCO and TDIS, emphasizing interdisciplinary approaches to low-carbon computing. Current projects include the EPSRC-funded Casper project (collaborating with AWS, BBC, and HU Berlin) and the DFG-funded CRC FONDA. Supervises PhD students and leads research teams addressing energy efficiency in distributed systems.

Xiaowei Guمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Artificial Intelligence
• Data Analytics
• +۵ مورد دیگر

Dr. Xiaowei Gu is a Senior Lecturer at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering and the Computer Science Research Centre. He holds a PhD in Computer Science from Lancaster University (2018). Previously, he served as a Lecturer at the University of Kent, Aberystwyth University, and as a Senior Research Associate at Lancaster University. His research focuses on transparent machine learning models that balance interpretability and performance, particularly in semi-supervised and streaming data contexts. Key applications include disaster response via satellite imagery analysis and environmental monitoring. Notable projects include developing a human-trustable machine learning framework for rapid disaster response using satellite data. Teaching responsibilities include courses like Programming Fundamentals (COM1027) and AI & AI Programming (EEEM005). He actively supervises exceptional PhD candidates with strong research backgrounds in machine learning and data analytics. No scientific awards are explicitly listed, but his work emphasizes impactful, application-driven research. Publications span explainable AI, fuzzy systems, and deep learning applications in environmental and cybersecurity domains. His work often bridges theoretical advancements with real-world challenges, such as wildlife tracking and ocean temperature modeling.

Daniel de Lengمشاهده پروفایل

استادیار

• Applied Human-Centered AI
• Explainable AI
• Autonomous Systems
• +۵ مورد دیگر

Daniel de Leng is an Assistant Professor at Linköping University (Sweden), affiliated with the Department of Computer and Information Science (IDA) and the Human-Centered Systems (HCS) division. He is part of the Semantic Web group and focuses on applied human-centered AI, particularly in autonomous systems and explainable AI. His work integrates symbolic and subsymbolic methods to address challenges in real-world AI deployment. Education: PhD (2019) and Licentiate (2017) in Computer Science from Linköping University, and MSc/BSc (2013/2011) from Utrecht University (Netherlands). Research Interests: Stream reasoning under uncertainty, autonomous system safety, and semantic integration in robotics. He developed the DyKnow-ROS framework for robot stream reasoning and contributed to the Stellar AIOps environment for AI research. His work emphasizes human-understandable explanations for AI decisions. Awards: Recipient of the SAIS Master's Thesis Award. Advising & Grants: Managed the AI Academy initiative, part of the AIOps ELLIIT Infrastructure and AI Academy management teams. Previously served as Saab Aeronautics' first Point of Contact for AI (until 2022). Labs & Teams: Active in the Reasoning and Learning Lab (ReaL), Semantic Web group, and Human-Centered Systems division. Collaborates on projects like Urdarbrunnen (AI for combat search and rescue) and multi-agent robotics systems.

Juan Yeمشاهده پروفایل

عضو هیئت علمی

• Adaptive Pervasive Systems
• Sensor-Based Human Activity Recognition
• Sensor Fusion
• +۶ مورد دیگر

Dr Juan Ye is a Reader in the School of Computer Science at the University of St Andrews. He holds a PhD from University College Dublin and BSc/MSc degrees from Wuhan University. His research focuses on adaptive pervasive systems, sensor-based human activity recognition, and overcoming challenges in real-world sensor data analytics. He teaches modules on component technologies and machine learning, and supervises PhD students and research internships. His research addresses the limitations of classical machine learning by developing continual-learning frameworks for long-term, real-world applications. Key interests include sensor fusion, context awareness, ontologies, and uncertainty reasoning. He has contributed to projects on gaze tracking, animal density estimation, and robust gaze interaction methods. Dr Ye collaborates with interdisciplinary teams and has published extensively on topics like continual learning, gaze estimation, and ecological monitoring. His work aligns with UN Sustainable Development Goals through applications in environmental and assistive technologies.

• Network Inference
• Protein Design
• Bioinformatics
• +۵ مورد دیگر

Dennis E. Shasha is a Silver Professor of Computer Science at New York University (NYU), affiliated with the Courant Institute of Mathematical Sciences. He holds academic positions within the Faculty of Arts and Science and the Department of Computer Science, and serves as an Associate Director at NYU WIRELESS. His research spans computational biology, database systems, time series analysis, and privacy-preserving data management. Shasha has advised over 30 doctoral students and has authored numerous influential publications in top-tier journals and conferences. Education: Ph.D. in Applied Mathematics from Harvard University (1984), M.Sc. in Computer Science from Syracuse University (1980), and B.Sc. in Electrical Engineering from Yale University (1977). Research Interests: Network inference, protein design, bioinformatics, database systems, time series analysis, DNA computing, and puzzles. His work bridges computational methods with applications in biology, finance, and healthcare. Notable contributions include algorithms for subgraph matching, time series analysis tools like StatStream, and frameworks for network inference in molecular biology. Publications highlight interdisciplinary collaboration, with recent focus on causal inference, blockchain systems, and machine learning applications in finance and biology. Awards include ACM Fellow (2018), ACM Sigmod Contributions Award (2020), and recognition as a Senior Member of the U.S. National Academy of Inventors (2023). Advising and Grants: Supervised 34 Ph.D. students, many now leading roles in academia and industry. His grants include support from NSF, NIH, and industry partnerships with companies like Lucent and Bell Labs. Active in pro bono work, including database design for the Ellis Island Restoration Commission. Labs/Teams: Core member of NYU WIRELESS, involved in computational biology collaborations with NYU Medical School and the Simons Foundation. Leads research groups focused on algorithm design, data privacy, and systems biology.

Jan Riezebosمشاهده پروفایل

استاد

• Educational Innovation
• Operations Management
• Process Optimization
• +۴ مورد دیگر

Prof. Jan Riezebos is a Full Professor of Educational Innovation at the University of Groningen's Faculty of Economics and Business, holding the academic lead role at the Teaching Academy Groningen (TAG). He has been a professor since 2016 and previously served as Associate and Assistant Professor in Operations Management. His roles include chairing the Lean Management Network and the Educational Leadership Program (Groningen-Twente), alongside fellowships like Comenius Leadership. Education: PhD in Operations Research (2001, University of Groningen), with visiting scholar experience at Ball State University (1993). Key research areas include educational logistics, curriculum internationalization, and lean management applications in education. Projects include EU-funded initiatives like Learn2Inspire (social innovation leadership) and EQUNI (addressing discrimination in higher education). Research focuses on optimizing educational processes, reducing work stress via Value Stream Mapping, and improving teaching methodologies. He leads interdisciplinary collaborations through TAG, fostering innovation across faculties. Active in editorial roles for journals like International Journal of Quality and Reliability Management and Omega .

Georg Kremplمشاهده پروفایل

مدرس

• Active Learning
• Data Stream Mining
• Concept Drift
• +۲ مورد دیگر

Georg Krempl is Lecturer at Utrecht University's Department of Information and Computing Sciences. His research develops adaptive learning algorithms for data streams with concept drift and verification latency. He specializes in probabilistic active learning methods and statistical approaches to data stream mining. Research Focus : Krempl's work addresses challenges in evolving data environments, particularly developing robust classification under delayed label scenarios and concept drift. Recent projects include generative adversarial networks for domain adaptation and Bayesian methods for anticipatory classification. Professional Activities : Organizer of the Interactive Adaptive Learning workshop series (ECML PKDD) and editorial board member for multiple machine learning journals. His group focuses on both theoretical foundations and practical applications in data stream mining.

Yue Xuمشاهده پروفایل

دانشیار

• Data Analytics
• Machine Learning
• Knowledge Discovery
• +۶ مورد دیگر

Associate Professor Yue Xu is affiliated with the School of Computer Science at Queensland University of Technology (QUT). She obtained her PhD in 2000 from the University of New England and has held academic positions at Tasmania University (2000–2002) and QUT since 2002, where she was promoted to Associate Professor in 2011. Her research focuses on data analytics, machine learning, and knowledge discovery. Key areas include pattern mining in large datasets, anomaly detection, vulnerability analysis, topic modeling for text summarization, and social media analytics for business and security improvements. She has developed methods to enhance recommendation systems through multi-dimensional user profiling, context-aware recommendations, and semantic-based approaches. Expertise: Text mining, social engineering attack detection, distributed algorithms for big data Research Themes: Cybersecurity policy frameworks, healthcare prediction models, and interpretable machine learning techniques Prof. Xu has received prestigious awards including the Best Paper Award at WEBIST 2014 and iiWAS2012. She leads research projects such as Web Services Reputation Management (funded by Australian Competitive Grant LP0776400, 2007). Her teaching responsibilities include courses like CAB430 and IFN645, and she coordinates the Master of Data Analytics program. Her current PhD supervisions address challenges in industrial control systems vulnerability detection, hospital readmission prediction, senior frailty assessment, and semantic topic modeling. Completed supervisions include impactful works on healthcare e-coaching systems, spammer detection on Twitter, and reputation models for recommender systems.

• Self-Organizing AI
• Machine Learning
• Text Analytics
• +۳ مورد دیگر

Damminda Alahakoon is Professor of Business Analytics and AI at La Trobe University, where he founded and directs the Research Centre for Data Analytics and Cognition (CDAC). With dual appointments at Luleå University (Sweden) and University of Moratuwa (Sri Lanka), he bridges international AI research. His 15+ year academic career follows 10 years in industry, including roles in finance and IT across Australia, Sri Lanka, and the Netherlands. Research Impact: Developed the Growing Self-Organizing Map (GSOM) algorithm for adaptive AI systems applied in healthcare, utilities, and smart cities. Current projects include generative AI for healthcare chatbots and quantum optimization for energy systems. Industry Collaboration: Secured $6M+ in research funding for projects like AI-driven stroke rehabilitation monitoring and mining workforce analytics. Recent industry partnerships focus on operationalizing ethical AI frameworks in Australia-India tech corridors. Teaching: Leads the Master of Business Analytics program, specializing in predictive analytics curriculum development.

Konstantinos Nikitopoulosمشاهده پروفایل

استاد

• Advanced Physical Layer Processing Architectures
• Massively Parallel Algorithms for Communication Systems
• Non-Linear Signal Processing for 6G Systems and Beyond
• +۷ مورد دیگر

Konstantinos Nikitopoulos is a Professor at the Institute for Communication Systems, University of Surrey, and Director of the Wireless Systems Lab. He leads research in next-generation wireless communications, including Open RAN, non-linear processing, and neuromorphic computing. His work focuses on advancing MIMO systems, energy-efficient architectures, and practical implementations of 5G/6G standards. He has attracted over £11M in grants, including the HiPer-RAN and NL-COMM projects, and received awards for innovation and teaching. Education : PhD from National and Kapodistrian University of Athens Postdoctoral roles at RWTH Aachen University, UC Irvine, and UCL Research Interests : Non-linear signal processing for 6G systems Massively parallel algorithms and hardware acceleration Open RAN architectures and standard-compliant PHY design Unconventional computing (neuromorphic, quantum annealing) Awards : 2024 Innovator of the Year (University of Surrey) EPSRC First Grant (2021) Incremental Innovation Award (UK Department for Science, 2023) Grants & Projects : HiPer-RAN (£7.8M, DSIT) NL-COMM (£904K, Innovate UK) Tbps Communication System (£520K, Industry) Labs & Teams : Leads the Wireless Systems Lab and contributes to the 5G/6G Innovation Centre’s “Theory and Practice of Advanced Concepts” work area.

Constantinos Costaمشاهده پروفایل

استادیار

• Spatial Big Data
• Distributed Systems
• Mobile Computing
• +۲ مورد دیگر

Constantinos Costa is a Visiting Research Assistant Professor in the School of Computing and Information at the University of Pittsburgh. He directs research in spatial big data architectures, distributed systems, and mobile computing applications. He earned a PhD from the University of Cyprus and co-founded Rinnoco Ltd., a research innovation consultancy. His work integrates indoor-outdoor navigation, telco data analytics, and context-aware systems. He developed the CAPRIO path recommendation framework and ASTRO pandemic exposure tool, receiving best paper and demo awards at IEEE MDM 2021. Teaching: Created Pitt's first Ableton beat-making course and taught database systems, high-performance computing, and mobile applications.