جست‌وجوی استاد و پژوهشگر

Panagiotis Bourosمشاهده پروفایل

استادیار

• Data Management
• Query Processing
• Spatial Data
• +۵ مورد دیگر

Panagiotis (Panos) Bouros is an Assistant Professor at the Institute of Computer Science, Johannes Gutenberg University Mainz (JGU), Germany, and founder of JGU's Data Management research group. He holds a PhD (2011) and Diploma (2003) in Computer Science/Engineering from the National Technical University of Athens (NTUA). His research focuses on data management, query processing for non-traditional data types (spatial, temporal, graphs), and parallel computing. Prior to JGU, he worked at Aarhus University, Humboldt-Universität zu Berlin, and the University of Hong Kong. Key roles include teaching Database Systems courses at JGU since 2018, supervising over 40 students, and leading projects on in-memory databases and spatial data analytics. Notable achievements include a Best Paper Award (2015 SSTD) and publications in top venues like IEEE TKDE and ACM SIGMOD. He actively reviews for VLDBJ, ACM TODS, and organizes workshops like LocalRec and GeoRich. Research interests span database systems, spatial/temporal data, and algorithm optimization. His work addresses challenges in indexing, query processing, and scalable data management for modern hardware. Current projects include temporal information retrieval and geosocial network analysis.

Panagiotis Stamatopoulosمشاهده پروفایل

استادیار

• Artificial Intelligence
• Constraint Programming
• Logic Programming
• +۹ مورد دیگر

Panagiotis Stamatopoulos is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, where he has been employed since 1993. He holds a PhD in Computer Science (1988) and a Diploma in Physics (1982) from the University of Athens. His research spans artificial intelligence, constraint programming, natural language processing, machine learning, and optimization. Specific interests include: Hybrid approaches combining constraint programming with operations research Natural language understanding for database access Parallel processing and distributed constraint solving Multi-agent systems and web intelligence applications His publications show consistent focus on constraint satisfaction algorithms, text summarization techniques, educational timetabling systems, and AI applications in diverse domains like sports analytics and robotics. Recent works demonstrate increased attention to NLP evaluation metrics and multimodal learning. He has supervised numerous diploma theses and led projects funded by the European Union (EDS, APPLAUSE, PARACHUTE, PARROT), University of Athens, and Olympic Airways. Stamatopoulos teaches undergraduate courses in Introduction to Programming and Logic Programming, plus postgraduate courses in Advanced Artificial Intelligence. He previously taught Artificial Intelligence, System Programming, and Expert Systems.

Daniel Weiskopfمشاهده پروفایل

استاد

• Scientific Visualization
• Eye Tracking
• Human-Computer Interaction
• +۵ مورد دیگر

Prof. Daniel Weiskopf is a Professor at the University of Stuttgart's Faculty of Computer Science, Electrical Engineering and Information Technology, affiliated with the Institute of Parallel and Distributed Systems. His research focuses on visualization techniques, eye tracking, and human-computer interaction, with applications in virtual/augmented reality (VR/AR), data analysis, and uncertainty modeling. He has contributed to advancements in scientific visualization, including ML-driven flow visualization, energy-efficient rendering, and gaze-aware interfaces. His work emphasizes empirical methodologies, such as eye-tracking studies for evaluating visualization literacy and collaborative learning in AR environments. Research interests include: Scientific Visualization and Uncertainty Representation Eye Tracking Methodologies and Human Factors VR/AR Systems and Immersive Analytics Machine Learning Applications in Visualization Recent publications highlight trends in optimizing visualization performance, improving data interpretation through gaze-aware systems, and integrating advanced visualization into gaming and engineering contexts. His work bridges computational methods with user-centric design principles. He leads efforts in the Institute of Parallel and Distributed Systems , collaborating on tools like Gazealytics for exploratory gaze analysis and mint for VR visualization integration. No awards or grants are explicitly listed in the provided information.

Michael Berryمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Text Mining
• +۴ مورد دیگر

Michael Berry is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. He holds a PhD in Computer Science from the University of Illinois at Urbana-Champaign, an MS in Applied Mathematics from North Carolina State University, and a BS in Mathematics from the University of Georgia. His research focuses on data science, machine learning, text mining, nonnegative matrix factorization, parallel computing, and their applications in biomedical and environmental domains. Notable contributions include work on tensor decomposition for big data analysis, algorithms for text mining, and computational tools like PolyLens and CodeAssessor. Berry's publications emphasize interdisciplinary applications, such as using nonnegative tensor factorization for biomedical literature analysis and developing GPU-accelerated methods for traffic flow analysis. His work spans conferences like the International Conference on Soft Computing in Data Science (SCDS) and journals in computational science. He has contributed to software tools like FutureLens for text visualization and SHEPPACK for interpolation algorithms. His research also addresses environmental challenges via parallel ecosystem modeling and spatial control problems. No scientific awards or grants are explicitly mentioned in the provided text.

Ulrike Stegeمشاهده پروفایل

دانشیار

• Computational Biology
• Parameterized Complexity
• Design of Heuristics
• +۴ مورد دیگر

Ulrike Stege is an Associate Professor and Director of the Master of Engineering in Applied Data Science (MADS) at the University of Victoria's Faculty of Engineering and Computer Science. She holds a PhD from the Swiss Federal Institute of Technology (ETH Zurich). Her research spans computational biology, parameterized complexity, algorithm design, graph theory, and cognitive psychology. She leads initiatives in quantum computing frameworks and educational tools, including projects like SCOOP (quantum optimization) and QGrover (quantum algorithm visualization). Her work also addresses RNA pseudoknot structure prediction and the integration of quantum computing into combinatorial optimization software. Key educational contributions include developing browser-based quantum learning tools (e.g., QNotation and QuantumCrypto) and promoting computational thinking in K-12 education. Her research bridges theoretical computer science with practical applications in biology and quantum systems, emphasizing algorithmic innovation and interdisciplinary collaboration. Her publications focus on advancing quantum computing frameworks, optimizing bioinformatics algorithms, and creating accessible educational resources. Recent work addresses quantum annealing for constrained optimization, structural biochemistry of viral RNA, and hybrid quantum-classical problem-solving methods.

Dar-Jen Changمشاهده پروفایل

دانشیار

• Computer Science
• Parallel Computing
• GPU Programming
• +۷ مورد دیگر

Dar-Jen Chang is an Associate Professor in the Department of Computer Science and Engineering at the University of Louisville. He holds a B.S. in Mathematics from National Tsing Hua University (1970), an M.S. in Computer Information/Control Engineering from the University of Michigan (1982), and a Ph.D. in Mathematics from the same institution (1982). His research focuses on parallel computing, GPU programming, bioinformatics, 3D modeling, and algorithm optimization. His work spans disciplines including computer science, data mining, and biomedical applications. Chang’s research interests emphasize leveraging GPU acceleration for computational tasks such as RNA folding algorithms, hierarchical clustering, and Euclidean distance calculations. He has explored applications in 3D anatomical modeling, robotics simulation using Unity, and gaming frameworks with CUDA integration. His contributions include developing frameworks for algorithm visualization and database systems for automated process planning. His publications highlight trends in GPU-optimized algorithms, graph database analysis (e.g., Yelp and IMDb datasets), and interdisciplinary applications like medical imaging and genetic sequence prediction. He has also contributed to neural networks for fuzzy logic systems and biological sequence mining.

Tzu-Yi Chenمشاهده پروفایل

استاد

• Network/Graph Analysis
• Combinatorial Scientific Computing
• High Performance Computing
• +۳ مورد دیگر

Tzu-Yi Chen is a Professor and Chair of the Computer Science Department at Pomona College since 2002. His research focuses on network/graph analysis, combinatorial scientific computing, and societally-relevant computer science education. He holds a Ph.D. and M.S. from UC Berkeley, and a B.S. in Computer Science and Mathematics from MIT. Education: Ph.D./M.S., Computer Science, UC Berkeley B.S., Computer Science & Mathematics, MIT Research interests include developing efficient algorithms for complex networks, high performance computing, and improving CS education accessibility. Recent work addresses network recovery in power systems, evacuation modeling, and community detection in complex networks. He has been recognized with awards including the NCWIT Undergraduate Research Mentoring Award (2019) and the Wig Distinguished Professorship (2017). Teaching includes Algorithms, Discrete Mathematics, and introductory CS courses. He mentors students through programs like Peer Mentoring in STEM and has advised 15+ undergraduate researchers. Current projects explore computational methods for societal challenges and inclusive CS education strategies.

Andrew Bloch-Hansenمشاهده پروفایل

استادیار

• Approximation Algorithms
• Efficient Algorithms
• Data Structures
• +۴ مورد دیگر

Andrew Bloch-Hansen is an Assistant Professor in the Department of Mathematics & Computing at Mount Royal University. He joined the department in 2024 and teaches courses on algorithms and data structures. His research focuses on designing efficient approximation algorithms for NP-hard optimization problems, particularly in network and packing optimization. PhD in Theoretical Computer Science, Western University (2024) MSc in Theoretical Computer Science, Western University (2019) BSc in Computer Science, Western University (2017) His research interests include approximation algorithms, combinatorial optimization, and efficient algorithm design. Notable areas of focus include the thief orienteering problem on series-parallel graphs and high-multiplicity strip packing. His work has been supported by an NSERC grant. Recent publications explore algorithmic advancements in graph theory, packing optimization, and combinatorial problems. Andrew emphasizes practical implementations of theoretical results, balancing computational efficiency with solution quality. He has no listed scientific awards but has contributed to significant research in approximation algorithms. His advising and grant activities are currently unspecified. Andrew is affiliated with Mount Royal University's mathematics and computing programs, contributing to both education and cutting-edge research in theoretical computer science.

Guangdong Baiمشاهده پروفایل

دانشیار

• Trustworthy AI
• Cyber Security
• Software Engineering
• +۵ مورد دیگر

Associate Professor Guangdong Bai is affiliated with The University of Queensland's School of Electrical Engineering and Computer Science, specifically within the Discipline of Cyber Security and Software Engineering. He holds the academic rank of Associate Professor and is currently on leave. His research focuses on Trustworthy AI, Cyber Security, Software Engineering, and Formal Methods, with notable contributions to privacy compliance, IoT/ICS security, and adversarial machine learning defense mechanisms. Education includes a PhD from the National University of Singapore and undergraduate/master's degrees from Peking University. His work spans theoretical advancements and practical applications, including blockchain-based systems for EV charging and vaccine tracking, adversarial attack frameworks (e.g., GenDroid), and federated learning security. Research interests emphasize AI ethics, privacy-preserving techniques, and securing emerging technologies like autonomous vehicles and smart agriculture networks. Notable projects include AuSSE (autonomous vehicle security framework), CoreLocker (neural network usage control), and PAODING (neural network pruning toolkit). Publications (15 most recent) reflect expertise in adversarial machine learning, federated learning, and IoT security. His work bridges theory and practice, addressing real-world challenges in critical infrastructure and decentralized systems. Labs/teams include collaborations on Cyber Security and Software Engineering at UQ, with supervision of PhD candidates such as Yanjun Zhang (Deakin University) and Kailong Wang (Huazhong University of Science and Technology).

Michael Huangمشاهده پروفایل

استاد

• High-Performance Computer Architecture
• Processor Microarchitecture
• Non-Von Neumann Computing
• +۴ مورد دیگر

Michael Huang is a Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Rochester's Hajim School of Engineering & Applied Sciences. He holds a PhD from the University of Illinois at Urbana-Champaign (2002) and has been faculty since 2002. His research focuses on high-performance computing, including processor microarchitecture, energy-efficient design, and non-von Neumann systems like Ising machines. He has received the NSF CAREER Award and is a member of ISCA/HPCA Hall of Fame. Education: BS (Tsinghua University, 1994), MS/PhD (UIUC, 1999/2002). Research emphasizes co-design of device, circuit, and system technologies. Notable projects include multi-chip Ising machine architectures and optical interconnects. Collaborates with IBM Research on future processor concepts. Active in top conferences like ISCA, HPCA, and ICLR. Key achievements include pioneering work on Ising machines for combinatorial optimization, energy-efficient architectures, and secure branch predictors. His work bridges traditional computer architecture with emerging technologies like optics and mixed-signal circuits.

Alex Olshevskyمشاهده پروفایل

دانشیار

• Distributed Optimization
• Machine Learning
• Control of Networks
• +۵ مورد دیگر

Alex Olshevsky is an Associate Professor of Electrical Engineering at Boston University. He holds affiliations with the Department of Electrical and Computer Engineering, the Division of Systems Engineering, and the Department of Computer Science (as an affiliate). His research focuses on distributed optimization, machine learning, control of networks, multi-agent systems, and reinforcement learning. He earned his BS from Georgia Tech and MS/PhD from MIT. Education: BS, Georgia Institute of Technology MS, Massachusetts Institute of Technology PhD, Massachusetts Institute of Technology Research interests emphasize theoretical foundations and applications of distributed algorithms, with a focus on optimization, learning, and control in networked systems. Key areas include reinforcement learning for control systems, distributed machine learning, and network-based epidemic control. His work bridges systems theory, optimization, and machine learning to address challenges in multi-agent coordination and large-scale data-driven systems. Notable contributions include advances in distributed stochastic gradient methods, temporal difference learning, and adversarial crowdsourcing. His research has been published in top venues like IEEE Transactions on Automatic Control, Journal of Machine Learning Research, and NeurIPS/ICML. Advising and grants include mentoring students now at institutions like Hong Kong University of Science and Technology and LinkedIn. His grants include NSF-funded projects on federated learning for energy consumption prediction and reinforcement learning algorithm design. Current roles include leadership in Boston University's systems engineering and computer science programs. He co-founded read.haus , leveraging machine learning for personalized book recommendations.

Ruth Hoffmannمشاهده پروفایل

مدرس

• Computational Combinatorics
• Graph Theory
• Permutation Classes
• +۴ مورد دیگر

Dr. Ruth Hoffmann is a Lecturer at the University of St Andrews, affiliated with the School of Computer Science and the St Andrews GAP Centre. Her research focuses on computational combinatorics, graph theory, permutation classes, computational group theory, and formal verification. She has contributed to improving combinatorial search algorithms for graph and group problems, including subgraph isomorphism and stabiliser chains. Research Interests Engineering efficient algorithms for graph and group problems Constraint programming applications in permutation enumeration and puzzle solving Formal verification of autonomous systems and memory models Combinatorial search optimization and algorithm design Teaching Current modules: Foundations of Computation (CS2001), Programming with Data (CS1003) Past roles: Overseer of BSc Data Science Graduate Apprenticeship and instructor for Computational Complexity (CS3052) and Information Security Management (IS5104) Recent Research Trends Dr. Hoffmann’s recent work emphasizes constraint modeling innovations, including permutation enumeration frameworks and automated puzzle-solving explanations. Her publications address formal verification challenges in autonomous systems and memory consistency protocols, alongside combinatorial search advancements for graph problems. Collaborative projects include EPSRC-funded efforts to enhance group search techniques using graph-based methods. Grants & Projects Enhancing Group Search with Graph Techniques (EPSRC, 2023–2025) C3+: Constraint-based Collaboration (EPSRC, 2018) Labs & Teams Active contributor to the St Andrews GAP Centre, focusing on computational algebra and combinatorics.

Jinyang Liمشاهده پروفایل

استاد

• Distributed systems
• Operating systems
• Machine learning systems
• +۳ مورد دیگر

Jinyang Li is a Professor of Computer Science at New York University's Department of Computer Science, part of the Courant Institute of Mathematical Sciences. His research focuses on distributed systems, machine learning systems, operating systems, and wireless networks. He holds a Ph.D. from MIT (2005) and a B.S. from the National University of Singapore (1998). He co-leads the NYU Systems Group and NYU WIRELESS. Key research interests include distributed training, fault tolerance, and parallel computing. Recent projects include Grendel (distributed 3D Gaussian Splatting) and Ares (memory-efficient GNN training). Awards: Distinguished Artifact Award (ASPLOS 2023), Best Paper Award (APsys'17) Teaching: Machine Learning Systems (Fall 2025), Computer Systems Organization (Spring 2026) Labs: NYU Systems Group, NYU WIRELESS Publications span topics like distributed protocols, database optimization, and deep learning systems, with a focus on scalability and efficiency. He advises students in collaboration with Prof. Panda, including Ding Ding, Haitian Jiang, and others.

Monika Henzingerمشاهده پروفایل

استاد

• Combinatorial Optimization
• Dynamic Optimization
• Parallel and Distributed Optimization
• +۵ مورد دیگر

Monika Henzinger is a Full Professor of Computer Science at the Institute of Science and Technology Austria (IST Austria) and Deputy Speaker of the Vienna Graduate School on Computational Optimization. She holds a PhD from Princeton University and has held positions at Cornell University, Digital Equipment Corporation, Google, EPFL, and the University of Vienna. Her research focuses on combinatorial algorithms, dynamic optimization, and efficient graph algorithms. She leads a group at IST Austria exploring algorithm design for dynamic environments, privacy-preserving algorithms, and practical implementations of theoretical results. Research Interests: Combinatorial algorithms (especially graphs), dynamic algorithms, approximation algorithms, algorithmic game theory, and privacy-preserving computation. Her work includes breakthroughs in decremental graph algorithms, submodular optimization, and computational advertising. Collaborations: Works with Vladimir Kolmogorov, Nysret Musliu, Günther Raidl (Combinatorial Optimization), Birgit Rudloff (Dynamic Optimization), and Dan Alistarh (Parallel/Distributed Optimization). Awards: Wittgenstein Award (2021), ERC Advanced Grants (2021, 2014), ACM Fellow (2016), and numerous others listed in her CV. Grants & Labs: Principal investigator on an ERC Advanced Grant for graph algorithms. Her team includes PhD students (e.g., Bardiya Aryanfard, Antoine El-Hayek) and postdocs focused on algorithmic challenges in dynamic systems.

John Langeمشاهده پروفایل

دانشیار

• High Performance Computing
• Operating Systems
• Networking
• +۵ مورد دیگر

Dr. John "Jack" Lange is an Associate Professor (on leave at Oak Ridge National Laboratory) in the Department of Computer Science at the University of Pittsburgh. He holds a PhD from Northwestern University (2010) and dual B.S. degrees in Computer Engineering and Computer Science from the same institution (2003). His research focuses on High Performance Computing, Operating Systems, Virtualization, and Networking, with a current emphasis on specialized operating systems for supercomputing environments. Education: PhD in Computer Science, Northwestern University, 2010 MS in Computer Science, Northwestern University, 2006 B.S. in Computer Engineering and B.S. in Computer Science, Northwestern University, 2003 Research Interests: High Performance Computing and Operating Systems Networking, Virtualization, and Distributed Systems Specialized OS design for supercomputing environments Recent Contributions: His work includes contributions to system software for exascale computing (e.g., Hobbes OS/R, Palacios VMM, and Kitten LWK), as well as research on virtualization, resource management, and secure access control in distributed systems. Recent publications emphasize network optimization, fault isolation, and scalable system design for leadership-class supercomputers. Awards: Departmental Teaching Award (2013, 2014) Symantec Graduate Research Fellowship (2007–2008) Advising & Grants: Advised multiple PhD and MS students, including Nicholas Gordon, Judicael Djoko-Takougue, Brian Kocoloski, and Jianan Ouyang. Secured significant grants from NSF and DOE, including collaborative projects on privacy controls, resource management, and exascale system software. Professional Roles: Affiliated with the Institute for Cyber Law, Policy, and Security Senior Systems Scientist at Oak Ridge National Laboratory (2021–present) Served in leadership roles at HPDC, TPDS, and other conferences