جست‌وجوی استاد و پژوهشگر

• Anomaly Detection
• Topic Modeling
• Human-Computer Interaction
• +۳ مورد دیگر

Tove Helldin is a Senior Lecturer at the School of Informatics, University of Skövde, Sweden. Her research focuses on anomaly detection , topic modeling , and human-computer interaction , particularly in telecommunications networks and decision support systems . She has led projects in AI for climate adaptation and future sepsis diagnostics , emphasizing team effectiveness and trust calibration in automated systems. PhD in Computer Science (2014), University of Skövde Licentiate in Computer Science (2012) MSc in Computer Science (2009) Her scientific work spans interactive machine learning , visualization of causal relationships , and transparency in military threat evaluation . She has contributed to automotive UI design and fighter aircraft automation , with publications in venues like ACM Computing Surveys and IEEE conferences. Notably, her work explores topic modeling applications in network monitoring and interpretable AI frameworks.

Pradip K Srimaniمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• Interconnection Networks
• Self-Stabilizing Systems
• +۲ مورد دیگر

Pradip K Srimani is a Professor at the School of Computing , Clemson University , with research focus on Parallel and Distributed Computing , Self-Stabilizing Systems , and Graph Theory Applications . He is an IEEE Life Fellow and ACM Distinguished Scientist with over 250 publications. Research Interests Self-stabilizing algorithms for network graphs Ontology-based biomedical information retrieval High-performance computing resource management Biologically inspired distributed algorithms Network topology computation Recent publications demonstrate expertise in token circulation protocols, genome assembly frameworks, and semantic similarity measurement. He has served on program committees for APDCM , GPC-2018 , and IEEE BigMM conferences, while maintaining editorial roles at International Journal of High Performance Computing and Journal of Big Data . Scientific Awards IEEE Life Fellow ACM Distinguished Scientist

Nandini Dendukuriمشاهده پروفایل

دانشیار

• Biostatistics
• Diagnostic Test Accuracy
• Bayesian Inference
• +۵ مورد دیگر

Nandini Dendukuri, PhD is a Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) at the 5252 de Maisonneuve site. She holds dual affiliations with the Infectious Diseases and Immunity in Global Health Program and the Centre for Outcomes Research and Evaluation (CORE). Academically, she serves as an Associate Professor in the Department of Medicine, Faculty of Medicine and Health Sciences at McGill University, specifically within the Division of Clinical Epidemiology at the MUHC. Dr. Dendukuri's research program centers on the development of advanced statistical methodologies for diagnostic test accuracy evaluation in the absence of perfect reference standards. Her work spans Bayesian inference, latent class modeling, and meta-analysis techniques specifically designed for diagnostic accuracy studies. She has made seminal contributions to addressing methodological challenges such as conditional dependence between diagnostic tests, verification bias, and limitations of composite reference standards. Her research bridges theoretical statistical methodology with practical applications in infectious disease diagnostics, particularly tuberculosis where perfect reference tests are often unavailable. An analysis of Dr. Dendukuri's extensive publication record reveals consistent innovation in diagnostic test methodology with increasing sophistication in statistical modeling approaches. Her recent work (2024-2025) demonstrates expansion into diverse application areas including cardiovascular disease, emerging infectious diseases like COVID-19, and novel diagnostic technologies, while maintaining her core methodological focus. Her publications show particular strength in applying latent class models to complex diagnostic scenarios across multiple disease domains. Dr. Dendukuri actively collaborates with researchers across multiple disciplines, particularly in epidemiology, infectious diseases, and technology assessment. Her work with hospital-based technology assessment units has directly contributed to translating scientific evidence into healthcare policy. She maintains strong connections between statistical theory and practical healthcare applications, frequently developing software implementations (particularly rjags programs) of her methodological innovations to facilitate adoption by the research community.

Martina Chirilus-Brucknerمشاهده پروفایل

استادیار

• Nonlinear Partial Differential Equations
• Dynamical Systems
• Mathematical Analysis
• +۳ مورد دیگر

Martina Chirilus-Bruckner is an Assistant Professor in the Mathematical Institute at Leiden University, Faculty of Science, specializing in the analysis of applied nonlinear partial differential equations. She is a member of the Analysis and Dynamical Systems group and actively contributes to the mathematical community through her research and service roles. Dr. Chirilus-Bruckner received her PhD in Mathematics in 2009 from the University of Karlsruhe (now Karlsruhe Institute of Technology), Germany, following her Diploma in "Technomathematik" in 2006. Her academic journey includes postdoctoral and lecturer positions at Centrum Wiskunde & Informatica in Amsterdam, Boston University, Brown University, and Sydney University, establishing her international research profile. Her primary research focuses on nonlinear partial differential equations, dynamical systems, and their applications. She investigates pattern formation, wave propagation, stability of solutions, and bifurcation phenomena in various contexts including reaction-diffusion systems, nonlinear wave equations, and ecological models. Her work combines rigorous mathematical analysis with applications to physical and biological systems, demonstrating the power of mathematical modeling in understanding complex phenomena. Analysis of her recent publications reveals a consistent focus on nonlinear phenomena in spatially extended systems. Her research spans from theoretical investigations of fundamental equations like the Swift-Hohenberg, Klein-Gordon, and reaction-diffusion systems to applications in ecological pattern formation. A notable trend is her work on validity of approximation methods, particularly modulation equations like the Nonlinear Schrödinger and Korteweg-de Vries equations for describing complex wave dynamics. Dr. Chirilus-Bruckner is actively involved in the mathematical community as a member of the Board of NDNS+ (Nonlinear Dynamics and Natural Systems Plus) NWO-Cluster of Mathematics and serves on the Education Committee of the Mathematical Institute at Leiden University. She is organizing academic events including an upcoming Autumn School on "From microscopic dynamics to continuum limits," demonstrating her commitment to advancing research and education in her field. Her research collaborations span multiple institutions and countries, reflecting the international nature of modern mathematical research. She frequently collaborates with researchers from the Netherlands, Germany, and the United States, contributing to a vibrant network of scholars working on nonlinear phenomena.

Teague Henryمشاهده پروفایل

استادیار

• Network Science
• Quantitative Psychology
• Neuroscience
• +۷ مورد دیگر

Teague Henry is an Assistant Professor at the University of Virginia with joint appointments in the Department of Psychology (Quantitative Psychology program) and the School of Data Science . His work bridges network science, dynamical systems modeling, and Bayesian estimation to analyze complex data in neuroscience, clinical science, and natural language processing. Methodological expertise: Graph theory, exponential random graph models, functional neuroimaging Substantive applications: Autism, ADHD, semantic networks, social media topic analysis His research spans functional connectivity, network psychometrics, and language networks, with a focus on cross-disciplinary similarities in network data. Recent publications emphasize time-series modeling, imputation methods, and neurodevelopmental disorders. Software development: R packages like gimme and netjack Future student advising: Plans to recruit graduate students starting 2022-2023

• Computational Mathematics
• Inverse Scattering
• Wave Propagation
• +۲ مورد دیگر

Alexander Mamonov is an Associate Professor in the Department of Mathematics at the University of Houston's College of Natural Sciences and Mathematics. He holds a Ph.D. in Computational and Applied Mathematics from Rice University and a Diploma with Distinction from Lomonosov Moscow State University. His research focuses on computational mathematics, inverse problems, wave propagation, and model order reduction. His research interests span computational mathematics, inverse scattering, wave propagation, model order reduction, and numerical linear algebra, with applications in geophysical imaging and scientific computing. Mamonov's publications demonstrate sustained focus on inverse problems and model reduction techniques, with recent work advancing tensor-based methods for parametric systems and data-driven approaches for waveform inversion. His articles consistently integrate mathematical theory with computational applications in wave propagation and imaging. Mamonov has received the ICERM Research Fellowship and maintains active research collaborations, including visiting positions at the University of Coimbra and ICERM. He coordinates the Seminar on Computational Mathematics in Oil & Gas Exploration and Imaging.

Michael Gruningerمشاهده پروفایل

استاد

• Ontologies
• Semantic Integration
• Process Modelling
• +۶ مورد دیگر

Michael Gruninger is a Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto, serving as Associate Chair of Undergraduate Studies. He holds a PhD and MSc in Computer Science from the University of Toronto and a BSc in Computer Science from the University of Alberta. His research focuses on semantic integration, process modeling, and mathematical logic applications in manufacturing and enterprise engineering. He contributed to the ISO 18629 standard for Process Specification Language. Research interests include ontologies, semantic web technologies, knowledge representation, and formal methods. He leads the Semantic Technologies Laboratory, advancing theories in mereotopology, spatiotemporal ontologies, and ontology engineering. Recent work emphasizes automated spatial reasoning in robotics and standards-based ontology development. Publications span ontology validation, mereological foundations, and applied semantic technologies. His work bridges theoretical computer science with practical enterprise systems and smart city applications. No awards are explicitly listed, though his contributions to ISO standards reflect industry impact. Advising and grants: No specific students/grants detailed here. His lab focuses on semantic technologies with applications in manufacturing and urban systems. Collaborations include NIST and the Industrial Ontologies Foundry.

• Construction Engineering and Management
• Data Analytics
• Artificial Intelligence (AI)
• +۶ مورد دیگر

Professor Nora El-Gohary is a faculty member in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. She holds the title of CEE Excellence Faculty Fellow and has held academic positions including Assistant Professor (2009–2017), Associate Professor (2017–2023), and full Professor (2023–present). Her professional experience includes roles at the University of Manitoba and Hyundai Engineering & Construction Co. Ltd. Education: B.Sc. Construction Engineering, American University in Cairo (1999) M.Sc. Construction Engineering, American University in Cairo (2002) Ph.D. Civil Engineering, University of Toronto (2008) Research focuses on data analytics, AI, and BIM for sustainable infrastructure. Key areas include: Automated compliance checking using NLP Machine learning for energy consumption prediction Human-centered systems for construction management Awards include the NSF CAREER Award (2013), NCSA Fellow (2018), and 2025 ASCE Computing in Civil Engineering Award. She has led over 100 research projects funded by agencies like NSF and Illinois Department of Transportation. Editorial roles include Co-Editor-in-Chief of the ASCE Journal of Computing in Civil Engineering (2024–present) and Associate Editor (2012–2023). Active in professional societies such as TRB and ASCE.

Andrew McGregorمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Data Streams
• +۵ مورد دیگر

Andrew McGregor is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. He is a core member of the Theory Group and directs the TRIPODS Institute for Theoretical Foundations of Data Science. His research focuses on algorithms for massive data sets, data streams, and information theory, with applications in clustering, approximation algorithms, and coding theory. Education: PhD, Computer Science, University of Pennsylvania (2007) MEng, Computer Science, University of Pennsylvania (2002) BA, Mathematics, University of Cambridge (2000) Research Interests: Design and analysis of algorithms for data streams and massive datasets Clustering algorithms and approximation techniques Coding theory and information-theoretic approaches Algorithmic fairness and combinatorial optimization Recent Article Trends: Advances in streaming algorithms for dynamic graphs and parameterized complexity Graph reconstruction and allocation problems under noisy conditions Quantum communication and scheduling policies for satellite networks Entropy estimation and probabilistic methods in data analysis Awards: NSF CAREER Award (2010) ACM PODS Test-of-Time Award (2020) Lilly Teaching Fellowship (2012) Outstanding Teacher Award (2016) Grants & Labs: Director, TRIPODS Institute for Theoretical Foundations of Data Science Collaborations with Microsoft Research and UCSD's Information Theory & Applications Center Active in Center for Data Science and Theoretical Computer Science Group Future Work: Expanding research on quantum algorithms, scalable causal inference, and fair data diversification techniques.

Michael Shahمشاهده پروفایل

مدرس ارشد

• Computer Science Education
• Software Visualization Tools
• Performance Analysis
• +۳ مورد دیگر

Michael Shah is a Senior Lecturer of Computer Science at Yale University's School of Engineering & Applied Science. His research focuses on software visualization tools, performance analysis frameworks, and innovative computing education methodologies. He has contributed to fields such as parallel computing, GPU execution visualization, and asynchronous programming patterns. Dr. Shah's work bridges technical software development with educational applications, including curriculum design for introductory graphics courses and teaching assistant training programs. His research emphasizes practical tool development for debugging and performance optimization in both academic and industry contexts. Notable projects include the Daisen GPU visualization framework, DrAsync for JavaScript anti-pattern detection, and the Iceberg static analysis tool for Java concurrency issues. His publications span topics from procedural content generation in games to runtime performance bug mitigation strategies.

• Visual Search
• Visual Attention
• Methodology
• +۳ مورد دیگر

Johan Hulleman is a Senior Lecturer at the University of Manchester's Division of Psychology Communication and Human Neuroscience. His research focuses on Visual Search, Visual Attention, and Methodology. He holds a PhD in Experimental Psychology from the University of Nijmegen (The Netherlands) and a BSc in Medical Biology from the University of Utrecht. Education: PhD in Experimental Psychology, University of Nijmegen BSc in Medical Biology, University of Utrecht Research interests include exploring how visual attention operates in complex tasks, error mechanisms in search processes, and the application of transcranial electrical stimulation (tES) to enhance cognitive performance. His work contributes to the UN Sustainable Development Goals by advancing understanding of neurological conditions like neurofibromatosis type 1 through electrophysiological studies. Recent articles highlight stochastic error analysis in visual search, cross-cultural reading direction effects, and meta-analyses of tES applications. Collaborations include projects with the University of Liverpool and Vrije Universiteit Amsterdam. He co-led the 'Application of Novel Techniques to Enhance Cognitive Performance' project (2020–2023), focusing on transcranial electrical stimulation. Teaching includes courses on psychological statistics and conceptual issues in psychology.

Bruno Sinopoliمشاهده پروفایل

استاد

• Cyber-Physical Systems (CPS)
• Networked Control Systems
• Smart Infrastructures
• +۷ مورد دیگر

Bruno Sinopoli is a Professor in the Electrical and Computer Engineering Department at Carnegie Mellon University, with courtesy appointments in the Robotics Institute and Mechanical Engineering Department. He holds affiliations with ICES, CyLab, and the Pittsburgh Digital Studio (PDL), and co-directs the Smart Infrastructure Institute (SII). His research focuses on cyber-physical systems, networked control systems, and resilient infrastructure, addressing challenges in smart grids, wireless sensor networks, and cybersecurity. Education: Ph.D. (2005), M.S. (2003), and Laurea (1998) in Electrical Engineering from UC Berkeley, Università di Padova, and University of California, Berkeley, respectively. Notable awards include the George Tallman Ladd Research Award (2010), NSF Career Award (2010), and Eliahu Jury Award (2006). Research Interests include networked embedded control systems, cybersecurity for critical infrastructure, and smart city technologies. He has led projects funded by NSF, NIST, and industry, such as a $3.2M NIST grant for first responder communication systems and NSF CRISP grants for infrastructure resilience. Key contributions include frameworks for secure control systems, resilient sensor networks, and adaptive video streaming algorithms. His work bridges theoretical control systems with practical applications in energy, transportation, and urban infrastructure. Students supervised include doctoral candidates like Dragana Bajovic (A.G. Milnes Award winner) and Yilin Mo, as well as master’s students in localization and control systems. Active collaborations span robotics, mechanical engineering, and interdisciplinary initiatives in smart infrastructure.

Enyue Annie Luمشاهده پروفایل

استاد

• High Performance Computing
• Computer and Communication Networks
• Algorithm Design and Analysis
• +۵ مورد دیگر

Dr. Enyue (Annie) Lu is a Professor of Computer Science at Salisbury University, affiliated with the Department of Computer Science within the Richard A. Henson School of Science & Technology. She holds a Ph.D. from the University of Texas at Dallas. Her research focuses on High Performance Computing, including parallel and distributed processing, cloud computing, and GPU computing. She also explores Computer and Communication Networks, such as network security and wireless sensor networks, alongside Algorithm Design and Graph Theory applications in bioinformatics and medical imaging. Dr. Lu has led and contributed to numerous research projects, including the REU (Research Experience for Undergraduates) program, which emphasizes emerging computing in science and engineering. Her work spans interdisciplinary areas like medical imaging algorithms and stable matching problems, leveraging GPU acceleration and parallel computing techniques. She has advised over 20 students in these areas and collaborates with institutions like the University of Maryland Eastern Shore and Johns Hopkins University. Her publications emphasize practical applications of theoretical computer science, such as improving tomographic imaging and network intrusion detection through machine learning and distributed systems. Despite not listing explicit awards, her extensive contributions to undergraduate research and algorithm development highlight her scholarly impact.

Neave O'Cleryمشاهده پروفایل

دانشیار

• Data Science
• Urban Systems
• Economic Development
• +۴ مورد دیگر

Neave O'Clery is an Associate Professor at the Centre for Advanced Spatial Analysis (CASA) at University College London (UCL), leading a research group focused on data-driven models for economic development and urban systems. She holds additional roles as Visiting Professor, Research Fellow, and Lecturer. Previously, she was a Senior Research Fellow at the University of Oxford’s Mathematical Institute (2016–19) and a Fulbright Scholar/Postdoctoral Fellow at Harvard Kennedy School’s Center for International Development (2013–16). She earned a PhD in mathematics from Imperial College London (2009–13). Education: PhD in Mathematics, Imperial College London (2009–2013) Bachelor’s/Master’s degrees (implied) Research Interests: O'Clery’s work bridges data science, urban systems, and economic development. She explores network dynamics, industrial agglomeration, and policy-relevant applications of complex systems theory. Her methodologies combine mathematical modeling with real-world socioeconomic analysis. Publications: Recent work spans AI’s societal impacts, network prediction models, and industrial development theories, reflecting her interdisciplinary approach. Awards: Fulbright Scholar (2013–2016) Advising & Grants: Founder and Editor-in-Chief of Angle journal (since 2009), focusing on policy-science intersections. Active in grant-funded research at UCL, Oxford, and Harvard. Labs/Teams: Leads the economic development research group at CASA, collaborating with interdisciplinary teams on urban and industrial systems modeling.

Xin Xiaمشاهده پروفایل

پژوهشگر ارشد

• Recommender Systems
• Machine Learning
• Graph Neural Networks
• +۳ مورد دیگر

Dr. Xin Xia is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advancing machine learning techniques, particularly in the domain of recommender systems, with an emphasis on on-device deployment, graph-based learning, and self-supervised methods. He has contributed to optimizing recommendation systems for edge computing environments and improving their efficiency in resource-constrained settings. Key research areas include: On-device recommendation systems and edge AI Graph neural networks for recommendation Self-supervised learning and contrastive techniques Efficient model optimization and knowledge distillation His publications analyze trends such as transitioning cloud-based systems to on-device applications, reducing communication overhead in distributed models, and simplifying graph contrastive learning frameworks. These contributions highlight his expertise in bridging theoretical advancements with practical, deployable solutions. Dr. Xia’s work has been presented at top-tier conferences such as SIGIR, CIKM, and AAAI, reflecting the academic rigor and industry relevance of his research.