جست‌وجوی استاد و پژوهشگر

Rob Jamiesonمشاهده پروفایل

استاد

• Ecological Engineering
• Contaminant Hydrology
• Wastewater Treatment
• +۴ مورد دیگر

Dr. Rob Jamieson is a Full Professor in the Civil and Resources Engineering Department at Dalhousie University, holding a Tier 2 Canada Research Chair in Cold Regions Ecological Engineering. He leads the NSERC CREATE ASPIRE program, focusing on aquatic ecosystem restoration. His research spans ecological engineering, contaminant hydrology, and cold regions infrastructure. Dr. Jamieson co-chairs the CSA Technical Sub-Committee for northern wastewater standards and advises multiple industry and government bodies. Education: Ph.D. (Water Resources Engineering), University of Guelph, 2004 MASc. (Biological Engineering), Dalhousie University, 2000 B.Eng. (Biological Engineering), Technical University of Nova Scotia, 1998 Research Interests: Dr. Jamieson’s work addresses challenges in wastewater treatment for Arctic communities, climate change impacts on hydrological systems, and microbial risk mitigation in cold regions. He develops innovative solutions for cold regions engineering, including geotextile-based treatment systems and watershed modeling under climate change scenarios. Awards: Tier 2 Canada Research Chair in Cold Regions Ecological Engineering Advising & Grants: Supervises graduate students in watershed hydrology and environmental microbiology. Leads research projects funded by NSERC, including the Nunavut Wastewater Treatment Program and ASPIRE’s industrial training initiatives. Collaborates on multi-institutional efforts to address northern water infrastructure challenges. Labs & Teams: Active in Dalhousie’s Water Studies group, leading fieldwork in northern Canada and coastal Nova Scotia. Engages with industry partners through the Cold Regions Infrastructure Lab and the Arctic Wastewater Research Network.

Josh Tenenbaumمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Cognitive Science
• +۶ مورد دیگر

Josh Tenenbaum is a Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT). He leads the Computational Cognitive Science group, dedicated to understanding how humans learn and reason, particularly focusing on the statistical and computational principles underlying human cognition. His work bridges cognitive science, machine learning, and artificial intelligence, emphasizing Bayesian models and probabilistic reasoning. Tenenbaum's research explores topics like concept learning, causal inference, intuitive physics, and language acquisition, with implications for both human cognitive development and AI systems. His research integrates computational models with behavioral experiments, aiming to explain how people generalize from limited data and form abstract theories of the world. Notable contributions include foundational work on probabilistic models of cognition, the development of the Isomap algorithm for nonlinear dimensionality reduction, and advancements in cognitive science through computational frameworks. Tenenbaum collaborates with students and researchers across disciplines, including neuroscience, computer science, and linguistics. He emphasizes interdisciplinary approaches to address challenges in understanding human intelligence and building more capable AI systems. His group's work is published in top journals like Science and Proceedings of the National Academy of Sciences , and he regularly presents at conferences such as NIPS and Cognitive Science Society meetings. Key themes in his research include the role of prior knowledge in learning, the computational basis of intuitive theories, and the development of algorithms that mimic human-like reasoning. Tenenbaum is also involved in educational initiatives, advising students pursuing PhDs in Brain and Cognitive Sciences or Electrical Engineering and Computer Science at MIT.

Yu Luمشاهده پروفایل

استادیار

• Video Coding
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Yu Lu is an Assistant Professor in the Department of Biological & Ecological Engineering at Oregon State University. Their research focuses on video coding, image processing, and algorithm optimization with applications in multimedia systems and computer vision. Current interests include fast video compression algorithms, salient object detection, and error resilience in 3D video coding. Yu Lu has published extensively on topics such as HEVC/SHVC intra/inter coding optimizations, sparse regularization techniques for image restoration, and Bayesian decision models for scalable video coding. Research highlights include developing multi-stage motion estimation algorithms for versatile video coding (VVC), fast intra-coding methods using online learning, and depth map error concealment strategies for 3D video. Their work bridges algorithmic efficiency and practical multimedia applications, with publications spanning both traditional video coding standards (HEVC) and emerging formats like screen content coding. Publications trends show a strong emphasis on optimizing video encoding processes through hierarchical classification, adaptive switching mechanisms, and edge-assisted mode decisions. Earlier work (2013) explored piezoelectric material analysis, indicating interdisciplinary research interests. No awards or grants are explicitly listed in the provided materials.

Thomas Giambellucaمشاهده پروفایل

استاد

• Climate variability analysis
• Land surface-atmosphere interactions
• Ecohydrology
• +۶ مورد دیگر

Thomas Giambelluca is Professor in the Department of Geography at University of Hawaiʻi at Mānoa, specializing in climate-ecosystem interactions. His research spans: Climate variability (past changes and future projections) Land surface-atmosphere exchanges (water, carbon, energy) Tropical montane cloud forest hydrology Impact of vegetation change on climate systems Key projects include: Ecohydrology Lab's flux tower networks in Hawai‘i and Southeast Asia Rainfall Atlas of Hawaiʻi mapping 1978-2007 patterns Evapotranspiration mapping projects funded through Army Corps of Engineers HaleNet climate monitoring network on Maui Collaborations span: National University of Singapore Nagoya University Pacific Islands Climate Science Center NOAA and NSF EPSCoR grants

• Predictive Maintenance
• Interactive Machine Learning
• Causal Inference
• +۳ مورد دیگر

Sepideh Pashami is an Associate Professor in Machine Learning at Halmstad University's School of Information Technology and a Senior Lecturer at the Center for Applied Intelligent Systems Research . She also serves as a Senior Researcher at RISE Research Institutes of Sweden . PhD in Machine Learning from Örebro University (2016) Technology Area Leader for Aware Intelligent Systems since 2020 Research Interests focus on predictive maintenance, interactive machine learning, causal inference, and representation learning. Her work addresses challenges in: Automotive fault diagnosis Survival analysis for time-to-event prediction Domain adaptation with limited data Explainable AI for industrial systems Feature selection optimization Graph neural networks for anomaly detection Scientific Contributions span 15 recent publications covering: Predictive maintenance for hybrid buses and heavy-duty vehicles Explainable AI frameworks (SurvSHAP, knowledge-based systems) Domain adaptation with geometric constraints Concordance index optimization in survival analysis Feature selection using genetic algorithms Multi-stream regression for industrial data Industry Collaboration includes extensive work with Volvo Group AB on projects like FREEPORT, EVE, In4Uptime, ARISE, and HEALTH. She has served on organizing committees for leading conferences: ECAI 2023, DSAA 2021, ECML/PKDD 2019-2022, and IJCAI-ECAI 2018 industry days.

Yu Wangمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Machine Learning
• +۷ مورد دیگر

Yu Wang serves as an Assistant Professor in the Department of Computer and Information Science at the University of Oregon's School of Computer and Data Sciences, where he leads research in graph-based machine learning and AI systems. Joining the faculty in September 2024 after completing his PhD at Vanderbilt University, he directs the Graph Machine Learning Lab and actively recruits PhD students for cutting-edge projects at the intersection of data mining, network analysis, and trustworthy AI. His educational background includes a Ph.D. in Computer Science from Vanderbilt University (2019-2024) and a B.S. from Harbin Institute of Technology (2015-2019). During his doctoral studies, he completed research internships at Adobe Research and The Home Depot, focusing on knowledge graph applications for information retrieval. Wang's research centers on graph machine learning, with emphasis on neural-symbolic learning, LLM integration with structured knowledge, and trustworthy AI systems. His work addresses critical challenges in imbalanced/biased graph learning, generative graph models, and social network analysis, driving applications in cyber-security, biochemistry, and infrastructure systems. Current projects explore agentic social simulations and spatial-temporal machine learning for real-world impact. His publication trajectory shows increasing focus on LLM-graph integration and trustworthy AI, with recent work spanning generative graph models, personalized LLMs, and robust networking systems. Key trends include bridging symbolic reasoning with neural approaches, advancing evaluation benchmarks for social network analysis, and developing privacy-preserving techniques for graph-based retrieval systems. Outstanding Doctoral Student Award (Vanderbilt, 2023-2024) Best Paper Award at NeurIPS GLFrontiers Workshop (2023) Vanderbilt Graduate Leadership Anchor Award for Research (2023) KDD Outstanding Dissertation Award Honorable Mention (2025) NSF IIS-III Core Program Grant as Lead PI (2025) Top-10 Most Influential Papers at CIKM'22 and WWW'23 Wang mentors PhD students including Yongjia Lei (SDM Doctoral Forum Honorable Mention recipient) and Riya (Pulse Research Fellow), with research funded through NSF grants and industrial collaborations with Adobe, Visa, and Home Depot. His lab provides substantial computational resources including L40S GPUs and OpenAI API access, supporting both theoretical innovation and real-world deployments through industry internships. Current advising focuses on generative graph models, LLM-agent collaboration, and trustworthy AI frameworks for societal applications. The Graph Machine Learning Lab maintains strong ties with industrial research groups, facilitating student internships at Adobe Research and other tech companies while developing open-source tools like ChemicalX for drug discovery. Ongoing projects include GraphRAG frameworks for security applications and social simulation environments for studying network dynamics.

Simon Kentمشاهده پروفایل

دانشیار

• Digital Education
• Machine Learning
• Distributed Computing
• +۴ مورد دیگر

Dr Simon Kent serves as an Honorary Reader in the Department of Computer Science within Brunel University London's College of Engineering, Design and Physical Sciences, while currently working at the University of Nottingham. Previously, he held the position of Director of Learning and Teaching in Brunel's Computer Science Department, leveraging his dual background in academia and financial services software architecture to bridge industry-academia gaps. His educational qualifications include a PhD in Artificial Intelligence focused on robot path planning and a Postgraduate Certificate in Learning and Teaching Higher Education. Kent is recognized as a Fellow of the Higher Education Academy and maintains membership in the British Computer Society. Research interests span digital education analytics, machine learning, distributed/cloud computing, and software engineering pedagogy. Kent pioneers industry-aligned educational innovations, notably implementing the UK's first large-scale BYOD digital examination system at Brunel and analyzing assessment data to enhance student outcomes. His work consistently applies computational techniques like genetic programming to cross-disciplinary challenges in healthcare, defense, and finance. Publication trends reveal an evolution from foundational AI work in the 1990s (oral cancer diagnosis, missile systems) through distributed computing applications in the 2000s (video rendering, diabetes monitoring) toward contemporary digital education research. Key thematic threads include real-world problem solving, industry collaboration, and translating technical innovation into educational impact. Scientific recognition includes: Fellow of the Higher Education Academy Member of the British Computer Society As an educator, Kent directs the Level 2 Group Project (CS2001) simulating commercial software development environments and mentors across undergraduate/master's programs. He secured significant research funding including a Sucden UK collaboration for distributed order matching systems and Diabetes UK proposals for diabetic teenager monitoring applications, emphasizing practical industry partnerships. Affiliated with Brunel's CIKM and CSSB research groups, Kent continues advancing digital assessment methodologies while exploring IoT applications for home energy optimization through appliance collaboration networks.

• Climate Science
• Volcanology
• Glaciology
• +۴ مورد دیگر

Eliza Cook is an Assistant Professor at the University of Copenhagen , affiliated with the Physics of Ice, Climate and Earth group under the Niels Bohr Institute . Her work focuses on integrating glaciology , volcanology , and climate science using ice core and tephra analysis.

• Artificial Intelligence
• Artificial Neural Networks
• Information Systems
• +۳ مورد دیگر

Konstantin Schekotihin is an Associate Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria University of Klagenfurt. His research focuses on artificial intelligence, machine learning, and semantic technologies with applications in industrial systems and semiconductor manufacturing. Reinforcement learning for industrial scheduling Answer Set Programming (ASP) and stream reasoning Failure analysis automation and ontology engineering Neuro-symbolic AI integration Knowledge-based systems in manufacturing Recent publications emphasize AI-driven optimization in semiconductor production, decomposition strategies for scheduling problems, and multi-agent systems for workflow management. His work combines symbolic reasoning with machine learning to address complex industrial challenges. Contact: Konstantin.Schekotihin@aau.at

Mojtaba Sadeghمشاهده پروفایل

دانشیار

• Water Resources Management
• Hydrology
• Climate Change Impacts
• +۴ مورد دیگر

Mojtaba Sadegh serves as Associate Professor in the Department of Civil Engineering within Boise State University's College of Engineering, maintaining an active research program from his office in ERB 4147 (contact: (208) 426-3774, mojtabasadegh@boisestate.edu). His academic profile bridges hydrological systems analysis with emerging climate-driven fire dynamics, reflecting institutional alignment with environmental engineering priorities. Dr. Sadegh's research portfolio demonstrates methodological versatility across water resources management, statistical hydrology, and climate change impacts. Initial work applied game theory to water transfer conflicts in politically contested basins, while recent efforts examine non-stationarity in hydrological systems, flow duration curve modeling, and drought prediction frameworks. His current focus centers on water-energy-food (WEF) nexus modeling, food waste impacts on resource security, and hydroclimate extremes affecting human health. This evolution is mirrored in his publication trajectory, which increasingly integrates machine learning with physical environmental processes. Analysis of his 15 most recent publications (2025) reveals three dominant research streams: wildfire science (70% of output), machine learning applications in environmental systems (20%), and socio-ecological vulnerability (10%). The wildfire studies emphasize predictive modeling of fire spread, fuel treatment efficacy, and human exposure patterns, while machine learning work features novel architectures like Kolmogorov-Arnold networks for flood mapping and bias correction in seasonal forecasts. A consistent thread examines disproportionate impacts on vulnerable populations, particularly regarding fire exposure and water stress. Dr. Sadegh actively mentors graduate students through his NSF S-STEM grant (BOARD# 450), specifically addressing barriers for low-income, first-generation, and rural STEM graduate students. His research receives external funding focused on climate adaptation and resource security, though specific grant mechanisms beyond S-STEM remain undocumented in available materials. The absence of laboratory or team descriptions suggests primarily computational and theoretical research approaches.

Sean Kauffmanمشاهده پروفایل

استادیار

• Formal Methods
• Real-time Systems
• Software Engineering
• +۴ مورد دیگر

Sean Kauffman is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University, Faculty of Engineering and Applied Science. He holds his office in Walter Light Hall, Room 611, and can be reached at sean.k@queensu.ca or by phone at 613-533-6000 ext. 77360. Dr. Kauffman earned his Ph.D. in Electrical and Computer Engineering from the University of Waterloo before completing a two-year postdoctoral position at Aalborg University in Denmark. Notably, he returned to academia after accumulating over a decade of industry experience as a software engineer, with his final industry role being Principal Software Engineer at Oracle. His research expertise spans several critical areas in computer science and software engineering, with a particular focus on safety-critical software systems. His work significantly contributes to the fields of Formal Methods, Runtime Verification, Anomaly Detection, and Explainable AI. Dr. Kauffman has established productive research collaborations with prestigious organizations including NASA's Jet Propulsion Laboratory, the Embedded Systems Institute, QNX, and Pratt and Whitney Canada. Dr. Kauffman's research output demonstrates a consistent focus on event stream analysis, formal verification techniques, and the development of practical tools for system monitoring. His most notable contribution is the nfer language and toolset, which has become influential in the runtime verification community for its ability to abstract event streams into meaningful temporal hierarchies. His publications reveal a progression from theoretical foundations to practical implementations, with applications spanning spacecraft telemetry, autonomous vehicles, and embedded systems. Among his scientific contributions, Dr. Kauffman has received recognition for his work on the complexity analysis of nfer evaluation, developing methods for annotating control-flow graphs for formalized test coverage criteria, and creating frameworks for anomaly detection in embedded systems. His research has been published in top-tier venues including Science of Computer Programming, International Journal on Software Tools for Technology Transfer, and proceedings of major conferences like Runtime Verification and NASA Formal Methods. As an educator, Dr. Kauffman employs active learning techniques, productive failure approaches, and peer instruction to foster student engagement. His industry background informs his teaching approach, providing students with practical insights into real-world software engineering challenges, particularly in safety-critical domains. Dr. Kauffman leads the CritLab research group at Queen's University, which focuses on critical systems research. The lab develops tools and techniques for analyzing and verifying systems where failures could have severe consequences, with applications in aerospace, automotive, and other safety-critical domains. His work on the nfer language has spawned related projects including nvis for visualizing temporal interval hierarchies.

Allison Stromمشاهده پروفایل

استادیار

• Galactic Evolution
• Astrophysics
• Stellar Feedback Mechanisms
• +۳ مورد دیگر

Allison Strom is an Assistant Professor in the Department of Physics and Astronomy at Northwestern University’s Weinberg College of Arts & Sciences. Her research focuses on galaxy formation and evolution using large-scale telescopic observations, particularly investigating baryonic processes such as accretion, stellar feedback, and outflows within dark matter halos. She holds a PhD from the California Institute of Technology (2017). Her research interests include chemical evolution of galaxies through spectroscopy, physical properties of distant galaxies (star formation rates, gas masses, electron densities), and massive stellar populations’ contributions to galactic feedback. She uses rest-UV and rest-optical spectroscopy to trace galactic chemical abundances and ionization states. Strom’s recent work emphasizes high-redshift galaxy studies, including circumgalactic medium dynamics, Lyα emission mechanisms, and ionizing radiation escape fractions. Her publications span observational surveys like CLASSY and KBSS-MOSFIRE, focusing on spectroscopic analysis and cosmological simulations. Awards: Packard Fellow (2024) Scialog Fellow (2024) Kavli Fellow (2024) Her research integrates observational data with theoretical models, addressing questions about galactic chemical enrichment, stellar feedback effects, and the interplay between galaxies and their environments. She contributes to major projects like the Keck Baryonic Structure Survey and COS Legacy Archive Spectroscopic Survey.

Guoxin Suمشاهده پروفایل

مدرس ارشد

• Formal Methods
• Probabilistic Verification
• Multi-Agent Systems
• +۲ مورد دیگر

Guoxin Su is a Senior Lecturer in the School of Computing and Information Technology at the University of Wollongong. He holds a Ph.D. in Computer Science from the University of Technology Sydney (2013) and previously served as a Senior Research Fellow at the National University of Singapore. His research spans software engineering (formal methods, probabilistic verification) and artificial intelligence (multi-agent systems, reinforcement learning). Current projects focus on developing verification techniques for distributed systems and adaptive AI algorithms. Su secured multiple grants including AUD $30,000 from the Australian Department of Defence for research on multi-agent systems. He teaches courses in Systems Analysis, Software Engineering Practices, and Big Data Management. As an active researcher, he serves on program committees for AIACT and CECIT conferences and reviews for IEEE Transactions on Software Engineering and IEEE Transactions on Information Forensics and Security.

Peter Ashmoreمشاهده پروفایل

استاد

• Fluvial Geomorphology
• Sediment Transport
• Urban River Systems
• +۳ مورد دیگر

Professor Peter Ashmore is a leading scholar in fluvial geomorphology at Western University's Department of Geography and Environment. His research focuses on sediment transport, urban river systems, and socio-geomorphological interactions, particularly in the Toronto region. He has supervised over 30 graduate students since 1990, advancing understanding of braided river dynamics, river restoration, and the impacts of urbanization. Teaching includes courses such as GEO 1300 (Physical Environment), GEO 2330 (Geomorphology & Hydrology), and GEO 3334 (River Channel Geomorphology). His work bridges field studies, experimental modeling, and computational tools, including the development of the Stream Power Index for Networks (SPIN) for urban watershed decision-making. Notable contributions include the 2019 Water Resources Research award-winning study on semi-alluvial river bedload transport under urbanization. Research interests span gravel-bed river morphodynamics, semi-alluvial channel processes, and socio-environmental dimensions of river management. Recent projects address river restoration design through geomorphic form variation metrics and synthetic digital modeling. His collaborative efforts integrate engineering, ecology, and policy to address urban river challenges.

Daniel Allenمشاهده پروفایل

دانشیار

• Stream Ecology
• Ecosystem Dynamics
• Biodiversity Conservation
• +۴ مورد دیگر

Daniel Allen is an Associate Professor in the Department of Ecosystem Science and Management at Pennsylvania State University. His research focuses on understanding how community structure influences ecosystem processes in rivers and streams, particularly across local to global spatial scales. He investigates factors affecting biodiversity, energy exchanges, and the impacts of environmental changes like stream drying. His work has been supported by grants such as a $1.95M NSF award for studying environmental DNA fate in streams. Key research areas include non-perennial streams, intermittent rivers, and the interplay between terrestrial and aquatic ecosystems. Allen collaborates globally, contributing to projects like the Aquatic Intermittency effects on Microbiomes in Streams (AIMS) network and the DRYvER initiative. Recent publications highlight his expertise in stream drying impacts, macroinvertebrate responses, and global patterns of ecosystem functioning. His findings emphasize the importance of hydrological connectivity and the vulnerability of freshwater systems to climate change. Allen’s research bridges ecology, hydrology, and biogeochemistry, with applications in conservation and policy. He has advised numerous collaborative projects but no explicitly listed students. Awards are not explicitly mentioned, but his extensive grant history reflects recognition of his contributions. His lab is part of the Institute for Energy and the Environment (IEE), focusing on water sustainability and climate systems.