جست‌وجوی استاد و پژوهشگر

Craig Knoblockمشاهده پروفایل

استاد پژوهشی

• Knowledge Graphs
• Data Semantics
• Schema Alignment
• +۵ مورد دیگر

Craig Knoblock serves as Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California (USC), Vice Dean of the USC Viterbi School of Engineering, and Research Professor of Computer Science and Spatial Sciences. He also directs the Data Science Program and the Center on Knowledge Graphs at USC. His educational background includes a Ph.D. and M.S. in Computer Science from Carnegie Mellon University (1991, 1988) and a B.S. with honors in Computer Science from Syracuse University (1984). Knoblock's research focuses on data semantics , specializing in source modeling, schema and ontology alignment, entity and record linkage, data cleaning, Web data extraction, and knowledge graph construction. His work bridges computer science, geospatial analysis, and artificial intelligence to solve complex data integration challenges. Recent projects emphasize historical map digitization, geospatial knowledge graphs, and smart city applications. His 300+ publications demonstrate consistent contributions to knowledge graphs and geospatial data integration, with a growing emphasis on historical map analysis and urban applications. The research trajectory shows increasing interdisciplinary collaboration across computer vision, geoinformatics, and domain-specific applications. IEEE Fellow (2020) ACM Fellow (2017) AAAI Fellow (2004) Robert S. Engelmore Memorial Lecture Award (2014) Donald E. Walker Distinguished Service Award (IJCAI, 2018) Use-Inspired Research Award (USC Viterbi, 2018) As Executive Director of ISI, Knoblock oversees one of USC's premier research centers with significant federal funding. His leadership extends to directing the Center on Knowledge Graphs and the Data Science Program. While specific grant details aren't provided, his extensive publication record and leadership roles indicate substantial research funding across data integration, knowledge representation, and geospatial applications. His work bridges theoretical computer science with practical applications in historical preservation, urban planning, and resource management through collaborative projects with government agencies and industry partners. Knoblock leads the Center on Knowledge Graphs at USC, focusing on developing techniques for building and utilizing knowledge graphs across diverse domains. His team combines expertise in artificial intelligence, geospatial analysis, and data integration to tackle challenges in historical map digitization, urban applications, and resource discovery. The research group maintains strong connections with both academic and government partners through the Information Sciences Institute's extensive network.

• Internet of Things
• Edge Computing
• Artificial Intelligence
• +۶ مورد دیگر

Professor Xiaodong Liu is a faculty member at Edinburgh Napier University, affiliated with the School of Computing, Engineering and the Built Environment . His research spans Internet of Things , Edge Computing , Artificial Intelligence , and Cybersecurity , with a focus on decentralized systems and data-driven decision-making. Research Themes : IoT orchestration, federated learning, smart city infrastructure, building maintenance optimization, and automotive cybersecurity. Current Projects : Leading Swarmchestrate (EU-funded), Long-range Perceptive Autonomous Vehicles (Royal Society), and Met-Bot for Disaster Surveillance (Royal Society). His recent publications emphasize privacy-preserving edge learning , semantic IoT data validation , and deep learning for weather prediction . As a supervisor, he has guided PhD students in areas like federated learning, smart building systems, and IoT security. Collaborations include partnerships with institutions in Scotland, China, and Italy, alongside funding from European Commission , Royal Society , and Scottish Funding Council . He contributes to international conferences and journals, with notable work in IEEE Transactions , ACM TAAS , and MDPI publications.

• Business Intelligence
• Cloud Computing
• Software Engineering
• +۷ مورد دیگر

Professor Farookh Hussain is a distinguished academic at the School of Computer Science , University of Technology Sydney , specializing in Artificial Intelligence , Cloud Computing , and Software Engineering . His research spans diverse sectors including agriculture, manufacturing, healthcare, and transportation. Affiliated with the Australian Artificial Intelligence Institute (AAII) , he leads impactful work in business intelligence and carbon credit systems. Key research areas: AI applications, blockchain for provenance, carbon credit analytics Active in Masters/PhD supervision and cloud computing education Research Highlights : Developed KACINO framework for carbon dynamics modeling Created hybrid cybersecurity frameworks for supply chain risk management Advanced chatbot dialogue breakdown solutions through systematic reviews Proposed hypercomplex knowledge graph recommenders Published extensively on carbon credit price prediction and blockchain storage methods Contributions to water demand forecasting and collaborative robotics adoption Grant Activities : Secured funding from Hampton Capital Asset Management , Innovation Connections , and Science and Industry Endowment Fund Projects include LLM-driven text-to-SQL conversion , blockchain for melanoma data , and AI for storm water management

Tien N. Nguyenمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۹ مورد دیگر

Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.

Kalina Bontchevaمشاهده پروفایل

پژوهشگر

• NLP for social media
• semantic search
• GATE infrastructure
• +۸ مورد دیگر

Kalina Bontcheva is a Senior Researcher in the Natural Language Processing Group within the Department of Computer Science at the University of Sheffield. She holds an EPSRC Career Acceleration Fellowship (working part-time since October 2015) focused on personalized summarization of social media content. Her research spans multiple EU-funded projects including PHEME (computing veracity of social media), TrendMiner, DecarboNet, and uComp, with significant contributions to the GATE (General Architecture for Text Engineering) open-source NLP infrastructure since 1999. Dr. Bontcheva's research interests focus on the intersection of natural language processing and social media analysis. Her work encompasses NLP for social media, semantic search, information extraction from social platforms, crowdsourcing of NLP corpora, collaborative text annotation, semantic technologies, and text mining and analytics. She has particular expertise in developing methods for personalized, abstractive multi-document summarization across different social media platforms, addressing the challenges of noisy, jargon-filled and dynamic content. Her interdisciplinary approach combines machine learning, semantic technologies, and social dimension analysis to create systems that adapt to individual users' information seeking goals. Analysis of her recent publications reveals a strong focus on social media processing challenges, with emphasis on Twitter analysis, temporal expression recognition, and handling noisy text. Her work consistently addresses the unique characteristics of social media content and develops specialized techniques for information extraction, sentiment analysis, and user geolocation within these platforms. The GATE framework serves as the foundation for much of her tool development, demonstrating her commitment to creating reusable, open-source NLP infrastructure. Her most significant award is the EPSRC Career Acceleration Fellowship, which supports her work on personalized social media summarization. This prestigious fellowship includes a substantial budget of £560k and involves collaborations with industry partners including The Press Association, British Telecom, and Fizzback. Dr. Bontcheva has led numerous major research projects throughout her career. She was Principal Investigator on three EU-funded projects (MUSING, TAO, and ServiceFinder) between 2006-2009, coordinating the TAO consortium with seven partner institutions. She currently leads the PHEME EU project and serves as PI for TrendMiner and DecarboNet European projects, while also contributing as Co-I on the uComp project. Her project portfolio demonstrates consistent success in securing competitive research funding across multiple domains within NLP and semantic technologies. She works within the Natural Language Processing Group at the University of Sheffield, which has been central to the development of the GATE infrastructure. Her work connects with various initiatives including the GATE Cloud platform and the TextVRE project for e-humanities textual studies. She has established collaborations with organizations including the Press Association, British Telecom, Oxford Internet Institute, and Sheffield's Department of Journalism to ensure her research addresses real-world needs across different user communities.

• Metallurgical Process Engineering
• Metal Recycling
• Extractive Metallurgy
• +۷ مورد دیگر

Professor Karl Bernhard Friedrich serves as Universitätsprofessor and Chair of Metallurgical Process Engineering and Metal Recycling at RWTH Aachen University's Faculty of Georesources and Materials Engineering. He leads the Institute for Metallurgical Science and Electrometallurgy (IME), with his office located in Building 1401, Room 108 at Intzestraße 3 in Aachen. His leadership extends to numerous EU-funded research initiatives focused on sustainable metallurgy and circular economy principles. Professor Friedrich's research interests span metallurgical process engineering, metal recycling, extractive metallurgy (pyrometallurgy and hydrometallurgy), battery recycling technologies, lithium recovery processes, and vacuum metallurgy. His work emphasizes resource efficiency and sustainable practices in metal production and recycling, with particular focus on developing environmentally sound processes for electronic waste and battery recycling. The institute under his leadership has pioneered several innovative recycling concepts for lithium-ion batteries and electronic scrap. Analysis of his recent publications reveals a strong focus on battery recycling technologies, particularly lithium recovery from spent batteries through both pyrometallurgical and hydrometallurgical routes. His research group has developed advanced processes for black mass treatment, slag valorization, and metal recovery optimization. The work demonstrates increasing integration of digital technologies and multi-objective modeling in metallurgical process development. Honorary doctorate from Montanuniversität Leoben (2025) recognizing outstanding scientific achievements and commitment to promoting young scientists Long-standing collaboration with Montanuniversität Leoben through the Aachen-Leoben Workshop for non-ferrous metallurgists Professor Friedrich actively supervises numerous doctoral students and has established several collaborative research programs including the DFG priority program 'engineered artificial minerals,' and doctoral programs 'circular electronics' and 'circular e-cars.' His institute maintains strong industry connections, with growing demand for TBRC and VAR trial campaigns. The institute recently commissioned a new large-scale arc furnace and is preparing for leadership transition in early 2027. The IME under Professor Friedrich's leadership operates extensive research facilities focused on vacuum metallurgy, pyrometallurgy, and hydrometallurgy. The institute collaborates with industry partners through the GDMB Zinc & Lead Technical Committee and participates in numerous European research networks. Current research activities focus on developing sustainable processes for battery recycling, electronic waste treatment, and critical metal recovery with emphasis on circular economy principles.

• Case-Based Reasoning (CBR)
• Artificial Intelligence
• Multi-Agent Systems
• +۴ مورد دیگر

Dr. Pascal Reuss is a Researcher at the Intelligent Information Systems (IIS) Division within the Institute of Computer Science , University of Hildesheim . His work focuses on Case-Based Reasoning (CBR) systems, Multi-Agent Systems , and Knowledge Management applications. Active in CBR framework development and game-based AI research Teaching Computer Science III (Databases) for winter 2025/26 Participating in university sustainability initiatives like Stadtradeln 2024/25 Reuss contributes to AI education through practical implementations in gaming environments and has developed visualization tools for CBR agent behavior. His research spans multi-agent collaboration , dynamic case bases , and domain-specific language implementations for knowledge maintenance. Notable contributions include: Co-developing the FEATURE-TAK framework for knowledge extraction Designing case factories for distributed CBR systems Implementing finite state machines for tactical game agents Creating CBR-based fitness planning systems His work appears in various CBR and Game Development publications from 2011-2024. The research demonstrates practical applications of CBR in aircraft maintenance diagnostics , training plan generation , and educational technology contexts.

Francesca Pianosiمشاهده پروفایل

دانشیار

• Water Resource Management
• Hydrology
• Environmental Modelling
• +۵ مورد دیگر

Dr Francesca Pianosi is an Associate Professor in Water & Environmental Engineering at the University of Bristol 's School of Civil, Aerospace and Design Engineering. She contributes to the Cabot Institute for the Environment and leads research on data analysis, mathematical modelling, and uncertainty quantification for hydrology and water engineering. Specialises in simulation and optimisation methods for water resource management Focuses on uncertainty propagation in natural hazard models Developed the open-source SAFE Toolbox for sensitivity analysis Research Trends Her recent publications (2023-2025) demonstrate expertise in: Groundwater flow and recharge in data-scarce regions Digital Twin applications for watershed management Climate change impact on landslides and droughts Multi-objective optimisation for reservoir operations Integration of machine learning with hydrological models Scientific Awards Arne Richter Award for Outstanding Young Scientists (2015) Best Research Oriented Paper - Journal of Water Resources Planning and Management (2024) Early Career Research Excellence (ECRE) award (2014) Francesca leads the Water Management and Adaptation based on Watershed Digital Twins project (2024-2027) and contributes to the USARIS project on uncertainty quantification for infrastructure systems (2023-2025).

• Ambient Intelligence
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Paulo Jorge Freitas de Oliveira Novais is a Full Professor of Computer Science at the Department of Informatics, School of Engineering, Universidade do Minho, where he also holds a Habilitation in Computer Science. He leads the Synthetic Intelligence Lab at ALGORITMI Centre and coordinates the research line on Ambient Intelligence for Well-Being and Health Applications. His research spans Intelligent Systems, Machine Learning, Multi-Agent Systems, and their applications in Smart Cities, Health Informatics, and AI Ethics. PhD in Computer Science, Universidade do Minho, 2003 Habilitation in Computer Science, Universidade do Minho, 2011 Research interests include Ambient Intelligence, Ambient Assisted Living, Intelligent Environments, AI and Law, Conflict Resolution, and Explainable AI. His work focuses on enhancing system intelligence and reliability through novel architectures and ethical frameworks. Recent publications highlight applications in wastewater energy prediction, violence detection, student risk modeling, and urban logistics. Awards include multiple Best Paper and IBM Excellence recognitions across 2015–2023, plus a 2022 Career Recognition Award from the Ibero-American Society of Artificial Intelligence. Senior IEEE Member Chair of IEEE Computational Intelligence Chapter, Portugal IFIP TC 12 Artificial Intelligence Working Group Leadership He has supervised 132 PhD and Master’s students and contributed to editorial boards of journals like JAISE and ComSIS . His leadership roles include coordinating LASI – Intelligent Systems Associate Laboratory and serving as former president of APPIA.

Fajar Juang Ekaputraمشاهده پروفایل

عضو هیئت علمی

• Semantic Web
• Knowledge Graphs
• Neurosymbolic AI
• +۴ مورد دیگر

Fajar Juang Ekaputra is a Tenure Track Assistant Professor at the Institute of Data, Process, and Knowledge Management (DPKM), WU Vienna and a part-time Postdoctoral Researcher at the Data Science research unit, TU Wien . With a focus on Semantic Web , Knowledge Graphs , and their integration with Machine Learning in Neurosymbolic AI systems, his work spans domains like Cyber-Physical Systems and Materials Engineering . Education: Dr.techn. (2018), TU Wien M.T. (2010) and S.T. (2008), Institute Teknologi Bandung (ITB) Research Interests center on hybrid AI systems combining Semantic Web and Machine Learning , with applications in Cyber-Physical Systems (e.g., smart grids, smart buildings), data privacy in smart cities, and materials engineering . His 102+ publications include frameworks like SWeMLS-KG and SHACL4Protege . Recent Articles (2024) address explainable AI in cyber-physical systems, privacy trust in data infrastructures, and neurosymbolic frameworks . Earlier works (2023–2022) explore ontology-based data management , auditable AI , and hybrid system architectures . Scientific Awards: Best Paper Awards (ICoDSE 2023, ICoDSE 2016) Best Poster Nomination (SEMANTiCS 2019) PhD Scholarship (Austria’s Agency for Education and Internationalisation, 2012) Advising includes supervising PhD students (e.g., Majlinda Llugiqi, Katrin Schreiberhuber) and master’s theses on topics like knowledge graph characteristics and data quality assessment . He leads projects such as FAIR-AI (FFG-funded, 2024–2026) and SENSE (Horizon Europe, 2023–2025).

• Flood Risk Management
• Climate Change Adaptation
• Disaster Risk Reduction
• +۳ مورد دیگر

Edmund Penning-Rowsell serves as Pro Vice-Chancellor (Research) and Deputy Vice-Chancellor Academic at Middlesex University, leading institutional research strategy while maintaining an active research profile in flood risk management. His work bridges hydrological science, social policy, and economic appraisal to address complex flood and coastal challenges in the UK and globally. His research focuses on: Human dimensions of flood risk and social vulnerability Policy innovation in flood governance and insurance mechanisms Economic appraisal methods for coastal and flood risk management Climate adaptation strategies including Sponge Cities and floating architecture Transboundary cooperation and judicial conflict resolution in water management Distributional justice in flood risk reduction policies Recent publications (2020-2025) reveal an interdisciplinary trajectory emphasizing socio-technical integration, with significant work on policy experimentation in Austria, partnership models for coastal management, and data-driven approaches to flood risk assessment. His scholarship consistently connects academic research to real-world policy implementation through collaborations with government agencies and international bodies. No specific scientific awards were documented in the provided information. As senior research leadership, he oversees Middlesex University's research portfolio including Flood Hazard Research Centre initiatives, though specific student supervision details are absent from the text. His work demonstrates extensive collaboration with agencies like the Environment Agency and European Commission-funded projects. He is central to the Flood Hazard Research Centre at Middlesex University, which conducts applied research on flood risk governance through international partnerships. Current projects address data transparency for coastal resilience, innovative flood insurance models, and climate adaptation strategies across urban and rural landscapes.

Melanie Firestoneمشاهده پروفایل

استادیار

• Public health surveillance
• Infectious disease
• Foodborne disease
• +۴ مورد دیگر

Melanie Firestone is an Assistant Professor in the Division of Environmental Health Sciences at the University of Minnesota School of Public Health. Her academic journey includes a PhD in Environmental Health Sciences from the University of Minnesota, an MPH in Epidemiology from Columbia University, and a BS in Health & Exercise Science from Wake Forest University. Prior to joining the School of Public Health, she served as a CDC Epidemic Intelligence Service (EIS) Officer assigned to the Minnesota Department of Health. Dr. Firestone's research focuses at the intersection of infectious diseases and environmental health, with particular emphasis on improving public health surveillance systems to reduce enteric infections. Her work extensively examines the role of restaurants in foodborne illness transmission and prevention, as well as effective communication of food safety messages to the public. Her expertise spans public health surveillance, infectious disease epidemiology, foodborne disease investigation, food safety practices, and outbreak response. Analysis of her publication record reveals consistent research contributions in food safety, infectious disease epidemiology, and public health surveillance. Her work frequently involves collaborative investigations of foodborne outbreaks, SARS-CoV-2 transmission patterns, and zoonotic disease events. Recent publications demonstrate her focus on methodological improvements in outbreak detection and understanding transmission dynamics across various settings including restaurants, fitness centers, and households. Dr. Firestone is actively engaged with professional organizations including the Council for State and Territorial Epidemiologists, the International Association for Food Protection, and the Minnesota Environmental Health Association, demonstrating her commitment to advancing public health practice through professional collaboration and knowledge sharing.

Flora Salimمشاهده پروفایل

استاد

• Machine Learning
• Time-series Analysis
• Spatio-temporal Data Mining
• +۵ مورد دیگر

Flora Salim is a Professor in the School of Computing Technologies at RMIT University. She serves as co-Deputy Director of the RMIT Centre for Information Discovery and Data Analytics (CIDDA) and an Associate Investigator of the ARC Centre of Excellence in Automated Decision Making and Society. Her research focuses on human behavior modeling, machine learning with time-series and spatio-temporal data, and edge AI applications in IoT and wearables. Flora has secured over $10M in research funding from ARC, industry partners, and government bodies. Notable awards include the 2021 PACM IMWUT Distinguished Paper Award, 2019 Humboldt-Bayer Fellowship, and RMIT's 2018 Research Impact Award. She leads the CRUISE research group and has held visiting professorships at the University of Kassel and University of Cambridge. Editorial roles: Associate Editor of PACM on IMWUT, Area Editor of Pervasive and Mobile Computing Steering Committee member of ACM UbiComp Her work bridges ubiquitous computing and machine learning, with applications in urban analytics, mobility, and health monitoring. Recent projects include self-supervised learning for multimodal data and forecasting with heterogeneous time-series. Supervision areas: Deep learning for sensor data, explainable AI, and wearable-based emotion sensing Teaching programs: Master of Artificial Intelligence and Master of Data Science

Arjun Mukherjeeمشاهده پروفایل

مدرس

• Bayesian Inference
• Data Mining
• Natural Language Processing
• +۳ مورد دیگر

Arjun Mukherjee is a Lecturer at the Department of Computer Science , University of Houston , where he teaches courses in Machine Learning , Data Mining , Natural Language Processing , and Data Structures . His research focuses on Bayesian Inference , Data Mining , Natural Language Processing , Sentiment Analysis , Opinion Spam , and Web Mining , with a strong emphasis on deception detection and social media analysis. His recent publications explore advanced techniques in LLM-generated content detection synthetic data applications cross-domain deception modeling temporal user behavior analysis , reflecting his commitment to addressing modern challenges in digital content authenticity and machine learning robustness. Dr. Mukherjee has developed educational materials for graduate-level courses, including a well-structured Machine Learning course (COSC 6342) covering probabilistic inference, supervised/unsupervised learning, and neural networks. He earned his Ph.D. from the University of Illinois at Chicago in 2014, with a thesis titled Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications .

• Cryptography
• Secure Multiparty Computation (MPC)
• Zero-Knowledge Proofs
• +۳ مورد دیگر

Aarushi Goel is an Assistant Professor of Computer Science at Purdue University's Department of Computer Science, part of the College of Science. She joined the department in Fall 2024 and is affiliated with Purdue's main campus in West Lafayette, Indiana. Her research focuses on cryptographic protocols, with a strong emphasis on secure multiparty computation (MPC), zero-knowledge proofs, and distributed systems security. Her work addresses challenges in privacy-preserving computation, Byzantine fault tolerance, and efficient cryptographic systems. Notable research directions include optimizing MPC protocols for dynamic environments, minimizing communication overhead in distributed systems, and developing practical zero-knowledge proof frameworks. Recent publications explore topics like homomorphic secret sharing, sublinear prover protocols, and Byzantine agreement with polylogarithmic communication complexity. Dr. Goel's contributions often bridge theoretical cryptography with real-world applications, such as secure machine learning training and verifiable computation. Her work has implications for decentralized systems, blockchain technology, and privacy-enhancing technologies. No awards or grants are explicitly listed in the provided materials, but her prolific publication record indicates active engagement in cutting-edge research. She holds no listed advisees or students in the current data. Lab and team affiliations are not specified in the provided text, though her research likely involves collaborations within Purdue's computer science department and external institutions. Her office is located in LWSN 2142D on the Purdue campus.