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Jay PujaraView profile

Academic

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +5 more

Jay Pujara is a Research Associate Professor in the Department of Computer Science at the University of Southern California (USC), affiliated with the Viterbi School of Engineering and the Information Sciences Institute (ISI). He directs the Center on Knowledge Graphs and leads research in artificial intelligence, specializing in knowledge graph construction, scalable machine learning, and probabilistic models. Education : PhD in Computer Science (University of Maryland, 2016), MS and BS in Computer Science from Carnegie Mellon University (2005 and 2004), with minors in Robotics, Mathematical Sciences, and Logic & Computation. Research Interests : His work focuses on probabilistic models for dynamic data, knowledge graph construction, entity resolution, and applications in NLP and social network analysis. He emphasizes scalable algorithms and real-world impact in domains like finance, climate science, and healthcare. Awards : Includes the SWSA Ten-Year Award (2023), Best Paper Awards at IUI 2019 and ISWC 2013, and grants totaling over $9M from DARPA, NSF, and industry partners. Grants & Mentorship : Principal Investigator on projects like "Artificial Domain-Understanding and Collaborative Agency" (DARPA) and "Explainable and Robust AI Agents" (NSF). Mentored over 50 students in PhD, MS, and undergraduate programs, focusing on knowledge graphs, NLP, and machine learning. Labs & Teams : Leads ISI’s Knowledge Graph and Neurosymbolic AI teams, coordinating the Open Knowledge Network (OKN) and tools like KGTK. Active in academic service, including roles on PhD admissions committees and ISI’s Space Management Committee.

• Knowledge Representation
• Semantic Web
• Ontology Engineering
• +7 more

Jiaoyan Chen is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester. She previously held roles as a Lecturer at Manchester, a Senior Researcher at the University of Oxford, and a Postdoctoral Fellow at Heidelberg University. Education: PhD and Bachelor's in Computer Science and Technology from Zhejiang University (2016 and 2011), with a visiting PhD stint at Zurich University's Department of Informatics. Research Interests: Integrating knowledge graphs and ontologies with machine learning and large language models (LLMs), focusing on semantic embeddings, knowledge curation, and explainable AI systems. Publication Trends show emphasis on ontology embeddings (e.g., OWL2Vec*), LLM evaluation with knowledge graphs, and hybrid neural-symbolic reasoning. Her work bridges structured knowledge and modern AI through projects like OntoEm and ConCur . Current Research Team includes postdoctoral researchers, PhD students, and externally co-supervised associates. She actively recruits PhD candidates in areas like Retrieval-Augmented Generation and LLM Explainability , with projects funded by EPSRC and international consortia. Grants & Leadership: EPSRC New Investigator Award (2023-2026) Manchester-Melbourne-Toronto Research Fund (2024-2026) EPSRC ConCur Project (2021-2025) Professional Service: Associate Editor, Transactions on Graph Data and Knowledge EPSRC Peer Review College member OAEI Track Co-organizer at ISWC

Sean RenView profile

Associate Professor

• Natural Language Processing
• Commonsense Reasoning
• Graph Neural Networks
• +3 more

Sean Ren is an Associate Professor in Computer Science at the University of Southern California, where he holds the Andrew and Erna Viterbi Early Career Chair. He directs the INK Research Lab and serves as Research Team Leader at USC's Information Sciences Institute. Affiliated with the USC NLP Group and Machine Learning Center, his research focuses on developing robust NLP systems through knowledge-aware architectures and data-efficient learning. His research interests include: Evaluation methods exposing NLP limitations in reasoning tasks Augmenting models with commonsense/knowledge via novel algorithms Graph neural networks for relational inference Model robustness verification and enhancement Neural-symbolic integration for interpretable AI Recent publications demonstrate strong emphases on language model reasoning, knowledge distillation, and compositional generalization. His group's ACL/NeurIPS papers frequently address robustness gaps in state-of-the-art models. Honors include: ACL Outstanding Paper (2023) MIT TR Innovator 35 Asia Pacific (2023) NSF CAREER Award (2021) Forbes 30 Under 30 (2019) ACM SIGKDD Dissertation Award (2018) Research is supported by NSF, DARPA, IARPA, and industry partners (Google, Amazon, Meta). He leads the INK Lab with focuses on label-efficient learning and knowledge-guided NLP, while actively recruiting PhD students for projects bridging symbolic and neural paradigms.

Heiko PaulheimView profile

Professor

• Knowledge Graphs
• Semantic Web
• Web Data Mining
• +5 more

Prof. Dr. Heiko Paulheim is a Professor of Data Science and currently serves as University Vice President at the University of Mannheim. He leads the Data and Web Science Group (DWS), which focuses on Web Data Mining, Knowledge Graphs, and Semantic Web technologies. His research group contributes to open source knowledge graphs like DBpedia and develops new knowledge graphs such as WebIsALOD and DBkWik. As of October 1, 2024, he has limited teaching capacity due to his vice presidential duties. Prof. Paulheim's research interests span Knowledge Graphs, Semantic Web, Web Data Mining, Machine Learning, and Natural Language Processing. His work particularly focuses on knowledge graph refinement, embedding techniques (notably RDF2vec), and applications in various domains including news recommendation, biomedical informatics, and environmental monitoring. His group develops practical tools like the RapidMiner Linked Open Data Extension and RDF2vec for knowledge graph applications. His recent publications demonstrate a strong focus on knowledge graph embeddings, with particular attention to RDF2vec variants, applications in news recommendation systems, biomedical data integration, and spatio-temporal knowledge graphs for environmental monitoring. His work bridges theoretical advances in knowledge representation with practical applications across multiple domains. Among his notable achievements are a nomination for the Best Paper Award at CAiSE 2025 and securing an Open Science Grant for the SpatialBenchRAG project. His research has significant impact in both academic and industrial contexts, with multiple papers accepted at top conferences like ISWC and ESWC. Prof. Paulheim has supervised numerous PhD students including Alexander Brinkmann and Michael Schlechtinger, and has led several research projects including the DFG Project Mine@LOD, State of BW Project SyKoW², and BMBF Project DS4DM. His group maintains strong industry connections with partners like SAP AG, Daimler AG, and IDS.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +12 more

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

Emanuel SallingerView profile

Professor

• Information Systems Engineering
• Computational Logic
• Scalable Artificial Intelligence
• +8 more

Emanuel Sallinger is a Full Professor at TU Wien's Databases and Artificial Intelligence Group and Vice Dean of Academic Affairs for Business Informatics and Data Science. He leads the Knowledge Graph Lab, focusing on scalable knowledge-based systems, reasoning in knowledge graphs, and AI integration. His research spans computational logic, database theory, and blockchain applications. Education: PhD in Computer Science (awarded 'sub auspiciis praesidentis rei publicae'), Master's degrees in Computational Intelligence and Informatics Management, and a Bachelor's in Software and Information Engineering. Research Interests: Knowledge graphs (construction, reasoning, scalability), logic-based systems, AI/ML integration with databases, enterprise architecture modeling, and financial knowledge systems. His work emphasizes practical applications like enterprise modeling, sustainable waste management, and regulatory compliance. Grants & Projects: Lead Vienna Science and Technology Fund (WWTF)-funded Knowledge Graph Lab. Involved in projects like 'Knowledge Graph-driven Tour Management' (sustainability), 'SustainGraph' (waste processing), and 'Enterprise Architecture Knowledge Graphs'. Teaching: Offers courses on Knowledge Graphs, Generative AI, Database Systems, and research methodology. Supervises doctoral and master's students in AI, databases, and knowledge representation. Labs/Teams: Knowledge Graph Lab at TU Wien, collaborating with industry on blockchain-based systems, financial AI, and enterprise architecture frameworks.

Sriraam NatarajanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Applications
• +5 more

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning and StaRLing Lab at The University of Texas at Dallas (UTD), part of the Erik Jonsson School of Engineering & Computer Science. He holds additional roles as a hessian.AI Fellow at TU Darmstadt and an RBCDSAI Distinguished Faculty Fellow at IIT Madras. His expertise spans Artificial Intelligence, Machine Learning, and their applications in healthcare, with a focus on Relational Learning, Reinforcement Learning, and Graphical Models. He has been honored as an AAAI Fellow (2025), elected to the AAAI Executive Council, and recognized with the UTD Outstanding Graduate Teaching Award. Education: Completed his PhD in Computer Science at Oregon State University in 2007 under Prof. Prasad Tadepalli. Postdoctoral work at the University of Wisconsin-Madison with Professors Jude Shavlik and David Page. Previously served as faculty at Indiana University and Wake Forest School of Medicine. Research: Active in developing AI systems for healthcare, including predictive models for gestational diabetes and cardiac arrest in children. His work emphasizes integrating human knowledge into machine learning (e.g., Human-in-the-Loop systems) and advancing statistical relational AI frameworks like Markov Logic Networks and Probabilistic Circuits. Publications: Over 100 peer-reviewed articles, including notable works on causal learning, relational reinforcement learning, and knowledge graph construction. Recent focuses include explainable AI and scalable probabilistic models. Awards: AAAI Fellow, RBCDSAI Distinguished Fellowship, UTD Teaching Excellence Award. Advising and Collaboration: Mentored over 30 students, many now in academia and top institutions like IBM Research, Facebook, and Microsoft. Collaborates globally on projects like GLAD (Glocalized Anomaly Detection) and StaRLing Lab initiatives. Labs/Teams: Leads the StaRLing Lab, focusing on statistical relational AI, and directs UTD's Center for Machine Learning. Engaged in interdisciplinary projects with healthcare, robotics, and data science communities.

CHAN Chee YongView profile

Associate Professor

• Database Systems
• Query Processing
• Query Optimization
• +3 more

CHAN Chee Yong is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS) . He earned his Ph.D. in Computer Science from the University of Wisconsin-Madison and holds B.Sc. and M.Sc. degrees in Computer Science from NUS. Education : Ph.D., Computer Science, University of Wisconsin-Madison M.Sc., Computer Science, NUS B.Sc., Computer Science (1st Class Honours), NUS His research focuses on database systems , emphasizing query processing and optimization , transaction management , and database usability . He has contributed extensively to XML data dissemination, skyline computation, and multicore database performance optimization, with publications in venues like ACM SIGMOD, VLDB, and IEEE ICDE. Recent publications show increasing emphasis on join optimization , transaction healing , and spatial-keyword queries , reflecting trends in multicore systems, complex query processing, and XML data management. His work combines theoretical rigor with practical applications in distributed databases and data engineering. Notable professional roles include Associate Editor for the VLDB Journal , ACM SIGMOD Record , and IEEE Transactions on Knowledge and Data Engineering . He has served on program committees for major conferences like SIGMOD, ICDE, and VLDB across 2003-2026. Dr. Chan has supervised 9 PhD students and 10 M.Sc./M.Comp. students , including WANG TaiNing (2021), LI Meiying (2020), and TRAN Quoc Trung (2011). His advisees have been placed in institutions like the Institute for Infocomm Research and Huawei Shannon Lab.

David WrightView profile

Professor

• Smart Home Technology
• Artificial Intelligence
• Usability Studies
• +3 more

Dr. David Wright is a Professor in the Department of English and Technical Communication at Missouri University of Science and Technology (Missouri S&T). He joined the faculty in 2007 after prior roles at NASA’s Education Project, Oklahoma state government, and the software industry. He holds a Ph.D. in Technical Communication (Oklahoma State University, 2007), an M.S. in Higher Education Administration (1996), and a B.S. in Organizational Psychology (1993), all from Oklahoma State University. His research focuses on smart home technology and artificial intelligence, particularly examining human-AI interaction through usability and user experience (UX) testing. He also explores technology diffusion, technical communication practices in emerging technologies, and educational methodologies for technical fields. His work integrates interdisciplinary approaches, blending engineering, sociology, and computer science. Recent publications highlight his contributions to IoT usability, smart home adoption challenges, and the intersection of AI ethics with virtual assistants. He has also authored studies on knowledge graph design, technical documentation in software development, and educational initiatives in computer science and healthcare. Dr. Wright teaches courses in technical writing, usability studies, and web-based communication. His academic service includes curriculum development and advising on technical communication pedagogy. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in his fields.

Anupam JoshiView profile

Professor

• Networked Computing
• Intelligent Systems
• Cybersecurity
• +6 more

Anupam Joshi is the Acting Dean of the College of Information Technology and Engineering and Oros Family Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He also directs UMBC’s Center for Cybersecurity and leads the National Cybersecurity FFRDC for the University System of Maryland. His research focuses on networked computing, AI-driven cybersecurity, privacy-preserving technologies, and policy-driven security frameworks. He holds a Ph.D. in Computer Science from Purdue University (1993), an M.S. (1991), and a B.Tech in Electrical Engineering from the Indian Institute of Technology, Delhi (1989). Dr. Joshi’s work spans over 400 publications with 32,650+ citations (h-index 92) and nine patents. His grants include funding from NSF, DARPA, NASA, NIST, and industry partners like IBM and Northrop Grumman. Key contributions include developing CAPD frameworks for IoT security, FABULA for automated threat intelligence, and KiNETGAN for intrusion detection through synthetic data. He is an IEEE Fellow and pioneer in applying AI to secure critical infrastructure, smart grids, and healthcare systems. His research trends emphasize AI-empowered cybersecurity, privacy compliance in data sharing (e.g., agriculture, healthcare), and mitigating attacks on smart systems. Notable projects include combating fake cybersecurity reports using provenance analysis, securing EV charging infrastructure, and enhancing smart farming resilience through policy-driven access control. Awards: IEEE Fellow Grants: Over $30M from NSF, DoD, NASA, and industry collaborations Labs/Teams: Director of UMBC Center for Cybersecurity, Cybersecurity Knowledge Graph initiatives Future work includes advancing neurosymbolic AI for cybersecurity, semantic data extraction from scientific literature, and AI ethics in healthcare applications.

Jacco van OssenbruggenView profile

Professor

• Cultural Heritage
• Digital Libraries
• Ontology Engineering
• +7 more

Prof. Jacco van Ossenbruggen is a Full Professor in Intelligent Information Systems at Vrije Universiteit Amsterdam (VU), affiliated with the Network Institute. He serves on the Management Board of ODISSEI, a national research infrastructure for social sciences and economics. His academic background includes a PhD in Computer Science (2001) from VU’s Faculty of Science, focusing on hypermedia processing. Research Interests: His work centers on cultural AI, FAIR data principles, ontology engineering, and semantic web technologies. Key areas include inclusive cultural heritage metadata, bias mitigation in AI systems, and knowledge discovery via linked data. Recent projects involve leveraging large language models (LLMs) for metadata enrichment and ontology construction. Key Contributions: He leads initiatives like the Cultural AI Lab, exploring AI applications for cultural heritage. His research bridges technical innovations (e.g., semantic integration of restricted-access data) with societal impacts (e.g., ethical AI frameworks for public-sector applications). Developed frameworks for evaluating entity alignment in knowledge graphs Pioneered FAIR-aligned data management plans for scientific communities Designed tools like Alter Heritage for collaborative metadata curation Grants & Projects: Principal Investigator of the ODISSEI Portal project (2020–2024), advancing open data infrastructures. Active in funding initiatives promoting reproducible research and ethical data practices. Labs/Teams: Cultural AI Lab at VU, focusing on AI-driven solutions for cultural heritage preservation and accessibility.

Mohammad RostamiView profile

Academic

• Machine Learning
• Transfer Learning
• Domain Adaptation
• +5 more

Mohammad Rostami is a Research Assistant Professor at the University of Southern California (USC) in the Department of Computer Science and Electrical and Computer Engineering, with a joint appointment at the USC Information Sciences Institute (ISI). He holds a PhD in Electrical and Systems Engineering from the University of Pennsylvania and additional degrees in Robotics, Philosophy, Electrical Engineering, and Pure Mathematics from prestigious institutions including the University of Waterloo and Sharif University of Technology. His research focuses on machine learning in data-scarce environments, particularly transfer learning, domain adaptation, low-shot learning, and improving learning efficiency through continual and collective learning. He incorporates symbolic logic and neuro-symbolic approaches to address challenges in catastrophic forgetting and knowledge retention. Applications span medical imaging, computer vision, and explainable AI. Rostami has received several accolades including the UPenn Best PhD Dissertation Award, IJCAI Distinguished Student Paper Award, and University of Waterloo Outstanding Achievement Award. His work bridges theoretical advancements with practical implementations, emphasizing real-world applications in healthcare and autonomous systems. He teaches graduate courses in applied natural language processing and knowledge graph construction. Rostami advises students at all academic levels and collaborates with remote researchers, emphasizing motivated, long-term project commitments.

Elsa A. OlivettiView profile

Professor

• Sustainable Materials
• Environmental Engineering
• Materials Science
• +3 more

Elsa A. Olivetti is the Jerry McAfee (1940) Professor in Engineering and Professor of Materials Science and Engineering at MIT, and a MacVicar Faculty Fellow. She leads the Olivetti Group, focusing on sustainable materials design, recycling strategies, and computational models for environmental and economic impact assessment. Her work bridges materials science with sustainability, emphasizing circular economy principles and decarbonization. Education: B.S. in Engineering Science from University of Virginia (2000); Ph.D. in Materials Science and Engineering from MIT (2007). Her doctoral research centered on lithium-ion battery electrode materials. She joined MIT’s Department of Materials Science and Engineering (DMSE) in 2014 as an Assistant Professor, later advancing to full Professor. She co-directs the MIT Climate & Sustainability Consortium and chairs the MIT Climate Nucleus. Research interests include: sustainable materials systems, recycling-friendly material design, waste mining, and AI-driven materials discovery. She develops models for cost prediction, environmental impact analysis, and policy-relevant supply chain dynamics. Notable contributions include high-throughput zeolite design and battery recycling frameworks. Awards include the Bose Teaching Award (2021), NSF Early Career Award (2018), and Minerals, Metals & Materials Society Early Career Fellowship (2019). Her work emphasizes education and curriculum development, including courses for MIT’s Climate Scholars program. Labs/Teams: Olivetti Group (MIT), MIT Climate & Sustainability Consortium. Active in global sustainability initiatives, focusing on materials for energy transition and climate resilience.

Hongwei WangView profile

Researcher

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +6 more

Dr. Hongwei Wang is a Senior Researcher at Tencent AI Lab Seattle , specializing in applied machine learning for Natural Language Processing and Interconnected Systems . His work bridges Knowledge Graphs , Recommender Systems , and Graph Neural Networks , with a focus on large language models and retrieval-augmented generation. Ph.D. (2018), B.E. (2014) in Computer Science from Shanghai Jiao Tong University Postdoctoral Researcher : Stanford University (2019-2021), University of Illinois Urbana-Champaign (2021-2022) Dr. Wang’s research explores integrating Knowledge Graphs with Graph Neural Networks to enhance recommendation systems, language models, and information retrieval. His work spans Retrieval-Augmented Generation , Representation Learning , and GAN-based Graph Modeling , with recent papers on State-Space Exploration for LLM agents and Semantic Watermarking . His 15 most recent publications (2022-2024) focus on Retrieval Granularity , Interactive Memory , and Agent Systems , with keywords spanning Computer Science , Machine Learning , and Knowledge Graphs . Trends highlight advancements in Token-Level Semantic Matching , Schema-Guided Event Prediction , and Multi-Document Summarization . Scientific Awards: 2020 CCF Outstanding Doctoral Dissertation Award 2018 Google Ph.D. Fellowship Dr. Wang contributes to open-source projects like DKN and RippleNet , with 11 repositories on GitHub. He actively engages in Knowledge Graph Conferences (KDD, WWW, AAAI) and studies Chinese Classical Poetry and Film Arts .

Marat I. LatypovView profile

Assistant Professor

• Materials Science
• Computational Materials Science
• Materials Informatics
• +4 more

Marat I. Latypov serves as Assistant Professor in the Department of Materials Science and Engineering at the University of Arizona's College of Engineering. He is also a member of the Applied Mathematics Graduate Interdisciplinary Program and leads the Materials Informatics Lab. His research spans computational materials science, sustainable alloy design, and machine learning applications for materials development. Dr. Latypov holds a PhD in Materials Science and Engineering from Pohang University of Science and Technology (POSTECH, South Korea, 2014) and a Dipl.-Ing. in Engineering Physics from Ufa State Aviation Technical University (Russia, 2011). His postdoctoral training included appointments at Georgia Tech/CNRS in France and the University of California, Santa Barbara. His research focuses on materials informatics , physics-informed machine learning , and sustainable structural alloys . Key methodologies include graph neural networks for polycrystal mechanics, vision transformers for microstructure representation, and adaptive experimental design for materials optimization. Recent work emphasizes circular economy applications through construction waste recycling and copper mine tailings valorization. Analysis of his publication record reveals strong emphasis on computational microstructure-property linkages (35% of recent work), machine learning for materials design (30%), and sustainable materials processing (25%), with growing integration of large language models for materials knowledge extraction. NSF CAREER Award (2025) : For damage control in recycled aluminum alloys ISTI Distinguished Faculty Scholar (2024) : At Los Alamos National Laboratory Novelis Hackathon First Prize (2021) : Computer vision application Acta Materialia Outstanding Reviewer (2018) Young Researcher Award (2017) : NanoSPD7 Conference Dr. Latypov advises PhD students including Herbold Fellow Zhuocheng Huang and leads projects funded by NSF and the Grantham Foundation. Current initiatives include chalcopyrite leaching optimization for copper mining and graph neural network development for fatigue prediction. His Materials Informatics Lab maintains collaborations with Los Alamos National Laboratory, MIT, and industry partners including Novelis. The lab operates at the intersection of metallurgy , machine learning , and high-performance computing , with capabilities spanning deep learning, Bayesian inference, and cloud-based computational infrastructure. Recent news highlights participation in CODAS-HEP summer school and publication of vision transformer work in Acta Materialia.