جستجوی استاد راهنمای دانشگاهی

Teresa Wangمشاهده پروفایل

مدرس ارشد

• Entity/User Modeling
• Relational/Structural Machine Learning
• Spatial/Temporal Machine Learning
• +۵ مورد دیگر

Dr. Teresa Wang is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in entity/user modeling, relational/structural machine learning, and graph/network analysis. She holds a Ph.D. from the University of Queensland and degrees from Nanjing University. Currently, she directs the Master of Data Science Program and teaches courses like FIT5201 Machine Learning. Her research focuses on social, e-commerce, and health data modeling, with notable projects including the Knowledge Enriched Approach for Effective Personalization (2025–2027) and collaborations on AI in Mental Health and Site Safety. Dr. Wang has co-authored over 59 publications, emphasizing areas like ontology matching and multimodal data analysis. She actively supervises PhD students and contributes to initiatives like the CSIRO Next Generation Graduates Program for clean energy and sustainability. Education: Ph.D. in Computer Science (2017), University of Queensland Master of Computer Science (2013), Nanjing University Bachelor of Software Engineering (2010), Nanjing University Research Interests: Entity modeling, spatio-temporal data analysis, graph mining, recommender systems, and health/medical records mining. She explores applications in social media, e-commerce, and healthcare sectors. Projects: "Knowledge Enriched Approach for Effective Personalization" (2025–2027) "AI for Clean Energy and Sustainability" (2023–2027) "CSIRO Next Generation Graduates Program: AI in Mental Health" (2023–2027) "Large-scale multimodal knowledge management" (2022–2025) Grants & Collaborations: Engaged with CSIRO, Crank Group, and Pola Practice Pty Ltd. Her work aligns with UN SDGs in education and sustainable energy systems. Labs/Teams: Part of the Monash Energy Institute and Monash Data Futures Institute, contributing to interdisciplinary AI and energy research.

Michael Gubanovمشاهده پروفایل

استادیار

• Big Data Management
• Scalable Machine Learning
• Knowledge Graphs
• +۱ مورد دیگر

Dr. Michael Gubanov is an Assistant Professor in Computer Science at Florida State University and founder of BigLab!, specializing in scalable data systems for scientific knowledge discovery. Research: Develops hybrid polystore/LLM systems for cancer research (CancerKG.ORG), COVID-19 knowledge graphs (COVIDKG.ORG), and aging studies (AgingGraph.ORG). Focuses on metadata classification, tabular embeddings, and web-scale knowledge extraction. Funding: Secured $1.8M+ from NSF, Florida Department of Health, and AWS for projects bridging data management and AI. Awards: IEEE ICDE Best Paper (2017), ACM SIGMOD Research Highlight (2018), CACM Research Highlight (2020). Elected to Sigma Xi. Education: PhD in Computer Science (University of Washington); Postdoc at MIT CSAIL.

Antonella Poggiمشاهده پروفایل

دانشیار

• Database Theory
• Data Integration and Exchange
• Knowledge Representation and Reasoning
• +۷ مورد دیگر

Antonella Poggi is an Associate Professor in the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome, holding the position in Computer Science and Engineering (IINF-05/A). She recently obtained the National Scientific Qualification as full professor in July 2024. Her research interests include: Database theory, data integration, and exchange Knowledge representation and reasoning Ontologies, knowledge graphs, and Description Logics Data governance and personal information management Metamodeling and semi-structured data Recent publications (2021-2025) demonstrate a consistent focus on ontology-based data access, with key contributions in query answering, data abstraction, and knowledge graph semantics. Her work bridges theoretical foundations with practical applications, as evidenced by co-founding OBDA Systems Srl. Dr. Poggi has led the MODEUS research project (MIUR SIR) and participated in international collaborations. She is an active member of the academic community, serving as General Chair for IRCDL 2024 and CIKM 2026, and Program Co-Chair for multiple conferences including KEOD and ODOCH. Her academic service includes extensive program committee memberships for top conferences (ICDT, EDBT, AAAI, etc.) and leadership in organizing workshops and conferences in digital libraries and knowledge engineering.

Hongwei Wangمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +۶ مورد دیگر

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 .

Murali Maniمشاهده پروفایل

استاد

• Database Systems
• Data Provenance
• Generative AI
• +۱۲ مورد دیگر

Murali Mani is a Professor in the Department of Computer Science, Engineering, and Physics at the College of Innovation and Technology, University of Michigan-Flint. He is actively involved in teaching courses such as Database Design (CSC 384, CSC 584) and Independent Graduate Study in Computer Science (CSC 591), and serves as Principal Investigator on multiple research grants focused on computing education and data science. His research interests span database systems, data provenance, generative AI for data augmentation, computing education, and the societal impact of technology . He has developed educational tools including epidemiology calculators and market basket analysis modules to support interdisciplinary learning. His work emphasizes integrating computing skills across disciplines such as health sciences and management. The 15 most recent scholarly contributions reflect a strong focus on data management, AI-augmented data curation, educational technology, and the cognitive aspects of learning programming. These publications appear in venues such as VLDB, IEEE FIE, and ACM conferences, with several under review or in preparation for top-tier journals like Communications of the ACM and the VLDB Journal. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: Murali Mani actively mentors students through independent graduate studies and collaborative research projects. He has secured funding from the National Science Foundation (SGER grant on provenance metadata) and internal university sources, including the CIT/CHS Joint Grant and the Office of Research at UM-Flint, supporting projects on civic literacy, computational skills integration, and AI for social science data archiving. Labs and Teams: While no formal lab name is mentioned, Murali Mani leads a research group focused on data systems and computing education, collaborating with colleagues across departments and institutions. He contributes to initiatives such as the Michigan Institute for Data & AI in Society (MIDAS) and the Academic Data Science Alliance (ADSA), and has presented at conferences including IASSIST, FIE, and ICCTAC.

Grant Weddellمشاهده پروفایل

دانشیار

• Database Technology
• Real-Time Applications
• Schema Management
• +۵ مورد دیگر

Grant Weddell is an Associate Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. His research focuses on database technology for real-time applications, including large-scale schema management, information clustering, dependency theory, and query optimization for heterogeneous data sources. He teaches courses such as CS338 (Introductory Databases), CS348 (Advanced Databases), CS446 (Software Engineering), and CS848 (Advanced Database Systems). His research interests emphasize the interplay between description logics and database systems, particularly in optimizing query processing and managing complex schemas. Recent work explores path agreements, functional dependencies, and ontology-mediated querying to enhance data integration and schema management efficiency. Teaching responsibilities include foundational database courses (CS338/348), software engineering (CS446), and advanced topics in information integration (CS848). No scientific awards are explicitly listed, though his contributions to database theory and optimization are extensive.

• Building Information Modeling (BIM)
• Geographic Information Systems (GIS)
• Digital Twin Technology
• +۳ مورد دیگر

Prof. Bernd Domer is an Associate Professor at the Geneva School of Landscape, Engineering and Architecture (HES-SO) specializing in Building Information Modeling (BIM) , Geographic Information Systems (GIS) , and digital transformation of civil engineering . He leads multiple ongoing research projects including CU_OFROU_PAB (CHF278,844) focused on BIM-GIS workflows for noise barriers, and SousEtoile (CHF50,000) developing subsurface prediction models for urban planning. His work addresses critical challenges in software interoperability and point cloud processing for infrastructure digital twins. BA HES-SO in Architecture (HEPIA) BSc Civil Engineering (EPFL) BSc HES-SO in Civil Engineering (HEPIA) MSc HES-SO in Engineering (HES-SO Master) His research explores digital workflows for infrastructure projects, with over 15 recent publications examining topics like: Semantic segmentation of point clouds (2024) IFC standard optimization (2023) Underground confidence level modeling (2021) Swiss BIM implementation frameworks (2020) Construction waste management platforms (2018) He serves as Head of the MIC Group and co-directs the CAS in BIM Coordination . Active in international committees like EG-ICE and Bauen digital Schweiz , his work bridges academic research with practical implementation through collaborations with HEPIA , HEIG-VD , and institutions like the Swiss Federal Roads Office (OFROU) .

Christian Unkelbachمشاهده پروفایل

استاد

• Social Cognition
• Evaluative Judgments
• Information Ecologies
• +۶ مورد دیگر

Christian Unkelbach is Professor of Social Psychology at the Department of Psychology, Faculty of Humanities, University of Cologne, where he leads the Unkelbach Group. His research spans multiple domains of social and cognitive psychology with significant contributions to understanding evaluative processes. His primary research interests include Social Cognition, Evaluative Judgments, Information Ecologies, Stereotypes and Prejudice, Evaluative Learning, Fluency Effects, and Sport Psychology. Unkelbach's work examines how humans process evaluative information in social contexts, with particular focus on the truth-by-repetition effect , evaluative conditioning , and cognitive ecological frameworks that explain why certain information patterns dominate social cognition. His research demonstrates how repetition increases belief in information , how attributes become associated through mere pairings , and why positive information is processed more efficiently than negative information. Analysis of his recent publications reveals a consistent trajectory examining the relativity of social cognition , truth effects across contexts , and metacognitive awareness of cognitive biases . His work increasingly integrates cross-linguistic perspectives and examines real-world applications in media consumption and social judgment. Honorary Doctorate from UCLouvain (2025) ISCON Best Paper Award 2020 Speaker of DFG Research Unit FOR2150 'Relativity in Social Cognition' (2017-2021) Emmy Noether Research Grant (DFG, 2009-2013) Feodor Lynen Research Scholarship (Alexander von Humboldt Foundation) Fulbright Scholarship Unkelbach has advised numerous doctoral researchers including Andreas Miculka, Anne Irena Weitzel, Felix Speckmann, Lea Sperlich, and Tabea Zorn. His research group has secured multiple C-SEB grants for projects examining social cognition phenomena. The Unkelbach Group maintains active collaborations with international researchers across Europe and North America, with recent work funded through the DFG Research Unit on Relativity in Social Cognition. The Unkelbach Group laboratory at Richard-Strauss-Str. 2 in Cologne comprises postdoctoral researchers, doctoral candidates, and student assistants working on experimental paradigms examining evaluative learning, truth effects, and social judgment processes. Current projects investigate cross-linguistic truth effects, metacognitive monitoring of repetition effects, and ecological validity in social cognition frameworks.

Pierre Monninمشاهده پروفایل

پژوهشگر

• Knowledge Graphs
• Semantic Web
• Graph Embedding
• +۴ مورد دیگر

Pierre Monnin is a Junior Fellow in AI at Université Côte d'Azur , conducting research within the Wimmics team at the I3S Laboratory . He also teaches within the EFELIA Côte d'Azur program. His work spans multiple institutions through funded projects like SHACKLE (EU Horizon), ECLADATTA and AT2TA (ANR), with collaborations at Télécom Paris , Università di Bari , and INESC-ID in Lisbon. Previous roles include temporary lecturer at TELECOM Nancy (2023-2024) and researcher at Orange (2020-2023). Research Interests focus on the knowledge graph lifecycle (construction, matching, refinement, mining, discovery) from neurosymbolic AI and analogical reasoning perspectives. He explores Domain knowledge injection into ML models Symbolic-semantics for graph embeddings Zero-shot bootstrapping techniques Context-aware semantic annotation Link prediction with constraint enrichment Life sciences applications Recent scientific awards include: Best Paper Award at ESWC 2024 (Student & Resource Papers) Best Thesis Award from French Association EGC (2022) 1st Prize (Accuracy Track) at Semantic Web Challenge (2021) His teaching portfolio covers AI fundamentals for foreign languages, marketing, and adult education programs, with specialized courses in Semantic Web technologies NoSQL databases XML tools Compiler implementation He supervises multiple PhD students and interns on topics involving neurosymbolic refinement , knowledge reconciliation , and analogical reasoning . Key software contributions include: KGPrune - Web application for thematic Wikidata subgraph extraction PyGraft - Synthetic knowledge graph generation tool DAGOBAH UI - Semantic table interpretation interface He also maintains datasets like PGxLOD and YAGO4-LP for pharmacogenomics and link prediction.

• Data Quality
• Database Systems
• Data Profiling
• +۷ مورد دیگر

Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.

• Artificial Intelligence
• Machine Learning
• Activity Recognition
• +۱۲ مورد دیگر

Gita Reese Sukthankar is a Professor in the Department of Computer Science at the University of Central Florida (UCF) , where she directs the Intelligent Agents Lab . Her research focuses on activity and plan recognition , with applications in multi-agent systems, robotics, and human-robot interaction. She earned her Ph.D. from the Robotics Institute at Carnegie Mellon University and joined UCF in fall 2007. Research Interests: Her work spans activity recognition , intent inference , multi-agent coordination , and human-robot teams . She has applied these techniques to domains such as adversarial games (e.g., military simulations, Unreal Tournament), assistive technologies, and cooperative robotics. Her research integrates AI, machine learning, and probabilistic models to understand and predict complex team behaviors. Publication Trends: Her publications emphasize spatio-temporal modeling , probabilistic graphical models (e.g., HMMs, CRFs) , and multi-agent plan recognition . She frequently publishes in top venues like AAMAS, AAAI, and ICRA, with a focus on robust recognition of team behaviors, transfer learning, and real-world AI applications. Scientific Awards: NSF CAREER Award (2009) AFOSR Young Investigator (2009) ONR Summer Faculty Fellow (2008) UCF Faculty Excellence for Doctoral Mentoring (2012) CECS Dean's Research Professorship (2013) AAAI Senior Member (2021) ACM and IEEE Senior Member Advising and Grants: She mentors graduate students in AI and robotics and has led research funded by DARPA, AFOSR, and ONR. Her lab develops systems for intelligent agents that can understand and collaborate with humans. She has served on numerous program committees and editorial boards, including ACM Transactions on Autonomous and Adaptive Systems . She teaches courses such as Intelligent Systems , Robotics , and Machine Learning , and has been recognized for both research and teaching excellence. Labs and Teams: She leads the Intelligent Agents Lab at UCF, which focuses on data-driven social informatics and AI for human-agent teams. Her group collaborates with researchers in robotics, computer vision, and cognitive science to build adaptive, intelligent systems.

• Database Systems
• Query Optimization
• Index Tuning
• +۷ مورد دیگر

Surajit Chaudhuri is a Researcher at Microsoft , with a career spanning decades in database systems and data management . He has received the prestigious SIGMOD Edgar F. Codd Innovations Award (2011) for his contributions to query optimization , index tuning , and data lakes . Research Interests : His work focuses on database tuning , approximate query processing , fuzzy similarity joins , automated data transformations , and machine learning integration for scalable data systems. Recent Publications : In 2025, his research includes Auto-Test for unsupervised error detection in tables, Esc for budget-aware index tuning, and MMTU for multi-task table understanding benchmarks. Earlier works in 2024–2023 address spreadsheet formula recommendation , low-overhead index filtering , and time-series pattern recognition . Scientific Impact : He has co-authored influential papers in SIGMOD , VLDB , and IEEE Transactions , shaping practices in cloud databases , query optimization , and self-service BI . His collaborations span institutions like Microsoft, MIT, and ETH Zurich.

Felix Naumannمشاهده پروفایل

استاد

• Data Profiling
• Data Quality
• Data Integration
• +۴ مورد دیگر

Professor Felix Naumann is Chair for Information Systems at the Hasso Plattner Institute (HPI) at the University of Potsdam in Germany, where he leads the Information Systems research group. He is also Coordinator for MSc. Data Engineering and for the Data and AI track for MSc. Computer Science, and Speaker of the Research School on Data Science and Engineering. His extensive academic career includes visiting positions at CIRES Centre in Brisbane (2024-2025), SAP's Innovation Center (2020), AT&T Research (2016), and QCRI (2012). Professor Naumann's research focuses on data profiling, data cleansing, data integration, and data quality assessment with over 200 scientific publications. His work spans theoretical foundations and practical applications, with significant contributions to data quality metrics, metadata extraction, and AI-driven data preparation techniques. His research group develops prominent systems like Metanome for data profiling and Metis for data quality assessment. His recent publications demonstrate continued innovation in data management, with a growing intersection between traditional database research and AI applications. The 15 most recent papers show increasing focus on data quality for AI applications (KITQAR), multimodal data analysis (MELArt), and practical data cleaning frameworks that bridge database systems with machine learning pipelines. GI Dissertationspreis 2000 for best computer science PhD thesis IBM Research Division Award, 2002 Distinguished ACM member since 2021 Distinguished Reviewer Award - SIGMOD 2023 Best paper award at EDBT 2024 for Tasheeh paper Professor Naumann has successfully advised over 30 PhD students who now hold prominent positions at institutions like MIT, Google, Snowflake, and universities worldwide. His research has been funded by major grants including DFG Nachwuchsforschergruppe (2003-2008), IBM SUR Grant (2007), and DFG Forschergruppe Stratosphere (2010-2016). He leads the Information Systems research group at HPI, which includes PostDocs, PhD students, and student assistants working on projects like Metanome, Metis, KITQAR, and Janus, focusing on data profiling, quality assessment, and change exploration in data systems.

Jeff Heflinمشاهده پروفایل

دانشیار

• Semantic Web
• Ontology Development
• Artificial Intelligence
• +۳ مورد دیگر

Jeff Heflin is an Associate Professor in the Department of Computer Science and Engineering at Lehigh University, and Director of the Cognitive Science Program. He holds a PhD from the University of Maryland, College Park, and a B.S. from The College of William and Mary. His research focuses on AI for Data Management, Scalable Reasoning, and Data Exploration, with significant contributions to Semantic Web technologies like SHOE, DAML+OIL, and OWL. Education: Ph.D. in Computer Science, University of Maryland, 2001 M.S. in Computer Science, University of Maryland, 1999 B.S. in Computer Science, The College of William and Mary, 1992 Research Interests: Heflin’s work advances Semantic Web infrastructure, including ontology development, scalable reasoning systems, and data integration. His contributions include foundational benchmarks like LUBM and innovations in entity matching (DAME), table search (StruBERT), and neural-augmented logical reasoning. Awards: Winner of the Billion Triple Challenge (ISWC 2012) Best Paper Award, ISWC 2004 Ruth and Joel Spira Award for Excellence in Teaching (2017) Service & Leadership: Served as Vice-President of the Semantic Web Science Association (SWSA), Editorial Board member of the Artificial Intelligence Journal , and General Chair of ISWC 2017. Active in conference organization (CIKM 2020, COLING 2022) and journal special issues on Semantic Web evaluation. Labs & Collaborations: Leads the Semantic Web and Agent Technologies (SWAT) Lab at Lehigh, focusing on AI-driven data management and scalable reasoning systems. Collaborates on NSF-funded projects like CRISPS (cell-centric image similarity search) and Domain-Agnostic Dataset Search.

• Database Systems
• Data Analytics
• Machine Learning in Databases
• +۳ مورد دیگر

Jignesh M. Patel is a Professor at the University of Wisconsin, Madison, WI, USA , with over 25 years of contributions to database systems, data analytics, and hardware-aware query processing. His work bridges theoretical advancements with practical systems engineering. Research Interests span: Database systems optimization (query processing, transaction management) Hardware acceleration for analytics (eBPF, PIM, GPUs) Machine learning integration in databases (feature selection, model optimization) Efficient data structures (hashing, encoding, indexing) Multi-tenant and cloud database management Recent Work focuses on kernel-embedded databases (BPF-DB, 2025), memory-efficient dataframe processing (SplitDF, 2024), and algorithmic-hardware co-design for dense retrieval (DReX, 2025). He has pioneered techniques for adapting to data skew (VIP Hashing, 2022), leveraging static analysis in R optimization (ROSA, 2017), and rethinking benchmarking paradigms. Collaborations include key partnerships with: Systems researchers (Andrew Pavlo, José F. Martínez) Machine learning experts (Arun Kumar, Kevin Skadron) Education-focused colleagues (Adalbert Gerald Soosai Raj, Richard Halverson) Industry leaders (David J. DeWitt, Microsoft Research)