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Lihui LiuView profile

Assistant Professor

• Natural Language Processing
• Knowledge Graph Reasoning
• Large Language Models
• +2 more

Lihui Liu is an Assistant Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research focuses on neural-symbolic AI, integrating knowledge graphs with large language models for enhanced reasoning capabilities. Research Focus: Dr. Liu develops methods for complex knowledge graph reasoning, conversational question answering, and neural-symbolic integration. His lab creates algorithms that combine structural knowledge representations with language model capabilities for interpretable AI systems. Recent work demonstrates strong emphasis on knowledge graph applications (60% of publications) and language model enhancement (30%). Notable contributions include logical query decomposition techniques and attention-based graph neural network architectures. Mentoring: Actively recruiting PhD students to work on NSF-funded projects involving knowledge-guided AI systems. Professional service includes program committees for major AI conferences and editorial contributions.

Riccardo CantiniView profile

Assistant Professor

• Deep Learning
• Large Language Models
• Sustainable AI
• +3 more

Riccardo Cantini is an Assistant Professor (RTDA) at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria. He holds a European Ph.D. in Information and Communication Technologies (2023) and has been a visiting researcher at the Barcelona Supercomputing Center (BSC-CNS, 2021-2022). His research focuses on deep learning (Large Language Models, sustainable AI) and big social data analysis targeting politically polarized data and high-performance distributed systems. Education: B.Sc. (2016), M.Sc. (2019), and Ph.D. (2023) in Computer Engineering from the University of Calabria. Research interests include: Large Language Models and their ethical deployment Sustainable AI and energy-efficient edge computing Political polarization analysis using social media data Optimization of data-intensive workflows in distributed environments Key projects include the FAIR initiative (Green-Aware AI), eFlows4HPC (HPC workflows), and ASPIDE (Exascale data processing). He has authored/co-authored over 30 publications, including works on bias detection in LLMs and explainable AI in healthcare. Awards: 2024 Top 3 Best PhD Thesis in Big Data & Data Science (CINI), 2022 Editor's Choice article in Big Data and Cognitive Computing . Teaching roles include courses on Business Intelligence, High-Performance Computing, and Operating Systems. He has advised over 40 theses in AI, NLP, and big data. Professional services: Guest Editor for Big Data and Cognitive Computing , Program Chair of Green-Aware AI workshops, and reviewer for top journals/conferences (ICLR, IEEE BigData, etc.).

Mohammad Al HasanView profile

Professor

• Data Mining
• Machine Learning
• Social Network Analysis
• +6 more

Mohammad Al Hasan is a Professor of Computer Science at the Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis. He previously worked at eBay Research Labs and holds a Ph.D. from Rensselaer Polytechnic Institute. His research focuses on data mining, machine learning, and social network analysis, with contributions to graph embeddings, network sampling, and natural language processing for knowledge graphs. He leads the DDML Research Group at IU Indy. Education: Ph.D. Computer Science, Rensselaer Polytechnic Institute (2009) M.S. Computer Science, University of Minnesota Twin Cities (2002) B.Sc. Computer Science and Engineering, Bangladesh University of Engineering and Technology (1998) Research Interests: Bioinformatics, Information Retrieval, Machine Learning, Social Network Analysis, and Algorithm Development for Text and Relation Embeddings. Al Hasan has authored over 100 research articles and received prestigious awards including the NSF CAREER Award. His work emphasizes interdisciplinary applications of data science. Labs/Teams: Founder of the Database, Data Mining & Machine Learning (DDML) Research Group.

Noorbakhsh Amiri GolilarzView profile

Research Professor

• Artificial Intelligence
• Image Processing
• Deep Neural Networks
• +2 more

Noorbakhsh Amiri Golilarz is Assistant Research Professor in Computer Science and Engineering at Mississippi State University. His research focuses on artificial intelligence applications in image processing, computer vision, and medical diagnostics. Key research areas include deep neural networks for image denoising, medical image analysis, and cybersecurity for AI systems. Recent work explores adversarial robustness, medical knowledge graphs, and generative model evaluation. Publications demonstrate strong methodological innovations in optimization algorithms and deep learning architectures applied across domains including healthcare, renewable energy, and industrial quality control.

Ashraf AboulnagaView profile

Professor

• Database Systems
• Cloud Computing
• Big Data
• +3 more

Ashraf Aboulnaga is a Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA). He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison and has held prior roles as Chief Scientist at the Qatar Computing Research Institute, Associate Professor at the University of Waterloo, and Research Staff Member at IBM Almaden Research Center. His research focuses on database systems, cloud computing, and graph analytics, with recent emphasis on cloud-native database architectures and scalable graph processing. Education: Ph.D. (2002), M.S. (1999) from University of Wisconsin-Madison; B.S./M.S. (1993/1996) from Alexandria University. Affiliations: ACM Distinguished Scientist (2015), IEEE Senior Member (2009). Research interests include cloud database systems, distributed transaction processing, and knowledge graph integration. He has authored over 100 publications in top venues like SIGMOD, VLDB, and ICDE, and received Best Paper Awards at SoCC 2015 and VLDB 2011. Grants: Current funding includes NSF travel support for ICDE 2024 and a $1.4M NSF MRI grant for DiscourseLab (2025). Past grants include DOE support for soil microbiome data integration. Service: Associate Editor for VLDB Journal, PVLDB, and Distributed and Parallel Databases. Served as Program Committee member for SIGMOD, ICDE, and EDBT. Industry collaborations include advising on cloud DBMS architectures and distributed systems.

• Data Mining
• Machine Learning
• Network Science
• +3 more

Tina Eliassi-Rad is the Joseph E. Aoun Professor at Northeastern University's Khoury College of Computer Sciences. She serves as core faculty at the Network Science Institute and external faculty at the Santa Fe Institute and Vermont Complex Systems Institute. Her research bridges data mining, machine learning, and network science with emphasis on trustworthy AI systems and societal impacts. Her current research programs focus on: Trustworthy Network Science: Enhancing explainability, robustness, and fairness in graph-based machine learning Just Machine Learning: Analyzing AI within broader sociotechnical systems to mitigate systemic risks Dr. Eliassi-Rad teaches graduate courses in Machine Learning with Graphs and has organized numerous workshops including the 2024 ICML Workshop on Humans, Algorithmic Decision-Making and Society. Her interdisciplinary work connects computer science with social science, biology, and policy studies.

Soha HassounView profile

Professor

• Machine Learning
• Systems Biology
• Metabolic Engineering
• +5 more

Soha Hassoun is a Professor at Tufts University with primary appointment in the Department of Computer Science and secondary appointments in the Department of Electrical and Computer Engineering and the Department of Chemical and Biological Engineering. Her interdisciplinary work bridges computer science, engineering, and biology to develop computational tools for biological systems analysis and design. Education: Ph.D. from University of Washington (1997) M.S. from Massachusetts Institute of Technology (1988) B.S. from South Dakota State University (1986) Soha Hassoun's research focuses on the intersection of Machine Learning and Biology, with particular emphasis on developing ANALYSIS + DESIGN tools to advance (RE-)DESIGNING BIOLOGY. Her work spans Systems Biology, Metabolic Engineering, and computer-aided design for integrated circuits. She develops MACHINE LEARNING models specifically tailored for biological data, drawing inspiration from her prior experience in circuit design and design automation for electronic systems where she contributed seminal ideas for FinFET devices and power grids for 3D integrated circuits. Her recent publications demonstrate a strong trend toward applying advanced machine learning techniques, particularly graph neural networks and transformer models, to biological problems including metabolite annotation, enzyme-substrate interaction prediction, and metabolic network analysis. Her work increasingly focuses on creating standardized benchmarks and frameworks for evaluating computational methods in metabolomics. Soha leads the Hassoun Lab, which is dedicated to developing computational tools that provide insight into complex biological systems and enable building novel biological components to produce useful chemicals and therapeutics. The lab's research spans from fundamental machine learning algorithm development to practical applications in biotechnology.

Nalini RathaView profile

Professor

• Computer Vision
• Artificial Intelligence
• Biometrics
• +3 more

Nalini Ratha is a SUNY Empire Innovation Professor and Professor in the Department of Computer Science and Engineering at the University at Buffalo. She is affiliated with the School of Engineering and Applied Sciences. Her research focuses on computer vision, artificial intelligence, biometrics, and ensuring fairness and trust in AI systems. Education: PhD in Computer Science (Michigan State University), MTech in Computer Science and Engineering (IIT Kanpur), and BTech in Electrical Engineering (IIT Kanpur). Research interests include privacy-preserving AI, FHE (Fully Homomorphic Encryption) applications, adversarial defense, and ethical AI. Her work addresses challenges like secure biometric systems, deepfake detection, and medical data privacy. Notable contributions include developing FHE-based frameworks for secure inference, enhancing AI fairness, and combating identity fraud through biometric analysis. Awards: NAI Fellow (2021), ACM Distinguished Member (2015), IAPR Fellow (2008), and IEEE Fellow (2007). Her publications explore cutting-edge topics like encrypted medical diagnostics, secure UAV navigation, and robust deep learning models resistant to adversarial attacks.

Yan ZhangView profile

Lecturer

• Recommendation Systems
• Data Mining
• Machine Learning
• +5 more

Dr. Yan Zhang is a Lecturer in Information Technology within the Faculty of Science and Technology at Macquarie University. She completed her PhD in recommendation systems from the University of Technology Sydney (UTS) in 2022 and has established herself as an active researcher with 17 publications spanning from 2016 to 2025. Her academic profile shows consistent research output with multiple publications each year, demonstrating ongoing productivity and engagement in her field. She is registered to supervise postgraduate research, indicating her role in mentoring graduate students. Dr. Zhang's research interests center around recommendation systems, data mining, machine learning, and transfer learning. Her work spans both theoretical foundations and practical applications, with a strong emphasis on solving cold-start problems, cross-domain recommendations, and efficient recommendation techniques. The fingerprint of her research shows significant contributions in areas including User, Hashing, Collaborative Filtering, Recommender Systems, User Preference, Algorithms, Matrix Factorization, and Boolean Function. Her research contributes to UN Sustainable Development Goals through technological innovation in data science and information systems. Analysis of Dr. Zhang's publication history reveals an evolving research trajectory with increasing sophistication in recommendation system methodologies. Her early work focused on discrete techniques and hashing methods for efficient recommendations, while recent publications demonstrate expansion into federated learning, multi-agent systems, and medical applications. The thematic progression shows movement from foundational recommendation techniques toward more complex, interdisciplinary applications while maintaining core expertise in user preference modeling and efficient recommendation algorithms. Dr. Zhang has accumulated 268 citations with an h-index of 7, indicating growing scholarly impact in her field. Her publications appear in reputable venues including IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, and proceedings of major conferences like IEEE ICDE. She has demonstrated consistent collaboration with researchers across institutions, as evidenced by her co-authorship patterns across multiple publications. As a postgraduate research supervisor, Dr. Zhang contributes to academic training in information technology. Her research program appears to focus on both theoretical aspects of recommendation systems and practical applications in cybersecurity, healthcare, and content delivery. The diversity of her recent work suggests she leads or participates in multiple research streams addressing different application domains while maintaining core methodological expertise in recommendation technologies.

Peter DologView profile

Associate Professor

• Recommender Systems
• Web Engineering
• Machine Learning
• +5 more

Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design. His research focuses on Recommender Systems, Web Engineering, Machine Learning, and Health Informatics. He leads projects such as the MEco Medical Ecosystem and the IWIS Intelligent Web and Information Systems initiative. His work emphasizes explainable AI, knowledge graphs, and social media analytics for public health monitoring. Notable contributions include hypergraph-based recommendation models and sensitive information detection in legal documents. Projects include MEco (medical event surveillance) and IWIS (web systems personalization), funded by EU and national grants. His research spans 148 publications, with recent work on explainable recommendation systems and neural networks for medical data analysis. Collaborations include the University of Vienna and international conferences like ECIR and RecSys. Media coverage highlights his work on using social media for epidemic detection. Key research areas include recommendation systems with attention mechanisms, knowledge graph integration, and ethical AI applications in healthcare. His methodologies bridge machine learning with user-centric design, emphasizing transparency and explainability in AI-driven systems.

Alessia AntelmiView profile

Assistant Professor

• Complex networks
• Hypergraphs
• Hypergraph representation learning
• +4 more

Alessia Antelmi is an Assistant Professor (RTD-A) in the Department of Computer Science at the University of Turin, where she contributes to the Parallel Computing Group. Her work bridges theoretical computer science with real-world applications in social and technological systems. Her educational background includes: Ph.D. in Computer Science with honors from the University of Salerno, focusing on diffusion phenomena modeling using high-order networks Her research centers on complex network structures and human behavior in digital environments: Develops hypergraph representation learning techniques for modeling complex relationships Investigates social influence diffusion and user behavior evolution in online networks Designs agent-based simulations for large-scale system analysis Creates tools for data literacy and knowledge graph education Her approach integrates mathematical modeling with computational experimentation to address challenges in social dynamics and information systems. Analysis of her 15 recent publications (2023-2025) reveals three dominant trends: hypergraph-based methods for complex data representation, large-scale analysis of online communities using agent-based models, and educational tools for data literacy. Her work increasingly incorporates LLMs for social network analysis while maintaining strong theoretical foundations in network science. Her scientific recognition includes: Best paper nominee at CSEDU 2023 for open data education research Best paper nominee at AsiaSim 2019 for Rust-based simulation frameworks She has secured competitive research funding: 55,200 DKK grant under the COCOONS project (2023) led by Prof. Luca Maria Aiello at IT University of Copenhagen Erasmus+ Traineeship grant (2018) to work with Prof. John Breslin at Galway's Data Science Institute Though no advisees are listed, her collaborative publications suggest active mentorship in computational research. She actively contributes to the Parallel Computing Group at the University of Turin, developing frameworks like SWH-Analytics for large-scale software analysis and HypergraphRepository for community-driven data curation.

Agnese PintoView profile

Assistant Professor

• Artificial Intelligence
• Internet of Things
• Semantic Web
• +2 more

Agnese Pinto is an Assistant Professor at Polytechnic of Bari, teaching courses including Operating Systems and Operating Systems for Management Engineering. Her research focuses on Artificial Intelligence, Internet of Things, Semantic Web, and Cloud-Edge Computing, with applications in logistics, sensor networks, and smart city technologies. She is affiliated with SisinfLab, contributing to projects in cloud-edge intelligence and IoT systems. Her publications demonstrate a strong emphasis on integrating semantic technologies with distributed systems, particularly in IoT and edge computing environments. Recent work explores AI-driven logistics optimization, serverless architectures for sensor networks, and knowledge representation for autonomous systems. The consistent themes include semantic reasoning, distributed intelligence, and real-time system efficiency across transportation, UAV operations, and urban computing domains. She maintains active participation in academic events such as the International Conference on Web Engineering (ICWE) and Cyber Security Challenge initiatives.

Aparna GuptaView profile

Professor

• Quantitative Finance
• Risk Management
• Financial Decision Support
• +5 more

Aparna Gupta is a Professor of Quantitative Finance at the Lally School of Management, Rensselaer Polytechnic Institute (RPI). She previously co-founded and co-directed the NSF IUCRC Center for Research toward Advancing Financial Technologies (CRAFT) and served as Associate Dean for Academic Affairs at Lally School. Her research focuses on financial decision support systems, risk management, and applying machine learning/mathematical modeling to financial engineering and renewable energy sectors. She has held a visiting researcher position at the U.S. Securities and Exchange Commission (SEC) and contributed to over 80 indexed publications across Computer Science, Decision Sciences, and Mathematics. Education: PhD (2000) and MS (1997) in Operations Research from Stanford University; B.Sc. and M.Sc. in Mathematics from Indian Institute of Technology (IIT) Kanpur. Research interests include blockchain technology applications, climate change impact analysis, renewable energy risk mitigation, and financial regulatory frameworks. Notable work includes developing frameworks for assessing risk culture in banks/insurance firms and creating algorithms for predicting financial performance from SEC filings. Her book Risk Management and Simulation remains a key reference in the field. Awards include the 2018 Financial Management Association Best Paper Award and 2019 FRAP Conference Best Paper Award. She serves on the editorial boards of quantitative finance journals and the GARP FRM Advisory Board. Her interdisciplinary projects involve collaborations with federal agencies (SEC), industry partners, and international academic networks. Current focus areas include AI-driven fraud detection, cross-border payment innovations, and systemic risk management in interconnected financial systems.

Matteo PalmonariView profile

Associate Professor

• Knowledge Graphs
• Natural Language Processing
• Semantic Web
• +3 more

Matteo Palmonari is an Associate Professor affiliated with the University of Milano-Bicocca, where he leads the Insid&s LAB. His research focuses on advancing knowledge graphs, natural language processing (NLP), and semantic web technologies to address challenges in data integration, entity linking, and criminal justice applications. He specializes in developing tools like ABSTAT, SemTUI, and LamAPI for semantic enrichment and analysis of structured and unstructured data. Palmonari's work emphasizes practical applications in public administration, legal systems, and criminal investigations. His recent efforts include exploring large language models (LLMs) for entity disambiguation, zero-shot classification in procurement, and semantic interpretation of tabular data. He has contributed to projects such as the EU Business Graph ontology and semantic document management systems for governmental contexts. His publications span topics like temporal embeddings, cross-lingual ontology matching, and data quality assessment. The Insid&s LAB collaborates on initiatives like the 'innograph' knowledge graph for AI-driven innovation and semantic tools for event-based marketing analytics (BEEO and EW-shopp projects). Palmonari's research bridges theoretical advancements with real-world applications, particularly in improving data interoperability and decision-making through semantic technologies.

Mohammed ZakiView profile

Professor

• Data Mining
• Machine Learning
• Graph Learning
• +5 more

Mohammed Zaki is a Professor and Department Head of Computer Science at Rensselaer Polytechnic Institute (RPI). He holds a Ph.D. in Computer Science from the University of Rochester (1998) and dual B.S./M.S. degrees from Angelo State University and the University of Rochester. His research focuses on data mining, machine learning, and graph-based methods, with applications in bioinformatics, financial analytics, and personal health. He has authored over 300 publications and 6 patents, including the textbook Data Mining and Machine Learning (2nd Ed., Cambridge University Press, 2020). Education: Ph.D. (1998) and M.S. (1995) from University of Rochester; B.S. (1993) in Computer Science and Mathematics from Angelo State University. Research Interests : Zaki's work spans novel data mining techniques for graph-structured/textual data, scalable graph algorithms (e.g., GRAIL, IBMGenerator), and applications in finance, healthcare, and bioinformatics. He has pioneered methods for frequent pattern mining, sequence analysis, and knowledge graph construction. Recognition : Recipient of NSF CAREER Award, DOE Early Career Award, and Fellowships from IEEE, ACM, AAAS, and SIAM. His research is supported by NSF, DARPA, NIH, DOE, and industry partners like IBM, Google, and NVIDIA. Grants & Leadership : Leads the CRAFT Center for Financial Technology research and serves on ACM SIGKDD's Board of Directors. Has chaired major conferences including SIGKDD, CIKM, and IEEE BigData. Active in open-source projects, maintaining tools like IBMGenerator for synthetic data creation and GRAIL for graph reachability queries. Labs/Teams : Directs research in RPI's Data Mining Lab, focusing on scalable algorithms and real-world applications. Collaborates with interdisciplinary teams on health informatics, financial systems analysis, and molecular graph learning.