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Christos MakrisView profile

Associate Professor

• Data Structures
• Information Retrieval
• Data Mining
• +5 more

Christos Makris is an Associate Professor in the Department of Computer Engineering and Informatics at the University of Patras, Greece. His academic career spans over two decades with significant contributions to computer science, particularly in data structures, algorithms, and information systems. He maintains active research collaborations and supervises graduate students in his areas of expertise. Dr. Makris's research spans several key areas in computer science with a strong focus on efficient data organization and processing. His work encompasses Data Structures , Information Retrieval , Data Mining , String Management and Processing Algorithms , Computational Geometry , Internet Technologies , Bioinformatics , and Multimedia Databases . His interdisciplinary approach bridges theoretical computer science with practical applications across various domains including web technologies, bioinformatics, and emergency response systems. Analysis of Dr. Makris's publication record reveals a consistent research trajectory focused on efficient algorithms for information management. His work demonstrates evolution from foundational data structure research in the 1990s to more applied work in web technologies, social media analysis, and machine learning applications in recent years. A notable pattern is his ability to adapt core algorithmic techniques to emerging application domains while maintaining theoretical rigor. Dr. Makris maintains an impressive scholarly record with over 3,000 citations, an h-index of 29, and an i10-index of 71 according to Google Scholar metrics. These indicators reflect the significant impact of his research within the computer science community. As an active faculty member, Dr. Makris maintains regular office hours on Tuesdays from 18:00-20:00 and Thursdays from 12:00-14:00. He is accessible via email at makri@ceid.upatras.gr or makri@upatras.gr for academic inquiries and student supervision.

Xin PengView profile

Professor

• Software Analytics
• Intelligent Software Development
• Microservice
• +4 more

Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.

Nazli GoharianView profile

Clinical Professor

• Information Retrieval
• Text Mining
• Natural Language Processing
• +1 more

Nazli Goharian is a Clinical Professor of Computer Science at Georgetown University and Associate Director of the Information Retrieval Lab. She holds a PhD from Florida Institute of Technology and joined Georgetown in 2010 after industry experience and previous academic positions at Illinois Institute of Technology. Education: PhD Computer Science, Florida Institute of Technology (2001) MSc Computer Science, George Mason University (1995) BSc Computer Science, Dortmund University (1992) Her research spans information retrieval, text mining, and natural language processing with applications in health/medical domains. She focuses on developing computational methods for medical search, mental health analysis from social media, clinical text summarization, and adverse drug reaction detection. Her recent publications (2020-2016) predominantly focus on neural ranking models, transformer architectures for document retrieval, and clinical NLP applications. Notable trends include work on BERT-based re-ranking, zero-shot multilingual retrieval, and ontology-aware medical summarization. Awards & Honors: EMNLP 2017 Best Long Paper Award COLING 2018 Honorable Mention & Area Chair Favorite Julia Beveridge Award for Faculty (IIT, 2009) Multiple Teaching Excellence Awards (2002-2007) Research Leadership: She has supervised 6 PhD students to completion with placements at leading institutions. Secured over $500,000 in research funding from NSF, Adobe, and international partners. Founded the Semi-Annual Graduate Research Presentation Days at Georgetown and served as Program Chair for ECIR 2024. She leads the Information Retrieval Lab which focuses on developing novel algorithms for efficient document retrieval, cross-lingual search, and specialized applications in healthcare text analysis.

• Machine Learning
• Explainable AI (XAI)
• Deep Learning
• +8 more

Haoyi Xiong is an active academic researcher in artificial intelligence, machine learning, and data science, with extensive publications in top-tier journals and conferences including IEEE TPAMI, NeurIPS, ICML, KDD, and AAAI. His work spans explainable AI, graph neural networks, diffusion models, remote sensing, and large language models. Research Interests: Explainable AI (XAI) and model interpretability Graph Neural Networks and contrastive learning Diffusion models and generative AI Medical and remote sensing image analysis Large language models and autonomous agents Learning to rank and web search His recent publications (2023–2025) show a strong trend toward self-supervised learning , model robustness , and integration of LLMs with structured data and knowledge graphs . He frequently collaborates with researchers from major tech and academic institutions. Scientific Awards: No explicit awards mentioned in the provided text. Advising and Grants: While no direct mention of students or grants, his role as a senior author on numerous papers suggests he advises graduate students and likely leads funded research projects in machine learning and AI. His work on frameworks like COLTR , GS2P , and MUSCLE indicates leadership in developing scalable AI systems. Labs and Teams: Though not explicitly stated, his frequent collaboration with Jiang Bian, Dejing Dou, and Dawei Yin suggests affiliation with a well-established AI research lab or industry-academia partnership focused on data mining, intelligent systems, and large-scale learning.

• Information Retrieval
• Crowdsourcing
• Human-in-the-loop AI
• +5 more

Dr. Gianluca Demartini is a leading researcher in Human-in-the-loop AI Systems with significant contributions to Crowdsourcing , Information Retrieval , and Generative AI applications. His work bridges Machine Learning and Human-Computer Interaction , focusing on Bias Management , Fact-Checking , and Ethical AI . Major Affiliations : L3S Research Center, ScienceWISE platform, and collaborations with institutions like University of Queensland and University of Padua Over 15 years, his research has explored Crowdsourcing Quality Control (Mechanical Cheat 2012), Entity Ranking (2008-2013), and Semantic Search . Recent work (2024-2026) focuses on Generative AI Impacts in domains like Media Literacy , Data Curation , and Visual Analytics . Scientific Recognition : Best Paper Award (Top 1.4%) at ICTIR 2023 Best Short Paper Award (Top 0.6%) at ECIR 2020 Honorable Mention (Top 2%) at CSCW 2020 Best Demo Award at ISWC 2011 3rd Best Paper at LA-WEB 2008 His 15 most recent publications (2024-2026) demonstrate expertise in LLM-based Content Moderation , Immersive Data Visualization , and Trustworthy AI Systems . He has pioneered methods for Bias Detection in Wikipedia (2013), Entity Ranking (2008-2013), and Human-AI Collaboration frameworks. His work consistently addresses ethical challenges in AI for Social Good and Responsible Data Science .

Sharad MehrotraView profile

Professor

• Data Management
• Distributed Systems
• Database as a Service
• +7 more

Sharad Mehrotra is a Distinguished Professor at the University of California, Irvine (UCI), leading the Center for Emergency Response Technologies (CERT) and directing the NSF-funded RESCUE project. He previously served at the University of Illinois, Urbana-Champaign, and holds a Ph.D. from the University of Texas at Austin (1993). His research focuses on data management, IoT systems, privacy-preserving technologies, and smart spaces, with contributions to frameworks like TIPPERS and MARS. Education: Ph.D., Computer Science, University of Texas at Austin, 1993 Research Interests: His work bridges database systems, security, and IoT, emphasizing privacy in smart environments. Notable projects include sentient space technologies for disaster response, cryptographic methods for encrypted data queries, and semantic IoT integration. Recent efforts address privacy in multi-owner data systems and resilient community water infrastructure. Awards & Recognition: ACM Fellow (2024) SIGMOD Best Paper (2001), DASFAA Best Paper (2004) NAVWAR Innovation Award (2021) Outstanding Graduate Mentor (2005) Grants & Leadership: As RESCUE PI, he managed $12.5M NSF funding, developing crisis-response software deployed by emergency agencies. Collaborations include the Cal-IT2 institute (UCSD/UCI) and the US Navy’s TIPPERS platform. He co-leads initiatives like the NSF Civic Innovation Challenge for disaster resilience in aging communities. Labs & Teams: Directs UCI’s Information Systems Group and CERT, fostering interdisciplinary research with 60+ members. His teams produce open-source tools (e.g., SEMIoTIC, PrivacySphere) and engage in global partnerships via Fulbright Visiting Scholar programs.

• Information Access
• Information Retrieval
• Natural Language Processing
• +3 more

Tetsuya Sakai is a Professor at the School of Fundamental Science and Engineering within Waseda University's Faculty of Science and Engineering. His work focuses on information access, retrieval, and natural language processing, with a particular emphasis on evaluation frameworks for search systems. Affiliations: Waseda University (Faculty of Science and Engineering, School of Fundamental Science and Engineering) Academic Rank: Professor Research Interests : Dr. Sakai's research spans four key areas: (1) Information Access —designing systems for direct and immediate information delivery, (2) Search Evaluation —developing metrics like Height-Biased Gain and hierarchical intent-based diversity measures, (3) Fairness in IR —pioneering frameworks for group fairness in conversational search, and (4) Statistical Reform —advocating Bayesian methods and robust experimental design. His work also addresses privacy inconsistencies in mobile apps and cognitive biases in LLMs. Scientific Awards : Notable recognitions include induction into the SIGIR Academy (2023) , ACM Distinguished Member (2018) , ACM Senior Member (2016) , and multiple DEIM/FIT/CSS Best Paper Awards . He has received teaching honors like the Waseda Presidential Teaching Award (2016) and WASEDA e-Teaching Award (2018) . Article Trends : Recent publications highlight: Advancements in LLM-assisted relevance assessments and hallucination diagnostics for tool-augmented models Conversational search fairness through multi-level evaluation frameworks and group diversity metrics Innovations in 3D medical reconstruction from clinical data and multimodal uncertainty modeling Statistical rigor via randomization tests , credible intervals , and topic set design Privacy analysis in mobile app descriptions and cognitive bias studies in search interaction

• Information Retrieval
• Search Engines
• Human-Computer Interaction for Information Retrieval
• +3 more

Mark D. Smucker is a Professor in the Department of Management Science and Engineering at the University of Waterloo, cross-appointed with the David R. Cheriton School of Computer Science (Faculty of Mathematics). His research focuses on interactive information retrieval systems, including search engines and recommendation systems, aiming to enhance evaluation methods for better prediction of human search performance. He co-organized the TREC Health Misinformation Track (2019–2022) and currently co-leads the TREC DRAGUN Track, addressing health misinformation and trustworthiness assessment in news. Education: PhD (Computer Science, UMass Amherst, 2008), MSc (Computer Science, UW-Madison, 1996), BSc (Physics & Computer Science, Iowa State, 1994). Research interests include design/analysis of interactive IR systems, evaluation frameworks, and human-computer interaction. He has been recognized with the ACM SIGIR 2012 Best Paper Award and teaching excellence awards from the University of Waterloo. Teaching includes courses like Search Engines (MSCI/MSE 541/720) and Databases/Software Design (MSCI 245). Active in organizing TREC tracks and publishing over 50 peer-reviewed articles.

Kaize DingView profile

Assistant Professor

• Data Mining
• Machine Learning
• Large Foundation Models
• +7 more

Kaize Ding is an Assistant Professor in Statistics and Data Science at Northwestern University, leading the REAL Lab and affiliated with the IDEAL Institute. He holds a Ph.D. in Computer Science from Arizona State University (2023) under Prof. Huan Liu, with prior degrees from Beijing University of Posts and Telecommunications. His research focuses on reliable AI systems for autonomous decision-making, knowledge-guided algorithms using GNNs/LLMs, and applications in healthcare, environmental science, and cybersecurity. Collaborations include Google Brain, Microsoft Research, and Amazon Alexa AI. Education: Ph.D. in Computer Science, Arizona State University (2023) M.S. and B.S., Beijing University of Posts and Telecommunications Research Interests: Developing robust AI for decision-making under uncertainty Graph-based machine learning for anomaly detection and network analysis Large language models (LLMs) integrated with domain-specific knowledge Cross-domain applications in healthcare diagnostics, environmental monitoring, and cybersecurity Recent Activities: Received Amazon Research Award and Google Research Grant NeurIPS 2025 Area Chair and ARR Area Chair Postdoc opening in AI4Health for swallowing disorder research Recent publications at AAAI, NeurIPS, EMNLP, and KDD Lab & Team: The REAL Lab focuses on advancing AI through interdisciplinary projects. Current students include Ruiyao Xu (PhD), Qingcheng Zeng (co-advised), and over 10 master's/undergraduate researchers. Alumni have moved to top PhD programs at UVa, UIC, and JHU.

Jamie CallanView profile

Professor

• Information Retrieval
• Neural Search Engine Architectures
• Knowledge Graphs
• +5 more

Jamie Callan is a Professor at Carnegie Mellon University's Language Technologies Institute (School of Computer Science), where he leads research in Information Retrieval and Neural Search Architectures . He teaches advanced courses on search engine design and mentors students in multiple programs. Research Focus: Federated retrieval, knowledge graph integration in search, ClueWeb dataset development, and neural approaches to document ranking Leadership: Past SIGIR Treasurer/Chair, Co-founding Editor of Foundations and Trends in IR, former TOIS Editor-in-Chief His recent work explores: Neural Retrieval: Latent vocabulary for sparse systems, hypothetical documents for dense vector retrieval Dataset Innovation: Maintenance and distribution of ClueWeb09, ClueWeb12, and ClueWeb22 datasets Search Efficiency: Selective search architectures with 90% reduced computational costs Scientific Recognition: International ACM SIGIR Conference Leadership Co-founding Editor-in-Chief, Foundations and Trends in IR Former Editor-in-Chief of ACM TOIS Dr. Callan's Lemur Project has produced Indri/Galago search engines and supported TREC evaluations through dataset contributions.

João MagalhãesView profile

Professor

• Vision and Language Understanding
• Multimodal Information Understanding
• Multimodal Conversational AI
• +5 more

João Magalhães is a Full Professor in the Department of Computer Science at the Faculty of Science and Technology, Universidade NOVA de Lisboa, Portugal. He serves as Group Coordinator of the Multimodal Systems Group at the NOVA Laboratory for Informatics and Computer Science and leads the NOVASearch research group at FCT/UNL. His research focuses on vision and language information understanding, with particular emphasis on multimodal information understanding, multimodal conversational AI, multimedia search and summarization, temporal and memory models, and social media information quality. His work spans both theoretical foundations and practical applications across web, social media, and clinical domains. Analysis of his recent publications reveals a strong trajectory in multimodal conversational AI systems, with increasing sophistication in handling both voice and visual inputs. His research has evolved from foundational work in cross-modal embeddings to advanced large language models for dual-goal conversational settings, demonstrating consistent innovation in the field of multimodal understanding. 1st prize winner of the second Alexa TaskBot Challenge (2023) Award-winning solution in the Alexa TaskBot Challenge (2022) Best paper award at the Portuguese NLP conference (PROPOR) (2020) Best paper nominations at ACM conferences (2018) Professor Magalhães has advised numerous graduate students through the NOVASearch group and has secured substantial research funding through projects including Amazon Alexa TaskBot Challenge (2021-2023), iFetch (2020-2023), SmartyFlow (2017-2020), COGNITUS (2016-2019), GoLocal (2016-2020), QSearch (2012-2015), ImTV (2010-2013), and CS4SE (2010-2013). He actively serves the research community as ACM Multimedia 2026 Program Committee Chair and has held leadership roles in numerous conferences including ACM Multimedia 2022 General Chair and ECIR2020 General Chair. He leads the Multimodal Systems Group within the NOVA Laboratory for Informatics and Computer Science, where his team develops cutting-edge solutions for multimodal understanding with applications in conversational AI, multimedia search, and social media analysis.

• Big Data Management
• Data Mining
• Graph Learning
• +9 more

Dr. YANG, Renchi is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University, Faculty of Science. He earned his BEng in Software Engineering from Beijing University of Posts and Telecommunications and his PhD in Computer Science from Nanyang Technological University, followed by a postdoctoral fellowship at the National University of Singapore. His research is centered on developing efficient algorithms and systems for large-scale data management and analysis. His research interests include: Big Data Management and Analysis Graph Learning and Network Embedding Databases and Data Management (especially graph query processing and similarity search) The Web and Information Retrieval (search, ranking, recommendation, web mining) Data Mining and Machine Learning (social network analysis, text mining, large language models) Dr. Yang’s recent publications span top conferences such as KDD, SIGMOD, WWW, ICDE, and AAAI, focusing on scalable graph clustering, network embedding, GNNs, and LLM integration. His work emphasizes algorithmic efficiency, scalability, and practical applications in real-world graph data. Scientific honors include: VLDB 2021 Best Research Paper Award 2022 ACM SIGMOD Research Highlight Award Best Paper Award Nominee in WWW 2022 Honorable mention as best PC member in WWW 2022 Dr. Yang actively mentors PhD and research students, currently supervising several RPg students including LIN Xiaoyang, LAI Yurui, and ZHENG Haoran. He has secured research funding enabling PhD scholarships and research assistant positions. He serves on the program committees of major conferences like VLDB, KDD, WWW, and SIGIR, and reviews for journals including TKDE and VLDBJ. He is a key member of the Database Research Group at HKBU, which has published extensively in top venues, including 8 papers at SIGMOD 2023. His research lab, the LAGAS Group, focuses on large-scale graph analytics and systems. The team is actively working on projects involving graph clustering, embedding, GNNs, and integration with large language models. Dr. Yang is currently recruiting PhD students for 2026 and research assistants for 2025, indicating active and expanding research operations.

Latifur KhanView profile

Professor

• Data Mining
• Cybersecurity
• Machine Learning
• +2 more

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).

Charles LingView profile

Professor

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +4 more

Charles Ling is a Professor of Computer Science at Western University, holding the title of Science Distinguished Research Professor. He also serves as Director of the Data Mining and Business Intelligence Lab and Associate Scientist at the Lawson Health Research Institute. His academic background includes a B.Eng. (CS and EE) from Shanghai Jiao Tong University and MSc/PhD from the University of Pennsylvania (UPenn). Research interests span machine learning, deep learning, AI, and healthcare informatics, with notable contributions to the GlucoGuide diabetes management system. He has authored over 220 peer-reviewed papers and a book titled Crafting Your Research Future , focusing on academic career development. Awarded Fellow of the Canadian Academy of Engineering (CAE) and recipient of the First Prize for Best Clinical Research Presentation (2011). Active in grants (NSERC, FedDev, Mitacs) and organizational roles in top conferences (KDD, ICDM). Supervises 5 PhD and 4 MSc students, with notable advisees including Harry Zhang and Victor Sheng. Leverages AI in education to enhance children's cognitive abilities through video-based programs like Power Thinking , approved by Curriculum Services Canada. His work integrates machine learning with healthcare, finance, and software engineering.

Guido ZucconView profile

Professor

• Information Retrieval
• Health Search
• Formal Models of Search
• +15 more

Guido Zuccon is a Professorial Research Fellow at the School of Electrical Engineering and Computer Science , The University of Queensland (UQ), where he leads the Information Engineering Lab (ielab) . He serves as the AI Director for the Queensland Digital Health Centre (QDHeC) and is an Affiliate Professor at the UQ Centre for Health Services Research . He was previously a Lecturer and Senior Lecturer at Queensland University of Technology and a Postdoctoral Fellow at CSIRO. His research spans Information Retrieval , Health Search , Formal Models of Search , and Health Data Science , with a strong focus on consumer health search, cohort identification, clinical decision support, and systematic review automation. He has pioneered work on search interaction, semantic models, and the evaluation of retrieval systems in health contexts. His recent publications highlight a strong trend toward leveraging large language models (LLMs) for zero-shot retrieval, federated search, dense retrieval, and query formulation. His work integrates advanced neural methods with practical applications in healthcare, including systematic review automation and clinical AI. He frequently publishes at top venues such as SIGIR, ECIR, and WSDM, often in collaboration with key researchers like Bevan Koopman, Shengyao Zhuang, and Harry Scells. ARC DECRA Fellow (2018–2020) Best Paper Awards at AIRS 2017, CLEF 2016, ALTA 2015, ECIR 2012 Best Reviewer Award at ECIR 2014 Principal Investigator on ARC Discovery Projects and MRFF grants Guido Zuccon actively supervises a large cohort of PhD students, primarily in areas related to neural information retrieval, health search, and systematic review automation. He has led significant research projects funded by the ARC, Google, Microsoft, GRDC, and CSIRO. He is a key organizer of international evaluation labs such as the CLEF eHealth Consumer Health Search task and the TREC 2019 Decision Track. He leads the ielab , a vibrant research group focused on information retrieval and data science, and contributes to major open-source initiatives like Big Brother , a tool for logging user interactions in web studies.