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James CaverleeView profile

Professor

• Machine Learning
• Recommender Systems
• Information Retrieval
• +7 more

James Caverlee is a Professor in the Department of Computer Science and Engineering at Texas A&M University, and a Visiting Researcher at Google DeepMind. He holds a Ph.D. from Georgia Institute of Technology (2007), and degrees from Stanford University and Duke University. His research focuses on trustworthy, efficient, and personalized machine learning systems, including Recommender Systems, Large Language Models (LLMs), and bias mitigation. Recent work emphasizes fairness, efficiency, and transparency in AI, with applications in conversational systems, speech, and medical QA. He has been recognized with an NSF CAREER Award, AFOSR Young Investigator Award, and DARPA Young Faculty Award. His group has published extensively at top venues like KDD, EMNLP, WWW, SIGIR, and WSDM. Education: Ph.D., Computer Science, Georgia Tech, 2007 M.S., Computer Science & Engineering-Economic Systems, Stanford University, 2001–2000 B.A., Economics, Duke University, 1996 Research Interests: Machine Learning, Recommender Systems, NLP, LLMs Bias Mitigation, Efficiency, Personalization Social Computing, Information Retrieval Awards: NSF CAREER Award (2012) AFOSR Young Investigator (2012) SIGIR Test of Time Honorable Mention (2022) Grants: Supported by NSF, AFOSR, DARPA, and industry (Google, Amazon). Labs/Teams: InfLab research group at Texas A&M, collaborating with Google DeepMind. His publications address topics like adversarial attacks on LMs, gender bias in podcasts, and efficient recommendation algorithms. Recent trends show focus on generative AI for personalization and ethical AI challenges.

Kamesh MunagalaView profile

Associate Professor

• Algorithm design
• Discrete optimization
• Approximation algorithms
• +8 more

Kamesh Munagala is an Associate Professor of Computer Science at Duke University, where he has been employed since 2004. His research spans theoretical computer science with applications in e-commerce, databases, data analysis, and networks. He serves as an Area Editor for PeerJ Computer Science and has held leadership roles including Director of Graduate Studies for the Duke CS department from 2012 to 2015. Education: PhD in Computer Science from Stanford University (2003) BTech from IIT Bombay (1998) Munagala's research focuses on algorithm design and discrete optimization, particularly in scenarios with uncertain inputs. His work encompasses approximation algorithms, online algorithms, and algorithmic game theory. Recent research has concentrated on two main themes: (1) Persuading optimizers or learners toward certain objectives through information revelation and pricing, and (2) Ensuring fairness to groups based on proportionality and stability in resource allocation and societal decision making contexts. His theoretical work has practical applications in designing data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Analysis of Munagala's recent publications reveals a strong trend toward interdisciplinary research at the intersection of computer science, economics, and social choice theory. His work shows increasing sophistication in handling fairness constraints across multiple dimensions, particularly in societal decision-making contexts like school assignment, participatory budgeting, and redistricting. There is also a notable expansion into the emerging area of large language models for combinatorial optimization, reflecting the field's evolution toward AI-assisted algorithmic solutions. His research consistently bridges theoretical foundations with real-world applications requiring nuanced approaches to resource allocation under uncertainty. Scientific Awards: NSF CAREER Award (2008) Alfred P. Sloan Research Fellowship (2009) Best paper award at WWW 2009 conference Munagala has served as Director of Graduate Studies for the Duke CS department from 2012 to 2015 and was a Visiting Research Professor at Twitter in 2012. His research has been supported by prestigious grants including the NSF CAREER award. He actively mentors students, with many of his publications featuring student co-authors across his two main research thrusts. His work addresses fundamental challenges in computing efficient solutions under uncertainty, designing pricing and incentive mechanisms for selfish agents, and developing fairness-preserving algorithms for societal decision making. Munagala leads research projects in two broad categories: proportionality in resource allocation and societal decision making, and asymmetric information and persuasion in learning and optimization. His work on proportionality explores fairness concepts like proportionality or core-stability across various societal contexts, while his research on asymmetric information addresses how parties with incomplete information collaborate or compete to achieve optimization objectives through strategic information revelation.

Kamesh MunagalaView profile

Professor

• Theoretical Computer Science
• Approximation Algorithms
• Online Algorithms
• +7 more

Kamesh Munagala is a Professor of Computer Science at Duke University's Trinity College of Arts & Sciences since 2016. His primary affiliation is with the Computer Science Department, where he conducts research in theoretical computer science with a focus on algorithm design, fairness, and economic mechanisms. His educational background includes a Ph.D. (2003) and M.S. (2002) from Stanford University. Munagala has maintained a strong research trajectory with continuous publications spanning over two decades. Munagala's research focuses on theoretical computer science, particularly approximation algorithms, online algorithms, and computational economics. His recent work centers on two main themes: (1) persuading optimizers or learners toward specific objectives through information revelation and pricing, and (2) achieving group fairness based on proportionality and stability in resource allocation contexts. His research addresses practical applications including data network design, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. His publication record shows consistent output with multiple high-impact papers in top conferences like STOC, SODA, and ICML. Recent publications (2024-2025) demonstrate his focus on algorithmic fairness, differential privacy, and mechanisms for resource allocation. His work often intersects computer science with economics, game theory, and social choice theory. Best Paper Award at WINE '18 for 'A simple mechanism for a budget constrained buyer' Best Paper Award at WWW '09 for 'Hybrid keyword search auctions' Munagala has secured significant research funding including an NSF Medium grant (2024-2027) for 'Responsive Optimization for Algorithmic Decision Systems' and an NSF Small grant (2021-2025) for 'Algorithm and Incentive Design for Modern Resource Allocation Platforms'. His teaching portfolio includes graduate and undergraduate courses in algorithm design, randomized algorithms, and algorithmic game theory, with courses scheduled through Fall 2025.

• Interactive AI
• Human-Centered AI
• Fairness in AI
• +3 more

Elizabeth M. Daly is a Research Scientist at IBM Research Laboratory, Dublin, and an Adjunct Assistant Professor at Trinity College Dublin's School of Computer Science and Statistics. Her work focuses on interactive AI , human-centered design , and fairness in algorithmic systems . She contributes to AI governance and LLM safeguarding through projects like AutoFair and Granite Guardian. Ph.D. in Computer Science (2007), Trinity College Dublin Thesis: Social Network Analysis for Routing in Disconnected Delay-Tolerant MANETs Her research interests span: Interactive AI : Facilitating AI-human negotiation for common objectives Trustworthy AI : Addressing fairness, accountability, and transparency in industrial applications Explainable AI : Developing tools like AIMEE for model exploration and editing She serves on the program committees of top conferences (RecSys, IUI, WWW, UMAP, ICWSM) and the Royal Irish Academy’s committee on Engineering and Computer Science. Notable scientific award: ACM Distinguished Member . Projects: AutoFair : Human-compatible automation of fairness in AI AIMEE : AI model explorer and editor tool Usage Governance Advisor : Translating AI intent into governance frameworks She leads the Interactive AI Group at IBM Research Europe - Ireland.

• Information Retrieval
• Machine Learning
• Data Mining
• +5 more

Professor Klaus Berberich is a faculty member at htw saar (Saarland University of Applied Sciences), where he serves as Professor in the Databases & Information Systems department within the Faculty of Engineering. He is the Laboratory manager of the software laboratory (SWL) and Chairman of the examination boards for Practical Computer Science, Communication Informatics and Production Informatics. His research focuses on Information Retrieval, Machine Learning, Data Mining, and Web Archives, with significant contributions to knowledge graphs, temporal information retrieval, and neural information retrieval models. Professor Berberich has developed innovative approaches for quantity extraction from web tables, structuring text into tables, and knowledge graph querying. His publication record shows a consistent trend toward increasingly sophisticated neural approaches to information retrieval, evolving from traditional temporal search techniques to modern deep learning models. Recent work demonstrates strong integration of knowledge graphs with neural information retrieval systems, particularly in handling quantities and temporal aspects of information. Professor Berberich has received numerous prestigious awards throughout his career: 2020: Test of Time Award, ECIR 2020 2018: Honorable Mention for Best Poster Award, WWW 2018 2017: Prominent Paper Award, Artificial Intelligence Journal 2014: Highly Commended Poster Presentation Award, IIiX 2014 2013: Honorable Mention for Best Paper Award, CIKM 2013 2011: Best Demo Award, WWW 2011 2009: Best Late-Breaking Result Award, WSDM 2009 As an active researcher and educator, Professor Berberich serves on numerous program committees for major conferences including WSDM, CIKM, SIGIR, and ICTIR. He has been a consistent reviewer for prestigious journals in the field and is a member of the executive committee of the Information Retrieval specialist group of the German Informatics Society. His teaching portfolio includes courses in Databases, Information Retrieval, Data Science, Machine Learning, and Deep Learning. Professor Berberich leads the software laboratory (SWL) at htw saar and has been instrumental in developing research infrastructure for knowledge-centric tasks, including the GYANI indexing infrastructure. His research group has made significant contributions to temporal information retrieval, particularly in the context of web archives and news archives.

Jakob Linaa JensenView profile

Associate Professor

• Anthropology
• Democracy and power
• Internet
• +10 more

Jakob Linaa Jensen is an Associate Professor at Aarhus University's School of Communication and Culture, Department of Media and Journalism Studies. His work focuses on digital democracy, social media analysis, and internet theory. University: Aarhus University School: School of Communication and Culture Department: Department of Media and Journalism Studies Academic Rank: Associate Professor Jensen's research explores the intersection of internet studies, media theory, and political participation. Key areas include social media's impact on elections, digital privacy strategies, and platform-specific cultural analysis. His fingerprint includes collaborations on social media metrics and methodological approaches. Recent publications analyze longitudinal trends in digital communication, including the micro-macro effects of internet use in Danish elections. He has contributed to debates on online privacy, digital ethics, and social media governance.

• Information Retrieval
• Data Science
• Text Analytics
• +5 more

Christina Lioma is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen . She has held academic positions including Associate Professor (2014-2018) and Freja Fellow/Assistant Professor (2012-2013) at the same institution. M.Hons (University of Glasgow, 2001) M.Sc. (University of Manchester, 2003) Ph.D (University of Glasgow, 2007) Her research focuses on Information Retrieval , Text Analytics , and Recommender Systems within Applied Machine Learning and Natural Language Processing . Recent work examines fairness-relevance tradeoffs in recommendation systems and neural mechanisms for knowledge conflict tracing. Recent publications in Nature Communications and top conference proceedings (WWW, SIGIR) explore hybrid computation architectures, brain-based language generation, and fairness evaluation metrics. She actively participates in academic conferences as organizer and speaker, including the European Conference on Information Retrieval.

• Machine Learning
• Spatial Data Mining
• AI in Mental Health
• +4 more

Dr. Saida Elmi is an Assistant Professor Adjunct at Yale University School of Medicine and an Assistant Professor at the University of New Haven. She holds a Ph.D. in Computer Science from the National School of Mechanics and Aerotechnics (2017), followed by postdoctoral research at Korea University of Technology and Education (2017–2018) and National University of Singapore (2018–2021). Her research focuses on AI applications in healthcare, transportation systems, and spatial data mining. She developed the Automated Test of Embodied Cognition (ATEC), an AI-driven system using motion capture and machine learning for mental disease detection. Key contributions include predicting taxi fares, travel times, and energy consumption in transportation networks using deep learning architectures like RNNs and CNNs, and designing group-oriented recommendation systems for points-of-interest (POIs) using graph convolution networks and attention mechanisms. Education : Ph.D., Computer Science, National School of Mechanics and Aerotechnics (2017) Postdoctoral Research, Korea University of Technology and Education (2017–2018) Postdoctoral Research, National University of Singapore (2018–2021) Her research interests span machine learning, spatial data mining, and AI ethics. She has pioneered frameworks for spatio-temporal data analysis, including a transfer learning approach for travel time prediction in data-scarce regions. Her work on energy consumption modeling and POI prediction has been published in top-tier conferences like WWW, MOBIQUITOUS, and ECIR. Dr. Elmi’s interdisciplinary projects integrate AI with healthcare and urban systems, emphasizing automated cognitive assessment and smart city technologies. Her current efforts focus on refining action recognition algorithms for clinical applications and optimizing recommendation systems for group mobility decisions.

• Semantic Web
• Linked Data
• Blockchain
• +5 more

John Domingue is a prominent researcher at The Open University, UK , specializing in Semantic Web technologies, Blockchain applications, and Educational Technology. He has co-authored over 211 publications since 1987, with recent work focusing on decentralization, data privacy, and AI integration. Current research themes include: Decentralized learning analytics Blockchain-based micro-accreditation Semantic knowledge graphs AI for education Trusted decentralized systems His recent publications (2023-2025) demonstrate ongoing leadership in Semantic Web and Blockchain research applied to education and data governance. Key collaborations include Alexander Mikroyannidis, Aisling Third, and Carlos Pedrinaci. Notable projects: FORGE Toolkit for remote experimentation ScienceMiles digital currency for researchers ADA system for automated learning analytics Decentralized trust frameworks Semantic PETs (Privacy Enhancing Technologies) While formal awards aren't listed, his extensive publication record in top venues (WWW, ISWC, ESWC) and editorial contributions establish him as a key figure in Semantic Web research. His work bridges technical innovation with practical educational applications, particularly in decentralized architectures.

Shweta JainView profile

Research Fellow

• Randomized Algorithms
• Approximation Algorithms
• Combinatorial Optimization
• +2 more

Shweta Jain is a Research Fellow at the University of Utah, specializing in randomized and approximation algorithms, combinatorial optimization, graph mining, and algorithms for massive data. Previously, she held postdoctoral positions at the University of Illinois, Urbana-Champaign, and a PhD in Computer Science from the University of California, Santa Cruz, where she was advised by Prof. Seshadhri Comandur. SIGKDD Best Dissertation Runner-Up, 2021 Computing Innovation Fellowship, 2021 CSE Best Dissertation Award, UCSC, 2020 Rising Stars Workshop, Berkeley, 2020 Her research focuses on advancing algorithms for efficient graph analysis, particularly for maximal clique detection, cycle counting, and weighted clique decomposition. She develops practical frameworks like YACC and PEANUTS, with applications in large-scale network optimization and real-world data mining. Her work appears in top conferences including SIAM ACDA, WWW, ITCS, and WSDM. Shweta contributes to open-source algorithm implementations for clique counting and network analysis, with repositories hosted on platforms like Bitbucket. She has received multiple accolades, including Best Paper and Best Poster awards.

Georgios ZervasView profile

Associate Professor

• Marketing
• Computer Science
• Economics
• +3 more

Georgios Zervas is an Associate Professor of Marketing at Boston University Questrom School of Business and founding member of the Faculty of Computing & Data Sciences, where he serves as Director of Online Initiatives. Previously, he was a Simons Postdoctoral Fellow at Yale University and an Affiliate at Harvard's Center for Research on Computation and Society. His research focuses on empirical studies of internet markets at the intersection of marketing, computer science, and economics. Key areas include online reputation systems, sharing economy dynamics, consumer search behavior, and digital advertising. His work frequently analyzes large-scale datasets from platforms like Yelp, Airbnb, and Groupon to examine market design, regulatory impacts, and consumer behavior. Zervas' publications reveal a consistent focus on platform economics and digital marketplaces, with recent work applying machine learning to optical networks and analyzing search data for consumer preference modeling. His research demonstrates methodological versatility across econometrics, network analysis, and behavioral experiments. Finalist for the 2018 Paul E. Green Award Finalist for the 2018 John D. C. Little Award As Director of Online Initiatives for BU's Faculty of Computing & Data Sciences, Zervas leads digital education strategy. His professional service includes editorial roles at Marketing Science, Journal of Marketing, and Journal of Marketing Research, plus program committee positions for major conferences including ACM EC and WWW. He has contributed significant public datasets including the Daily Deals dataset and Swoopo auction data.

Jingyi WangView profile

Assistant Professor

• Software Engineering for AI
• Formal Methods
• Security
• +4 more

Jingyi Wang is an Assistant Professor at Zhejiang University (ZJU), China, leading the IS2 (Intelligent System Security) Lab. He holds a US-equivalent tenure-track position and has established himself as a prominent researcher in software engineering for AI and formal methods applied to security. His educational background includes: Bachelor's degree from Xi'an Jiaotong University (2013) PhD from Singapore University of Technology and Design (2018) Research Fellow at National University of Singapore (2019-2020) Wang's research focuses on developing principled methodologies for building trustworthy AI models and secure systems. His work spans testing, verification and repair of AI models/systems, AI safety and security, and formal reasoning of security. He has developed innovative approaches for neural network testing, verification, and repair, with particular emphasis on explainability, fairness, and robustness. His publication record shows a clear progression from foundational work on concolic testing (2018) to increasingly sophisticated methods for evaluating and improving AI systems, with recent focus on large language models and decentralized identity systems. His research bridges formal methods with practical software engineering challenges in AI security. His notable achievements include: Two ACM SIGSOFT Distinguished Paper Awards (ICSE 2018 and 2020) ACM SIGSOFT Research Highlights Best Paper Award Runner-up at IEEE TDSC 2024 Wang actively contributes to the research community as Program Committee member for top conferences including CCS, ICSE, ISSTA, ASE, WWW, and AAAI. He serves as PC Co-chair for ICFEM 2025, Large Model Safety Workshop 2025, and SAC-SVT 2026. His leadership in the IS2 Lab demonstrates his commitment to advancing intelligent system security through rigorous research.

DENG YangView profile

Assistant Professor

• Natural Language Processing
• Information Retrieval
• Large Language Models
• +7 more

Dr. Yang Deng is a tenure-track Assistant Professor at the School of Computing and Information Systems, Singapore Management University, and a Lee Kong Chian Fellow. Previously, he was a Postdoctoral Research Fellow at NExT++ (National University of Singapore). His research focuses on Natural Language Processing, Information Retrieval, and Large Language Models, with special interests in Proactive Conversational AI, Trustworthiness of LLMs, and Human-Centered Information Seeking. He has published over 40 papers in top-tier venues including ACL, EMNLP, WWW, and SIGIR. PhD from The Chinese University of Hong Kong (2023) Research Advisor to CHEANG Chi Seng Research Domains: Natural Language Processing Information Retrieval Large Language Models Human-Agent Interaction Digital Transformation Trustworthy AI Scientific Recognition: Lee Kong Chian Fellowship Google South Asia & Southeast Asia Research Awards 2024 EMNLP 2024 Outstanding Area Chair NeurIPS 2024 Best Reviewer

Gong ChengView profile

Professor

• Big Data Search
• Knowledge Graphs
• LLM Inference
• +3 more

Gong Cheng is a Professor at the School of Computer Science, Nanjing University. His research focuses on big data search, knowledge graphs, and LLM inference with real-world applications in search engines, decision support systems, and question answering systems. He has published extensively in top-tier venues like WWW, SIGIR, and TKDE. Research Interests: Big data search (dataset fusion, retrieval, ranking, summarization, recommendation) Graph search algorithms (efficient approximation algorithms for Group Steiner Tree problems) LLM inference optimization (task-specific enhancements, critical domain problem-solving) Knowledge-based RAG (multi-source heterogeneous knowledge retrieval for LLMs) Scientific Awards: ISWC 2023 Best Research Paper Nominee ISWC 2021 Best Resource Paper Nominee ESWC 2020 Best Research Paper Award ESWC 2020 Best Resource Paper Nominee COLING 2018 Area Chair Favorites ISWC 2014 Best Research Paper Nominee ISWC 2011 Best Research Paper Nominee Academic Service: PC co-chair of ISWC 2024 and CCKS 2023 Associate PC chair of CIKM 2025 Publicity co-chair of CCF BigData 2025/2023 Co-organized EYRE (2018-2020) and SumPre (2015-2016) Area chair/senior PC member for ISWC, CIKM, ESWC, and others

Tim SeppeltView profile

Researcher

• Graph Theory
• Theoretical Computer Science
• Algorithmic Complexity
• +3 more

Dr. Tim Seppelt is a postdoctoral researcher at the IT University of Copenhagen , working under the mentorship of Prof. Radu Curticapean. Previously, he earned his PhD from RWTH Aachen University with supervisors Prof. Martin Grohe and Prof. Michael Schaub. His research focuses on theoretical computer science, specifically homomorphism indistinguishability , a framework connecting graph isomorphism, quantum information, and logical equivalences. Current Role: Postdoc in Theoretical Computer Science, ITU Education: PhD in Computer Science, RWTH Aachen University Tim's work addresses algorithmic meta-theorems for homomorphism indistinguishability over minor-closed and treewidth-bounded graph classes. He has extended Lovász-type results to CMSO2 logic, resolved complexity conjectures for the Lasserre hierarchy, and classified quantum group-induced indistinguishability relations. His research spans quantum computing , graph algorithms , and descriptive complexity , often intersecting with applications in machine learning and finite model theory. Recent publications include a 2025 paper on quantum group-driven homomorphism indistinguishability and a 2024 journal article on logical equivalences and forbidden minors. He presented at workshops like the Graph Learning Meets TCS (Simons Institute, 2025) and delivered tutorials on finite model theory at Finite and Algorithmic Model Theory 2025 (Les Houches, France).