جست‌وجوی استاد و پژوهشگر

• Data Science
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

András A. Benczúr is a prominent researcher affiliated with the Hungarian Academy of Sciences, specifically with SZTAKI (Institute for Computer Science and Control). He has maintained a prolific research career spanning multiple decades with over 160 publications documented in the dblp database. His work primarily focuses on data science, machine learning, and network analysis, with significant contributions to recommender systems and big data analytics. Dr. Benczúr's research interests include Data Science, Machine Learning, Recommender Systems, Network Analysis, Big Data Analytics, Graph Theory, Information Systems, Web Mining, Stream Processing, and Artificial Intelligence. His work demonstrates a consistent focus on developing theoretical foundations while addressing practical applications in various domains. He has made significant contributions to understanding information networks, developing efficient algorithms for data streams, and creating novel approaches to recommendation systems. His recent publications (2023-2025) show a continued focus on cutting-edge topics including neural network uncertainty, AI-driven communication for 6G networks, network embedding applications for public health, and theoretical evaluations of explainable AI methods. These works demonstrate his ability to bridge theoretical computer science with practical applications across multiple domains including telecommunications, public health, and blockchain technology. Dr. Benczúr has collaborated extensively with researchers across Hungary and internationally, with notable long-term collaborations with Bálint Daróczy (30 joint publications), Róbert Pálovics (27 joint publications), and Domokos Kelen (14 joint publications). His work appears in prestigious venues including IEEE Access, ICLR, WWW, RecSys, and numerous IEEE and ACM conferences. His research has practical applications in diverse areas including social network analysis, telecommunications infrastructure, public health monitoring, and blockchain technology. The Hexa-X project publications indicate his involvement in shaping the future of 6G communication standards through AI integration, while his work on network embeddings has been applied to vaccine skepticism detection, demonstrating the societal impact of his research.

Soumen Chakrabartiمشاهده پروفایل

استاد

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +۳ مورد دیگر

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

• Algorithmics
• Machine Learning
• Mathematical Modeling
• +۲ مورد دیگر

Flavio Chierichetti is a Full Professor in the Department of Computer Science at Sapienza University of Rome, Italy. His research focuses on algorithmics, machine learning, and mathematical modeling, particularly in the contexts of social networks and the Web. His work integrates theoretical and applied approaches to address complex challenges in these domains. Research Interests : His primary areas of investigation include the design and analysis of algorithms, application of machine learning techniques, and the development of mathematical models to explore social network dynamics and web-based systems. These fields reflect his expertise in bridging computational theory with real-world data science problems. Scientific Awards : Google Focused Research Award Grants and Academic Engagement : PRIN project 20229BCXNW Additionally, he serves as an associate editor for ACM Transactions on Algorithms and has held leadership roles in program committees of major conferences like KDD, WWW, NeurIPS, and SODA. His teaching responsibilities include courses on Algorithms, Advanced Algorithms, and Algoritmi e Complessità.

Martin Heppمشاهده پروفایل

استاد

• Web Science
• Internet of Things
• Big Data and Data Science
• +۵ مورد دیگر

Martin Hepp is Professor of Web Science and Digitalization at the University of the German Federal Armed Forces Munich, where he leads the E-Business and Web Science Research Group. His academic career spans multiple prestigious institutions including the University of Innsbruck, Florida Gulf Coast University, and IBM Research. He holds a PhD in Business Information Systems from the University of Würzburg where he graduated Summa Cum Laude. His research focuses on shared data structures at Web scale, with particular expertise in Semantic Web technologies, ontology engineering, and data quality management. He is best known for developing the GoodRelations vocabulary for e-commerce, which since November 2012 has become the e-commerce core of schema.org, the official data markup standard adopted by major search engines including Google, Yahoo, Bing, and Yandex. His work is widely implemented by companies ranging from Google and Yahoo to BestBuy, Kmart, Volkswagen, and Renault, as well as tens of thousands of smaller businesses. His publication record shows a consistent focus on practical applications of Semantic Web technologies in business contexts, with recent work emphasizing Linked Data applications, product classification systems, and e-commerce standards. His research demonstrates strong industry impact, particularly through the adoption of GoodRelations as a foundational standard for web commerce data. Linked Data-a-thon Award (4th prize) at ISWC 2009 ACM Senior Member appointment (2009) FIT-IT outstanding research proposal award (2nd place, 2007) Dissertation Award from Alcatel SEL Foundation (2004) Dissertation Award from Unterfränkische Gedenkjahrstiftung (2004) Professor Hepp has served on numerous editorial boards including the International Journal on Semantic Web and Information Systems, and has been a program committee member for over sixty conferences including ECIS, EKAW, ESWC, ISWC, and WWW. He has organized more than fifteen workshops and conference tracks on conceptual modeling and Semantic Web topics. His work bridges academic research with practical business applications, particularly in the areas of e-commerce standards and semantic data interoperability.

Jiameng Puمشاهده پروفایل

پژوهشگر

• Cybersecurity
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Jiameng Pu is a Researcher in the Department of Computer Science at Virginia Tech, part of the College of Engineering. Their research focuses on cybersecurity, deep learning, and adversarial machine learning, with notable contributions to deepfake detection and AI robustness. Pu holds a Ph.D. in Computer Science and Applications from Virginia Tech (2017–2022) and a B.E. in Computer Science and Technology from Wuhan University (2013–2017). Education : Ph.D. in Computer Science and Applications, Virginia Tech (2017–2022) B.E. in Computer Science and Technology, Wuhan University (2013–2017) Research Interests : Pu’s work addresses cutting-edge challenges in cybersecurity, including detecting deepfake content (text, images, and videos), defending against adversarial attacks on AI models, and improving robustness in medical imaging systems. Their research combines techniques from machine learning, computer vision, and data mining to enhance security and reliability in AI applications. Key Publications : Pu has authored influential papers on deepfake detection (IEEE S&P 2023, WWW 2021) and AI security (USENIX Security 2021, EuroS&P 2020). Their work spans theoretical advancements and practical tools like NoiseScope for blind deepfake image detection. Awards : Dr. Dennis Kafura Graduate Fellowship (2022) Nominated for 2021 PhD Fellowship Awards (Facebook Visa Research Scholarship) IEEE S&P 2020 Student Travel Grant NDSS 2019 Student Travel Grant National Endeavor Fellowship (Twice), Chinese Ministry of Education Pu’s professional experience includes software engineering at Facebook (2021) and data science roles at IBM and Wuhan University labs. They actively collaborate with industry and academia to advance AI security and ethical AI practices.

Hubert T.H. Chanمشاهده پروفایل

دانشیار

• Algorithms
• Combinatorial Optimization
• Discrete Metric Spaces
• +۴ مورد دیگر

Professor Hubert T.H. Chan is an Associate Professor at the Department of Computer Science, University of Hong Kong, and serves as Programme Director for the BEng(CompSc) programme. He holds a PhD from Carnegie Mellon University (2007) and previously worked as a postdoc at Max-Planck-Institut für Informatik in Germany. His research focuses on algorithms, combinatorial optimization, discrete metric spaces, and security & privacy, with applications in graph theory, network analysis, and privacy-preserving mechanisms. Education: PhD in Computer Science, Carnegie Mellon University, 2007 BEng (Computer Science), details not specified in text Research interests emphasize algorithmic design for dynamic systems, privacy in data aggregation, and efficient optimization techniques. His work bridges theoretical computer science with practical applications in network security and distributed systems. Key grants include Hong Kong RGC-funded projects on oblivious data structures, spectral hypergraph analysis, and dynamic metric problems (2012–2018). His publications span top conferences like SODA, FOCS, WWW, and ICML, addressing challenges in clustering, sorting, privacy, and graph decomposition. He oversees research groups exploring hypergraph learning, secure computation, and algorithmic privacy. His lab's work often intersects with real-world systems, emphasizing both theoretical rigor and practical relevance.

Rajmohan Rajaramanمشاهده پروفایل

استاد

• Approximation algorithms
• Algorithmic game theory
• Distributed computing
• +۳ مورد دیگر

Rajmohan Rajaraman is a Professor at the Khoury College of Computer Sciences at Northeastern University . He specializes in theoretical computer science with a focus on approximation algorithms, algorithmic game theory, distributed computing, and network optimization. His research group contributes to advancing algorithms for dynamic networks and sensor systems. He has advised numerous PhD students, including graduates now at institutions like Google, Raytheon, and IMDEA Madrid. Current advisees include Sushant Agarwal and Omer Wasim. He teaches advanced courses such as CS 7870: Modern Algorithms Toolkit and CS 3000: Algorithms & Data Structures . Prof. Rajaraman is actively involved in academic leadership: he serves on the Computing Community Consortium (CCC) Council, is an associate editor for Theory of Computing Systems and International Journal of Foundations of Computer Science , and has chaired major conferences like ACM SPAA 2011 and IEEE DCOSS 2010. He co-organizes the NU Theory Seminar and leads Northeastern's Algorithms & Theory Group . His professional contributions extend to editorial roles for SICOMP and TOCS special issues, and he has delivered tutorials on network algorithms at venues like the ISI-IMSc Summer School. His work bridges theoretical foundations with practical applications in distributed systems and networked environments.

Elena Zhelevaمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Causal Inference
• +۳ مورد دیگر

Elena Zheleva is an Associate Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). She leads the EDGES Lab, focusing on unifying causal inference, machine learning, and network analysis to address societal challenges. Her research bridges data science, privacy, and AI fairness, with applications in social networks, health, and policy. She earned her Ph.D. from the University of Maryland in 2011. Research Interests: Dr. Zheleva's work spans data science, machine learning, causal inference, graph mining, and privacy. She develops methods to address biases in relational data, designs personalized privacy tools, and studies network interference effects. Key applications include social media analysis, healthcare informatics, and algorithmic fairness. Publication Trends: Her recent articles emphasize causal inference in networked environments , tackling problems like peer effects, diffusion interference, and bias in ranking systems. She frequently publishes in top-tier venues (e.g., UAI, WWW, KDD), showcasing innovations in experimental design, fairness-aware AI, and graph-based learning. Awards & Honors: NSF CAREER Award (2021) COE Research Award (2021) DCFemTech Award (2017) Best Paper Honorable Mention at ICWSM 2020 Advising & Grants: She mentors 5 Ph.D. candidates and has graduated 13+ students. Her lab secured major grants from NSF, DARPA, Adobe, and Anthem for projects on relational causal inference, COVID-19 attitudes, and privacy-aware systems. Key grants include NSF CAREER, TRIPODS, and DARPA EDIFICE. Leadership: Dr. Zheleva co-organizes workshops (e.g., KDD tutorials on causal inference), serves as associate editor for ACM TIST and DAMI, and is program chair for SDM 2025. She leads the EDGES Lab, fostering collaborations in computational social science and AI ethics.

Jiawei Chenمشاهده پروفایل

پژوهشگر ارشد

• Recommender Systems
• Information Retrieval
• Machine Learning
• +۵ مورد دیگر

Jiawei Chen is a "Hundred Talent" Research Fellow at Zhejiang University's College of Computer Science and Technology, with over 60 publications in top-tier venues including WWW, SIGIR, KDD, and NeurIPS. His research focuses on advancing recommender systems through innovative approaches to debiasing, graph-based learning, and large language model integration. His educational background includes: Ph.D. in Computer Science, Zhejiang University (2017-2020) Master's in Computer Science, Zhejiang University (2014-2017) Bachelor's in Micro-electronic, University of Electronic Science and Technology of China (2010-2014) Chen's research centers on solving fundamental challenges in recommender systems, particularly addressing popularity bias through spectral analysis and causal inference. His work bridges graph mining, knowledge representation, and large language models to develop robust recommendation frameworks. Recent contributions include pioneering graph transformers for ranking optimization and counterfactual reasoning to burst filter bubbles. His influential surveys on recommendation debiasing and deep clustering have established foundational taxonomies for the field. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Deepening causal approaches to bias mitigation through counterfactual interventions and distributionally robust optimization, (2) Advancing graph-based architectures with sign-aware transformers and uncertainty-aware structure learning, and (3) Integrating large language models through knowledge distillation techniques to enhance sequential recommendation. His work consistently targets high-impact solutions to popularity bias while maintaining strong theoretical grounding. His research excellence has been recognized with: Best Paper Award at WSDM 2025 for spectral analysis of popularity bias amplification Best Paper Honorable Mention at SIGIR 2023 for offline reinforcement learning in recommendation Chen actively mentors future researchers and seeks self-motivated graduate students for projects in recommendation systems, LLMs, and graph mining. His extensive service as program committee member for WWW, AAAI, KDD, and SIGIR—along with reviewing for IEEE TNNLS, TKDE, and TOIS—demonstrates significant community leadership. He has released valuable community resources including the KuaiRec and KuaiRand datasets for unbiased recommendation research. While specific lab affiliations aren't detailed, Chen maintains strong collaborative ties with researchers across institutions, particularly with Prof. Xiangnan He's group. His work frequently involves large-scale industrial datasets and open-source tools like EasyRL4Rec, indicating leadership in practical recommendation system development and community resource sharing.

• Complex systems
• Combinatorial optimization
• Evolutionary computation
• +۵ مورد دیگر

Sebastien Verel is a Researcher at the Laboratoire d'Informatique de l'Université du Littoral Côte d'Opale (LISIC) . His work focuses on complex systems, combinatorial optimization, evolutionary computation, and fitness landscape analysis. Research Interests: Multiobjective optimization, metaheuristics, adaptive algorithms, and simulation-based modeling Collaborations: Active in international conferences (GECCO, EvoCOP, PPSN) with teams from UK, Italy, Australia, and Morocco Key Publications (2023-2025) address topics like: Pareto local optimal networks for multiobjective landscapes Surrogate-assisted optimization using Walsh decomposition Fitness landscape analysis across ML models and protein engineering Urban mobility benchmarks (bus spacing in Calais)

Yu Wangمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Machine Learning
• +۷ مورد دیگر

Yu Wang serves as an Assistant Professor in the Department of Computer and Information Science at the University of Oregon's School of Computer and Data Sciences, where he leads research in graph-based machine learning and AI systems. Joining the faculty in September 2024 after completing his PhD at Vanderbilt University, he directs the Graph Machine Learning Lab and actively recruits PhD students for cutting-edge projects at the intersection of data mining, network analysis, and trustworthy AI. His educational background includes a Ph.D. in Computer Science from Vanderbilt University (2019-2024) and a B.S. from Harbin Institute of Technology (2015-2019). During his doctoral studies, he completed research internships at Adobe Research and The Home Depot, focusing on knowledge graph applications for information retrieval. Wang's research centers on graph machine learning, with emphasis on neural-symbolic learning, LLM integration with structured knowledge, and trustworthy AI systems. His work addresses critical challenges in imbalanced/biased graph learning, generative graph models, and social network analysis, driving applications in cyber-security, biochemistry, and infrastructure systems. Current projects explore agentic social simulations and spatial-temporal machine learning for real-world impact. His publication trajectory shows increasing focus on LLM-graph integration and trustworthy AI, with recent work spanning generative graph models, personalized LLMs, and robust networking systems. Key trends include bridging symbolic reasoning with neural approaches, advancing evaluation benchmarks for social network analysis, and developing privacy-preserving techniques for graph-based retrieval systems. Outstanding Doctoral Student Award (Vanderbilt, 2023-2024) Best Paper Award at NeurIPS GLFrontiers Workshop (2023) Vanderbilt Graduate Leadership Anchor Award for Research (2023) KDD Outstanding Dissertation Award Honorable Mention (2025) NSF IIS-III Core Program Grant as Lead PI (2025) Top-10 Most Influential Papers at CIKM'22 and WWW'23 Wang mentors PhD students including Yongjia Lei (SDM Doctoral Forum Honorable Mention recipient) and Riya (Pulse Research Fellow), with research funded through NSF grants and industrial collaborations with Adobe, Visa, and Home Depot. His lab provides substantial computational resources including L40S GPUs and OpenAI API access, supporting both theoretical innovation and real-world deployments through industry internships. Current advising focuses on generative graph models, LLM-agent collaboration, and trustworthy AI frameworks for societal applications. The Graph Machine Learning Lab maintains strong ties with industrial research groups, facilitating student internships at Adobe Research and other tech companies while developing open-source tools like ChemicalX for drug discovery. Ongoing projects include GraphRAG frameworks for security applications and social simulation environments for studying network dynamics.

Michael Schmittمشاهده پروفایل

استاد

• Experimental Particle Physics
• Collider Physics
• Muon-to-Electron Conversion
• +۳ مورد دیگر

Michael Schmitt is a Professor and Chair in the Department of Physics and Astronomy at Northwestern University. His research focuses on experimental particle physics, particularly collider experiments at the Large Hadron Collider (LHC) and the Mu2e experiment at Fermilab. He leads studies in electroweak physics, searches for new particles beyond the Standard Model, and precision measurements of Z boson decays. Current projects include the CMS experiment's WW/WWW cross-section measurements, rare Z boson decays, and the Mu2e collaboration's magnetic field mapping. Schmitt also contributes to advanced data analysis techniques, including Bayesian Blocks and random forest classifiers. He teaches statistical methods for physicists and has advised multiple graduate students and postdocs. Positions: Professor, Chair of Physics & Astronomy, CMS Collaboration member, Mu2e Collaboration member Education: PhD in Physics from Harvard University (1991) Labs/Teams: CMS and Mu2e experimental groups, Northwestern High Energy Physics group Recent Research Highlights: Precision electroweak measurements, high-granularity calorimeter timing studies, muon-to-electron conversion background analysis. Over 100 peer-reviewed publications in Phys. Rev. D, JHEP, and other top journals.

Philip Changمشاهده پروفایل

استادیار

• Experimental Particle Physics
• Higgs Boson Research
• Multiboson Signatures
• +۳ مورد دیگر

Philip Chang is an Assistant Professor in the Department of Physics at the University of Florida's College of Liberal Arts and Sciences. His research focuses on experimental particle physics, particularly the electroweak and Higgs sectors. He develops track-finding algorithms using AI for High-Luminosity LHC challenges. Chang's work analyzes multiboson signatures and quarkonium states at the LHC, with recent publications exploring CP violation, exotic particle searches, and vector boson scattering. His group utilizes machine learning for collision data reconstruction. Publications emphasize precision measurements of Higgs couplings, rare triboson events, and beyond-Standard-Model signatures using CMS detector data.

Antonietta Lanzaمشاهده پروفایل

استادیار

• Man-Machine Interaction
• Machine Learning
• Knowledge Acquisition
• +۴ مورد دیگر

Antonietta Lanza is an Assistant Professor of Computer Science at the Department of Informatics, University of Bari, Italy. She earned her degree in Information Science from the same university in 1978 and has been a faculty member since 1984. Her research spans man-machine interaction, machine learning, and knowledge acquisition with applications in technical document processing and geospatial data interpretation. Education : Degree in Information Science (University of Bari, 1978) Affiliations : Department of Informatics, University of Bari Her research focuses on integrating machine learning with geospatial analysis, particularly for wind speed forecasting, topographic map interpretation, and cadastral data processing. She has developed algorithms for spatio-temporal modeling and spatial clustering of structured data. Recent publications primarily address wind power prediction, spatio-temporal lazy learning, and cluster-based time series modeling. Applications span renewable energy, environmental planning, and maritime AIS data analysis. She actively contributes to academic associations AI*IA (Italian Association for Artificial Intelligence) IAPR-IC (International Association of Pattern Recognition - Italian Chapter) and has created software like PS_package , a tool for line-drawing segmentation.

Vangelis Markakisمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Game Theory
• Mechanism Design
• +۳ مورد دیگر

Vangelis Markakis is an Associate Professor at the Department of Informatics, Athens University of Economics and Business (AUEB), where he has been a faculty member since 2009. He holds a PhD in Theoretical Computer Science from Georgia Institute of Technology (2005), an M.Sc. in Computer Science (Georgia Tech 2004), and a Diploma in Electrical and Computer Engineering from NTUA (2000). His academic career includes postdoctoral research at the University of Toronto and CWI (Amsterdam). Research Interests: Theoretical computer science, algorithm design, game theory, mechanism design, social networks, combinatorial optimization Awards: Recipient of the 2025 ACM SIGecom Test of Time Award for foundational work on fair allocations (2004) and the 2003 ACM EC Best Student Paper Award His work bridges algorithmic game theory, fair division, and computational economics, with notable contributions to approximation algorithms for Nash equilibria and auction mechanisms. He has supervised numerous PhD and MSc students, including prominent researchers now at institutions like University of Essex, NTU Singapore, and Yale. Scientific activities include extensive program committee memberships (ACM EC, AAAI, AAMAS, SAGT, WWW) and organizing conferences like ISCO 2012 and ACAC 2009. His teaching portfolio covers game theory, data structures, and advanced algorithms.