جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Mengnan Du is an Assistant Professor in Data Science at New Jersey Institute of Technology (NJIT). Their research focuses on artificial intelligence, machine learning, and natural language processing with a strong emphasis on fairness, explainability, and bias mitigation in AI systems. Key areas include large language models (LLMs), neural network interpretability, and ethical AI. Dr. Du leads the Collaborative Research: III: Medium: Towards Effective Detection and Mitigation for Shortcut Learning project funded by the National Science Foundation (2023-2027), exploring framework development for addressing shortcut learning in deep neural networks. They are also involved in interdisciplinary work combining AI with solar physics and financial systems. Research themes include: 1) XAI (explainable AI) techniques for LLMs, 2) bias detection/mitigation in NLP systems, 3) robustness in neural networks, and 4) AI applications in healthcare/finance. Notable contributions include fairness-aware classifier generalization methods and novel approaches for debiasing knowledge graphs. Recent work has addressed topics like concept depth analysis in LLMs, generative AI ethics in advertising, and neural operator networks for scientific computing. Their publications span top conferences like ACL, COLING, and WWW, with a focus on bridging theory and real-world AI system development.

Johnnatan Messiasمشاهده پروفایل

پژوهشگر

• Blockchain governance
• Decentralized finance
• Network transparency
• +۱ مورد دیگر

Johnnatan Messias is a Research Scientist at the Max Planck Institute for Software Systems (MPI-SWS), specializing in blockchain governance, decentralized finance, and network transparency. He holds a Ph.D. in Computer Science from MPI-SWS in collaboration with Universität des Saarlandes, advised by Prof. Krishna P. Gummadi. His work focuses on improving fairness and accountability in decentralized systems through empirical analysis of blockchain transactions and governance mechanisms. Primary research areas include DeFi protocol vulnerabilities, blockchain scalability solutions (especially rollups), and transparency in transaction ordering. He develops measurement tools like Glasnost for detecting network differentiation and SatelliteLab for heterogeneous network testing. Recent work examines governance centralization in DeFi, arbitrage dynamics in layer-2 systems, and economic sustainability of token distribution mechanisms. Publications demonstrate consistent methodological innovation in analyzing blockchain data, combining network measurements, statistical modeling, and game-theoretic frameworks. Key contributions include revealing voting power concentration in DAOs, quantifying rollup performance during inscription surges, and detecting opaque transaction prioritization in Bitcoin and Ethereum. His empirical studies have exposed systemic issues like miner collusion in fee markets and arbitrage inefficiencies in AMMs. Dr. Messias has contributed to academic service as program committee member for FC, AFT, WWW, and other major conferences. He developed open-source tools including WhatsApp Monitor for tracking misinformation and public datasets for ZK-Rollup research. Current projects investigate the impact of inscriptions on EVM networks and design frameworks for decentralized insurance on blockchain infrastructures.

• Recommendation Systems
• Machine Learning
• Medical Informatics
• +۴ مورد دیگر

Zhaopeng Qiu is an active researcher in computer science with a strong publication record spanning from 2012 to 2025. His work primarily focuses on recommendation systems, machine learning, and their applications in healthcare and online services. He frequently collaborates with researchers including Xian Wu, Zhi Zheng, Hengshu Zhu, and Hui Xiong on projects related to large language models, medical informatics, and job recommendation systems. Dr. Qiu's research interests include Recommendation Systems, Machine Learning, Medical Informatics, Natural Language Processing, Artificial Intelligence, Data Mining, and Healthcare AI. His work demonstrates a clear evolution from earlier research in mobile robotics (2012-2015) to current cutting-edge work applying large language models to recommendation problems across various domains. Analysis of his recent publications shows a strong trend toward leveraging large language models for recommendation tasks, with significant contributions in medication recommendation, job matching, and fairness-aware systems. His work bridges theoretical AI advancements with practical applications, particularly in healthcare contexts where AI can have significant real-world impact. While specific scientific awards aren't documented in the available publications, his work appears in top-tier venues including WWW, AAAI, KDD, and IEEE/ACM transactions journals, indicating recognition within the research community. Dr. Qiu's research has practical implications for online platforms, healthcare systems, and labor market technologies. His recent focus on large language models for recommendation suggests he's at the forefront of integrating emerging AI capabilities with traditional recommendation paradigms.

Senuri Wijenayakeمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Social Computing
• Online User Behaviors
• +۵ مورد دیگر

Senuri Wijenayake is a Lecturer and Researcher in Human-Computer Interaction (HCI) and Social Computing at RMIT University's School of Computing Technologies, Australia. She holds a PhD from the University of Melbourne and a BSc (Hons) from the University of Moratuwa, Sri Lanka. Her research focuses on online social conformity, user behaviors in social platforms, and designing interfaces for ethical interactions. She has supervised PhD and postgraduate students, taught courses like Design Programming and Interaction Design, and led projects on autonomous vehicle-pedestrian interaction and online safety mechanisms. Awards include the Best Paper Award at INTERACT'21 and the 2021 Graduate Research Student of the Year. Education: PhD in Human-Computer Interaction, University of Melbourne (2018–2021) BSc (Hons) in Information Technology, University of Moratuwa (2013–2017) Secondary Education: GCE Advanced/Ordinary Levels, Sri Lanka Research Interests: Social Computing, Online Conformity, HCI, Autonomous Systems, Crowdsourcing, and Ethical AI. Current projects explore technology-facilitated abuse prevention and mitigating cyberbullying on social media platforms. Grants & Awards: Australian Communications Consumer Action Network Grant (AUD 78,045) for online safety strategies Instagram Research Awards Grant (AUD 75,000) for cyberbullying studies Recipient of multiple best paper awards and academic scholarships Teaching & Mentoring: Unit coordinator for over 450 students in Design Programming and Interaction Design, academic mentor for PhD/Masters researchers, and motivational speaker in Sri Lanka. Certifications include Melbourne Teaching Certificate and Modular Professional Learning Framework. Professional Activities: Organizing committee member for ACM UbiComp 2024, conference reviewer for CHI, CSCW, and WWW, and volunteer with Sri Lankan graduate societies.

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.

Carlos Alberto Alejandro Castillo Ocaranzaمشاهده پروفایل

استاد پژوهش

• Web Mining
• Information Retrieval
• Crisis Informatics
• +۵ مورد دیگر

Carlos Castillo is an ICREA Research Professor (Part-time) at Universitat Pompeu Fabra in Barcelona, where they lead the Social and Responsible Computing Research Group within the Department of Information and Communication Technologies. Dr. Castillo identifies as nonbinary and prefers they/them pronouns, and is also a Latinx migrant to Barcelona in Catalunya, Spain. Dr. Castillo received their Ph.D from the University of Chile in 2004, followed by visiting scientist positions at Universitat Pompeu Fabra (2005) and Sapienza Universitá di Roma (2006) before working as a scientist and senior scientist at Yahoo! Research (2006-2012), as a senior scientist and principal scientist at Qatar Computing Research Institute (2012-2015), and as director of research for data science at Eurecat (2016-2017). Dr. Castillo's research addresses issues of social significance through interdisciplinary computer science research, with primary focus on algorithmic fairness, crisis informatics, web content quality and credibility, and adversarial web search. Their work combines technical expertise in information retrieval with deep consideration of social implications, particularly in high-risk applications including criminal justice and recruitment. They have made significant contributions to understanding and mitigating discrimination in algorithmic systems, as evidenced by their book on Big Crisis Data and numerous influential publications. Recent publications demonstrate a strong emphasis on fairness in algorithmic decision-making, with numerous papers examining bias in hiring algorithms, recidivism prediction systems, and social media content analysis. The research shows an interdisciplinary approach that bridges computer science, social science, and policy considerations, with increasing attention to practical applications and real-world impact across domains including criminal justice, healthcare, education, and music recommendation systems. Dr. Castillo has received significant recognition for their work: Two test-of-time awards Four best paper awards Two best student paper awards ACM Distinguished Member IEEE Senior Member Accredited at the full professor level in Catalonia Dr. Castillo has served extensively in academic leadership roles, including as Program Committee or Senior PC member for major conferences (WWW, WSDM, SIGIR, KDD, CIKM), editorial committee member for ACM Transactions on the Web and ACM Transactions in Social Computing, and Executive Committee member of ACM FAccT. They were General Co-Chair of ACM FAccT (formerly FAT*) 2020, PC Co-Chair of ACM Digital Health 2016-2018, and PC Co-Chair of WSDM 2014. Dr. Castillo currently coordinates the Horizon Europe project FINDHR on detecting and mitigating discrimination in algorithmic hiring. They lead the Social and Responsible Computing Research Group, which takes an interdisciplinary approach to developing computational methods that consider social impact and ethical implications. The group works on projects involving computer scientists, social scientists, and domain experts to address societal challenges through responsible technological innovation, with current focus on algorithmic fairness in high-risk applications, crisis informatics, and understanding social dynamics through computational methods.

Muhammad Abu Bakar Siddiqueمشاهده پروفایل

استادیار

• Natural Language Processing
• Large Language Models
• Machine Learning
• +۷ مورد دیگر

Muhammad Abu Bakar Siddique is an Assistant Professor in the Department of Computer Science at the University of Kentucky, part of the Stanley and Karen Pigman College of Engineering. His research focuses on natural language processing, large language models, and machine learning with particular emphasis on zero-shot learning and conversational AI systems that are safe, personalizable, and interpretable. Dr. Siddique earned his Ph.D. in Computer Science from the University of California, Riverside (2017-2021), his M.S. from Lahore University of Management Sciences, Pakistan (2008-2011), and his B.S. from International Islamic University, Pakistan (2003-2008). His research interests include: Natural Language Processing and Large Language Models Zero-shot and few-shot learning for conversational AI Safe, personalizable, and interpretable conversational systems Task-oriented dialog systems with domain generalization Mobile app recommendation systems Scalable machine learning methodologies Dr. Siddique's publications span top venues including WWW, SIGIR, KDD, and IEEE S&P, demonstrating his focus on developing practical AI solutions that can adapt to new domains without extensive retraining. His recent work shows increasing exploration of quantum software and the intersection of AI with mobile applications. His notable achievements include Best Paper Awards at the IEEE International Conference on Quantum Software (2025) and IEEE ICSC (2021). Dr. Siddique has secured significant funding from the National Science Foundation: CPS Medium: Calfhealth: Explainable AI for Pneumonia Detection in Dairy Calves ($941,359) SaTC CORE: Personalized and Trustworthy Mobile App Recommendations ($300,000) III Small: User-Centric Task-Oriented Dialog Systems ($599,898) DCL EPSCOR: Distributed Edge Intelligence ($100,000) He currently advises three PhD candidates (Adib Mosharrof, Moghis Fereidouni, and Muhammad Umair Haider) and has mentored several successful graduates. Dr. Siddique serves on program committees for major conferences including ACL, NeurIPS, ICML, and AAAI, and participates in outreach by hosting high school students through the University of Kentucky's Summer Youth Program.

Peter L. Bartlettمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning Theory
• Pattern Classification
• +۲ مورد دیگر

Peter L. Bartlett is a Professor at the University of California at Berkeley , affiliated with the Department of Electrical Engineering and Computer Sciences and the Department of Statistics. He also serves as ML Research Director at the Simons Institute for the Theory of Computing, Director of the Foundations of Data Science Institute, and Director of the Collaboration on the Theoretical Foundations of Deep Learning. Additionally, he holds a position as Principal Scientist at Google DeepMind. Research Interests : His work focuses on Machine Learning , Statistical Learning Theory , Pattern Classification , Adaptive Control , and Reinforcement Learning . He investigates theoretical aspects of learning algorithms, optimization dynamics, and generalization in overparameterized models. Article Trends : Recent publications emphasize scaling laws in linear regression , gradient descent optimization , benign overfitting , generative adversarial networks (GANs) , and implicit bias in neural training . Key sub-fields include overparameterized learning , minimax optimization , low-dimensional data analysis , and efficient decoding algorithms . Students and Postdocs : He has advised numerous students and postdocs across institutions, including Ishaq Aden-Ali , Spencer Frei , and Yeshwanth Cherapanamjeri , with former advisees like Yasin Abbasi-Yadkori (Adobe) and Alexander Rakhlin (Penn). Contact : Email peter@berkeley.edu , Address: 387 Soda Hall #1776, Berkeley, CA 94720-1776.

Babak Naderiمشاهده پروفایل

پژوهشگر

• Subjective Quality Assessment
• Speech Quality Evaluation
• Crowdsourcing
• +۳ مورد دیگر

Babak Naderi is a Researcher at the Quality and Usability Lab of Technical University of Berlin , where he has been employed since August 2012. He holds a Dr.-Ing. (PhD) from 2017 for his dissertation on Motivation of Workers on Microtask Crowdsourcing Platforms , a master's degree in Geodesy and Geoinformation Science from TU Berlin (2017), and a bachelor's in Software Engineering . His research focuses on subjective quality assessment , speech quality evaluation in crowdsourcing environments, and motivation theory applied to microtask platforms. He developed the Crowdwork Motivation Scale based on Self-Determination Theory and has worked on standardization efforts within the P.CROWD work program of the ITU-T Standardization Sector . His publications demonstrate expertise in crowdsourcing for speech quality assessment, statistical modeling of worker behavior, and text complexity analysis . He has contributed to IEEE and ITU-T venues while serving as a reviewer for conferences like WWW and journals including International Journal of Human-Computer Studies . Awarded BMBF training program (2013-2015) Leader of the CrowdMAQA project

• Machine Learning
• Artificial Intelligence
• Ethics
• +۷ مورد دیگر

Dr. ir. Cynthia C.S. Liem is an Associate Professor in the Multimedia Computing department at Delft University of Technology, specializing in ethical and reliable AI frameworks. Her work bridges machine learning, human-computer interaction, and societal impact. Academic Rank: Associate Professor Department: Multimedia Computing School: Electrical Engineering, Mathematics and Computer Science Liem’s research focuses on Explainable AI , Algorithmic Transparency , and AI Ethics , emphasizing fairness in machine learning and societal implications of autonomous systems. Recent publications explore adversarial testing, energy-constrained counterfactuals, and critiques of unscientific AGI claims. Her most recent articles highlight trends in autonomous vehicle testing , AI explainability , and ethical algorithmic design , leveraging techniques like conformal prediction and differential evolution . Scientific Awards : SBFT 2023 Best Paper Award TU Delft Education Fellow WWW 2018 Challenge Winner 2024 Women in AI Netherlands Diversity Leader Award Liem serves as an advisor for DUO’s research ethics audits and frequently contributes to public discourse on AI’s societal role, appearing in national media outlets like Delta and Trouw . She also leads datasets on music representation learning and algorithmic recourse.

• Personal Analytics
• Lifelogging
• Information Retrieval
• +۳ مورد دیگر

Dr. Cathal Gurrin is a Lecturer at the School of Computing , Dublin City University, Ireland. He serves as a funded Investigator at the Insight SFI Research Centre for Data Analytics , where he leads a research group of 10 people, and holds the position of Visiting Scientist at the University of Tromso, Norway . Research Interests: Personal analytics and lifelogging ("a search engine for the self") Wearable sensor data analysis for activity inference and health enhancement Information retrieval (IR) from ubiquitous devices Development of WWW search algorithms and multimedia content mining tools Digital memory systems with over 15 million wearable camera images and sensor readings Grants & Research Leadership: As a funded Investigator at Insight SFI, Cathal leads a multidisciplinary research group focused on personal sensing and data analytics. His work involves long-term digital memory collection since 2006. Labs & Teams: He manages a research group of 10 people at Insight SFI, specializing in lifelogging technologies and personal data systems.

Aris Anagnostopoulosمشاهده پروفایل

استاد

• Stochastic Analysis of Dynamic Processes
• Modeling and Mining in Social Networks
• Data Mining and Algorithms for the WWW
• +۳ مورد دیگر

Aris Anagnostopoulos is a Professor at the Department of Computer, Control, and Management Engineering (Dipartimento di Ingegneria Informatica, Automatica, e Gestionale) at Sapienza University of Rome since April 2012. His academic journey includes a Marie-Curie fellowship at Sapienza University and a postdoctoral position at Yahoo! Research in Santa Clara, CA. His educational background includes: Ph.D. in Computer Science, Brown University, Providence, RI Sc.M. in Applied Mathematics, Brown University, Providence, RI Sc.M. in Computer Science, Brown University, Providence, RI Diploma in Computer Engineering and Informatics, University of Patras, Patras, Greece Professor Anagnostopoulos's research focuses on the design and analysis of algorithms with applications in data mining and data science. His work spans stochastic analysis of dynamic processes, social network modeling and mining, WWW algorithms, randomized and approximation algorithms, information retrieval, and information security. His research has evolved to address contemporary challenges in federated learning, knowledge graphs, and ethical AI considerations in recommendation systems. His recent publications demonstrate a strong trend toward addressing real-world applications of data science and machine learning, particularly in healthcare, social media analysis, and privacy-preserving techniques. His work shows increasing interdisciplinary collaboration, especially with medical researchers, while maintaining strong theoretical foundations in algorithm design. Among his notable scientific awards are: Google Focused Research Award (1 of 6 PIs), 1M USD Junior Fellow, School for Advanced Studies, Sapienza University of Rome Personal research grant, Swedish Research Foundation, 200K euro, 2011 (declined) Best Poster Award, 4th International Conference on Web Search and Data Mining (WSDM 2011) Marie Curie International Incoming Fellowship, 160K euro, 2010 Paris Kanellakis Fellowship, Brown University Runner Up, Best Paper Award, 14th International World Wide Web Conference 2005 (WWW 2005) Professor Anagnostopoulos serves as the academic responsible for mobility (RAM) for the Data Science master's program and has developed comprehensive teaching materials for data science education. He teaches courses including Social Networks and Online Markets, Algorithmic Methods of Data Mining, Data Mining, and Algorithm Design. His teaching approach emphasizes both theoretical foundations and practical applications, with extensive use of AWS and Python-based tools to prepare students for industry certification.

Marie-Christine ROUSSETمشاهده پروفایل

استاد

• Knowledge Representation
• Information Integration
• Ontology-based data access
• +۷ مورد دیگر

Marie-Christine ROUSSET is a Professor of Computer Science at the University of Grenoble Alpes (UGA) in France, where she is a member of the LIG (Laboratoire d'Informatique de Grenoble) in the SLIDE group. Previously affiliated with Paris-Saclay (LRI), she has established herself as a leading researcher in Knowledge Representation and Information Integration. She holds the distinguished position of Senior member of the Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) and serves as co-responsible for the chair Explainable and Responsible AI within MIAI Grenoble Alpes. Her research focuses on ontology-based data access, logic-based mediation between distributed data sources, query rewriting using views, data linkage, and distributed reasoning for the Semantic Web. She skillfully combines artificial intelligence and database techniques to address complex information integration challenges, with applications spanning biomedical informatics, educational technology, and trustworthy AI. Her work demonstrates consistent innovation from foundational research to practical implementations, as evidenced by her co-authorship of the book 'Web Data Management' published by Cambridge University Press. Professor ROUSSET's recent publications (2019-2022) reveal a growing emphasis on data privacy, RDF graph anonymization, and interactive ontology engineering, while maintaining her strong contributions to semantic web technologies and knowledge representation. Her research shows increasing attention to trustworthy AI concerns, aligning with her leadership roles in relevant projects. Scientific Recognition Senior member of Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) Junior member of Institut Universitaire de France (IUF) from 1997 to 2002 Chevalier de l'Ordre National du Merite (July 11, 2011) EurAI Fellow (nominated ECCAI Fellow in 2005) Best Paper Award at AAAI'96 for 'Verification of Knowledge Bases based on Containment Checking' Professor ROUSSET maintains an active role in the scientific community through editorial work and organizational leadership. She serves on the Editorial Board of Communications of the ACM (CACM) and has held significant roles including PC chair of EGC 2019, Workshops co-Chair of WWW 2018, and Area Chair of IJCAI 2017. Her consistent service on program committees of major international conferences demonstrates her standing in the field. Her laboratory, the SLIDE group within LIG, focuses on semantic web technologies, knowledge representation, and data integration. The group maintains strong connections with the international research community and participates in collaborative projects addressing cutting-edge challenges in artificial intelligence and data management, with particular emphasis on trustworthy and explainable AI systems.