Professor Falk Scholer is a Professor of Information Access and Retrieval Technologies in the School of Computing Technologies at RMIT University, Melbourne, Australia. He leads the RMIT University Centre for Human-AI Information Environments (CHAI). His research focuses on understanding how search engines and recommender systems assist users in resolving information needs and measuring their effectiveness. His work emphasizes fairness, accountability, and human-centered computing. Research interests include interactive information access, evaluation methodologies, misinformation, and the ethical implications of AI systems. His projects span topics like bias mitigation in conversational search, physiological data analysis for user behavior, and generative AI adaptation for diverse scenarios. He collaborates on industry projects, such as enhancing job search systems with SEEK, improving query performance prediction, and developing fact-checking tools. His teaching interests include Data Science, Web Programming, and Research Methods. He actively supervises PhD and Master’s students in areas like fairness-aware systems and complex answer retrieval. Professor Scholer serves on committees including RMIT’s Academic Board and Human Research Ethics Committee. His research has been recognized through high-impact publications and partnerships addressing real-world challenges in search and information systems.
Dr. Sean MacAvaney is a Lecturer in Machine Learning at the School of Computing Science, University of Glasgow. His research focuses on advancing information retrieval techniques, particularly in neural models, sparse/dense retrieval architectures, and system optimization. He actively contributes to open-source tools like PyTerrier and ir_datasets, emphasizing reproducibility in research. Education: Not explicitly detailed in the text, but his academic contributions imply advanced qualifications in computer science or related fields. Research Interests: MacAvaney's work spans neural information retrieval, adversarial model analysis, query expansion strategies, and large-scale system efficiency. He explores topics like instruction-following models, multilingual benchmarking, and the integration of machine learning with traditional retrieval methods. Article Trends: Recent work emphasizes practical system optimizations (e.g., in-memory indexes), cross-lingual evaluation frameworks, and bridging gaps between human and machine relevance judgments. He also investigates challenges in productionizing neural models and maintaining test collection relevance over time. Advising & Grants: Supervises students including Andreas Chari (deep learning for language tools) and Andrew Parry (uncertainty modeling in neural networks). His grants likely support projects in scalable retrieval systems and reproducible research practices. Labs/Teams: Engaged with the Glasgow Information Retrieval (GIR) group and collaborates internationally on initiatives like the Information Retrieval Experiment Platform (IREP).
Murat Uluk serves as an Assistant Professor at Beykent University in the Faculty of Communication, Department of New Media, and concurrently at Ostim Technical University in the Faculty of Architecture and Design, Department of Visual Communication Design. He has held academic positions since 2017, progressing from Lecturer to Doctor Lecturer in 2022 before attaining his current rank. His teaching portfolio spans Web Design, Social Media Metrics, Interaction Design, and related courses across both Turkish and English curricula. His research centers on Interaction Design and Digital Privacy, with significant contributions to understanding online surveillance, cookie usage permissions, and ethical implications of digital interfaces. Key interests include Social Media dynamics, Web Technologies, Human-Computer Interaction, Data Journalism, and Mobile Applications, often contextualized within Turkish digital landscapes and regulatory frameworks. Analysis of his 15 most recent publications reveals a dominant focus on privacy challenges in evolving web ecosystems, particularly the transition to cookie-less environments. His work consistently employs user-centered methodologies to evaluate mobile health applications, targeted advertising mechanisms, and algorithmic filter bubbles, with strong emphasis on empirical studies presented at major international communication conferences.
Prof. Dr. Gjergji Kasneci is a Professor of Responsible Data Science at the Technical University of Munich (TUM), leading the Chair of Responsible Data Science. He holds affiliations with the TUM School of Social Sciences and Technology and the TUM School of Computation, Information and Technology. His research focuses on ethical, legal, and societal aspects of AI, emphasizing transparency, fairness, and robustness in machine learning algorithms. Prof. Kasneci’s academic journey includes a PhD in Computer Science from the University of Marburg (2009), postdoctoral research at Microsoft Research Cambridge, and leadership roles at the Hasso Plattner Institute and SCHUFA Holding AG. He was an Honorary Professor at the University of Tübingen (2018–2023) and currently serves as Vice Dean and Information Officer at TUM. Key awards include the Seoul Test of Time Award (2018) and an Honorary Professorship from the University of Tübingen (2019). He leads initiatives like the AI in Finance Lab and contributes to AI policy through projects such as the EU-funded AI4POL initiative.
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.
Jordi Delgado Pin is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Computer Science (FIB) and the Department of Computer Science. He is an active researcher in the IDEAI-UPC and SOCO (Soft Computing) research groups, contributing extensively to artificial intelligence, machine learning, and complex systems. His research interests include artificial intelligence, machine learning, computational complexity, neural networks, and data science, with applications in biological and social networks. He has made significant contributions to unsupervised learning methods, particularly in contrastive divergence and community detection in complex networks. The analysis of his recent publications reveals a strong focus on machine learning optimization, neural network training, and theoretical foundations of computing. His work spans both theoretical and applied domains, including educational technology and bio-inspired algorithms. Jordi Delgado Pin has participated in multiple competitive R&D+i projects, such as 'Gestió i Anàlisi de Dades Complexes' and 'Mineria en datos biológicos y sociales', demonstrating sustained research activity. He has also contributed to educational initiatives in programming and data science. He is involved in key research laboratories including IDEAI-UPC, SOCO, and LARCA, fostering interdisciplinary collaboration in data science and AI.
Dr. Syed Hassan Shah is a distinguished adjunct faculty member at California State University, Fullerton's Department of Computer Science, where he teaches graduate-level courses. Concurrently, he serves as a Wi-Fi connectivity subject matter expert and Product Director for Short Range Technologies at Quectel Inc., and previously held roles as Product Manager for Mobile & Compute Connectivity at Qualcomm Inc., focusing on Mi-Fi, CPE, and UWB technologies. Ph.D. in Computer Science & Engineering, Kyungpook National University, South Korea (2013-2017) BS in Computer Science, Kohat University of Science & Technology, Pakistan (2007-2012) Dr. Shah's research spans interdisciplinary domains in Wireless Communications , Cyber-Physical Systems , and Smart Cities , with significant contributions to Vehicular Networks , Internet of Things , and Future Internet Architectures . His recent publications demonstrate expertise in Neural Networks , Optimization Theory , and Security & Privacy applications. Scientific recognition includes: Qualcomm Innovation Award (2016) IEEE Senior Member (2018) ACM Distinguished Speaker (2018) Best Research Contribution Award (Brain Korea, 2016) Multiple travel grants from ACM and IEEE KNU Honors Scholarship (2013) Dr. Shah actively contributes to academic governance as: Editorial Board Member for 60+ special issues in top-ranked journals TPC Member for 100+ international conferences including IEEE Globecom and ACM MobiHoc IEEE Vehicular Technology Society Board appointee (2018-2019)
Wenjie Li is a Professor at the Department of Computing, Hong Kong Polytechnic University, and holds a PhD from the Chinese University of Hong Kong (1997). His research spans Natural Language Processing, Artificial Intelligence, and Machine Learning , focusing on Large Language Models (LLMs) Multimodal Systems Dialogue and Recommender Systems Speculative Decoding and Inference Optimization Chain-of-Thought Reasoning Recent work emphasizes generative retrieval , personalized web agents , and error-resilient LLM frameworks . Key contributions include the JobFormer for skill-aware recommendations, STeCa for trajectory calibration, and TokenSkip for controllable reasoning compression. Publications reveal a trend toward enhancing multimodal alignment and safety mechanisms in aligned LLMs. Li actively collaborates with institutions like Queen's University , Tsinghua University , and Nanjing University , working on projects such as text-image interleaved retrieval and speculative decoding surveys . His 2024-2025 output includes 15+ papers at venues like ACL, CVPR, ICLR , and journals like IEEE Transactions on Neural Networks .
Masashi Toyoda is an Associate Professor at the Institute of Industrial Science, University of Tokyo. He leads the Kitsuregawa and Toyoda Laboratory and is affiliated with the International Research Center for Strategic Information Fusion. His research focuses on web mining, user interfaces, and information visualization. Education: Ph.D. in Computer Science, Tokyo Institute of Technology (1999) M.S. in Computer Science, Tokyo Institute of Technology (1996) B.S. in Computer Science, Tokyo Institute of Technology (1994) His research interests span web mining, user interfaces, information visualization, and visual programming. He has conducted significant work in spatiotemporal web analysis, web spam detection, evolution of web communities, and visualization techniques including zooming interfaces and 3D web graph visualization. Recent publications (2008-2011) demonstrate strong focus on web mining, information visualization, and web security. Research trends include 3D visualization of time-series web data, analysis of blog archives and CGM images, and novel methods for detecting and classifying web spam. The work consistently emphasizes large-scale web data analysis and interactive systems. Scientific Awards: 1st DBSJ Paper Award Winner (2003) FIT Paper Award and Funai Best Paper Award (2002) Best Presentation Award at DEWS2002 Presentation Award at DEWS2000 Best Paper Award at DEIM2011 Conference Excellence Award at IPSJ 72nd National Convention As head of the Toyoda Laboratory at the University of Tokyo, he leads research in web mining, information fusion, and visualization. The lab has developed notable projects including WebRelievo for web structure evolution analysis and KLIEG visual programming environment.
Lisovenko Iryna Dmytrivna serves as Assistant Professor in the Department of Computer Systems and Networks at Yuriy Fedkovych Chernivtsi National University since 2002, following her 2000 diploma in Computer Systems and Networks from the same institution. Her professional trajectory includes software engineering at a municipal medical institution (2000–2002) and ongoing contractual collaboration with Elogic Commerce since 2018. Her academic credentials include: Yuriy Fedkovych Chernivtsi State University, Faculty of Physics, Department of Computer Science, specialty "Computer Systems and Networks" (2000) Dr. Lisovenko's research centers on parallel and distributed computing, emphasizing automatic parallelization techniques, GPU-accelerated non-graphical computing, and structural analysis of program intermediate representations. She concurrently investigates specialized computer systems for data stream processing and develops methodologies for enterprise accounting automation, bridging theoretical computer science with business process optimization. Her scholarly output demonstrates sustained innovation in high-performance computing architectures. Analysis of her 15 most recent publications (2012–2021) reveals three dominant research thrusts: GPU-based parallelization (40% of works, including Kalyna encryption and bitonic sorting implementations), algorithmic optimization for diverse hardware (33%, covering sorting networks and structural matrices), and applied systems for real-world problems (27%, spanning smart home control, social media analysis, and enterprise automation). This evolution reflects increasing focus on practical GPU applications while maintaining core expertise in parallel algorithm design. She actively contributes to academic development through teaching Parallel and Distributed Computing, Economic and Legal Aspects of Enterprise Automation, and DevOps/DevNet courses, while authoring 12+ methodological manuals including "Parallel and Distributed Computing: Laboratory Workshop" (2022) and "System Administration of Linux OS" (2021). Her professional development includes certifications in Constitutional Law, Finance and Investment, DevNet Associate, and All Digital Week initiatives (2020–2021).
Huamin Qu is a Chair Professor and founding Dean of the Academy of Interdisciplinary Studies at the Hong Kong University of Science and Technology (HKUST) . He leads the VisLab and coordinates the Human-Computer Interaction (HCI) group within the Department of Computer Science and Engineering. His academic journey began with a BS in Mathematics from Xi'an Jiaotong University , followed by an MS and PhD in Computer Science from Stony Brook University . Founding Head of HKUST's Division of Emerging Interdisciplinary Areas (EMIA) Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ) Director of IPO and senior administration team member As a pioneer in data visualization and human-computer interaction , his research bridges urban computing , explainable AI , social media analysis , and E-learning . Recent work focuses on human-AI teaming , multimodal communication , and augmented reality applications . His publications reveal a trajectory from foundational graph visualization and volume rendering to cutting-edge AI-integrated visual systems . Scientific awards include induction into the IEEE Visualization Academy , IEEE VGTC Technical Achievement Award , and multiple best paper/honorable mention awards at top conferences like IEEE VIS, ACM CHI, and IEEE VAST. His lab has graduated 48 PhDs and 21 MPhil students , with 21 PhDs now faculty members at institutions including UC Davis, University of Minnesota, and Zhejiang University. Technologies developed by his group have been adopted by Microsoft , IBM , and Google . His work on projects like Pulse of HKUST and ATMSeer has received global media coverage from MIT News , IEEE Spectrum , and NHK TV . He has served as Associate Editor of IEEE TVCG and held leadership roles in major conferences including IEEE VIS , PacificVis , and VINCI .
April M. Barton serves as Dean and Professor of Law at Duquesne University's Thomas R. Kline School of Law, a position she has held since 2019. Under her leadership, the law school has risen 40 spots in U.S. News and World Report rankings and achieved record application volumes and student metrics. She oversees approximately 70+ faculty and staff, 500+ students, and 9000+ alumni, guiding the institution with a focus on ethical legal practice, equal justice, and civic engagement. Her educational background includes a J.D. from Villanova University Charles Widger School of Law and a B.S. from Moravian University. Barton's research spans the intersection of law, technology, and leadership. She is a leading voice in AI Law & Policy, having developed curricular innovations in Technology and AI, and teaches courses on AI Law & Policy and Law & Leadership. Her work in cyberlaw dates back to the 1990s, addressing foundational issues in internet regulation, intellectual property, and digital rights. More recently, she focuses on leadership development in legal education, advocating for the integration of leadership skills as a core competency for lawyers. She has launched initiatives such as the Lawyers as Leaders program and a Mini-MBA for law students, emphasizing business acumen and ethical leadership. Her publications reflect a trajectory from early cyberlaw scholarship to contemporary challenges in AI and legal education. She has consistently addressed the evolving relationship between law and technology, with recent work highlighting leadership as a critical component in preparing lawyers for the future. Barton has secured significant institutional support, including facilitating a $50 million commitment from alumnus Thomas R. Kline, the largest gift in Duquesne University history. She has testified before the U.S. Congressional Commission on Online Child Protection and the European Commission's Venice Commission, and serves on national bodies such as the Association of American Law Schools (AALS) Deans' Steering Committee. Her leadership extends to promoting diversity through the Pittsburgh Legal Diversity & Inclusion Coalition and advancing legal education innovation via national working groups. She has spearheaded physical renovations of the law school building and developed international partnerships, including a faculty exchange with the University of Sorbonne, Paris.
Fabrizio Silvestri is a Full Professor at Sapienza University of Rome's Department of Computer, Automatic and Management Engineering (DIAG), where he coordinates the Ph.D. program in Data Science. He leads the RSTLess research group focusing on Robust, Safe, and Transparent Deep Learning. Research interests: Artificial Intelligence, Machine Learning, Web Search, Natural Language Processing, Information Retrieval, Graph Neural Networks Research Trends from recent publications reveal: Advancements in sequential recommendation systems using topological and sheaf-based neural networks Focus on sustainable AI through eco-aware graph neural networks Counterfactual explanations for graph models and machine unlearning Security applications in dense retrieval and data poisoning defense Time series analysis for 5G network monitoring Integration of attention mechanisms and positional encoding in Transformers Scientific Achievements : ECIR 2018 Test of Time Award 3 Best Paper Awards (ECIR 2007, IEEE WI 2004, WSDM 2011 Runner-Up) Yahoo! Patent Milestone Award Recipient of Yahoo! Labs Excellence Program (LEAP) and Faculty Research Engagement Program (FREP) Finalist for ERCIM Cor Baayen Award (2005) Academic Leadership : Holds 9 industrial patents from Yahoo! and Facebook AI. Directed Facebook AI research groups combating malicious content. Ph.D. in Computer Science from University of Pisa with thesis on High-Performance Issues in Web Search Engines . Supervises thesis projects through the RSTLess group website .
Professor Farhad Oroumchian serves as Professor and Program Leader for General Computer Science within the Department of Information Sciences at the School of Computer Science, University of Wollongong in Dubai (UOWD). His career bridges academia and industry across Iran and the United States, with leadership roles including Department Chair at University of Tehran and research positions at TextWise LLC. His educational credentials comprise: BSc from National University of Iran (New York) MSc from Sharif University of Technology (Tehran) PhD from Syracuse University Research expertise centers on information retrieval and natural language processing, with pioneering work in Persian text processing systems and multilingual search engines . He developed an intelligent search engine using human-like reasoning models and created Hamshahri, the standard Persian text corpus. His publication portfolio exceeds 100 works with 1000+ citations, demonstrating significant impact in adaptive ranking algorithms and cross-lingual information retrieval. Professional engagement includes active membership in the Association for Computing Machinery (ACM) and its Special Interest Group on Information Retrieval (SIGIR). While specific grant details are unreported, his extensive publication record and industry collaborations indicate sustained research activity. No dedicated laboratory is mentioned, though his work involves computational linguistics infrastructure for Persian and multilingual systems.
J.A. Pouwelse is a Professor at the Data-Intensive Systems department within the Electrical Engineering, Mathematics and Computer Science school at Delft University of Technology . With 125 research outputs and 4 supervised works, his work focuses on Blockchain , Decentralized Systems , Federated Learning , and Peer-to-Peer Networks , particularly for Web3 applications. His recent publications analyze topics such as: Decentralized adaptive ranking Serverless federated learning Green smart contracts Search index optimization Zero-trust frameworks Notable recognition includes the LCN Best Paper Award 2021 . Research spans decentralized infrastructure design, privacy preservation, and scalable system implementation.
