Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas. Education: Ph.D. in Computer Science, Virginia Tech, 2021 M.S. in Computer Science, Virginia Tech, 2016 Research Interests: His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including: Graph neural networks for traffic incident prediction Social media analysis for urban challenges Physics-informed models for power grid stability Citation forecasting in scientific publications Grants & Projects: NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026) NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026) SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024) Professional Involvement: He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000). Labs/Teams: Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.
Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .
Dr hab. Krzysztof Węcel serves as Professor and current Head of the Department of Economic Informatics at Poznan University of Economics and Business (UEP), appointed on October 4, 2024. His primary affiliation spans over 25 years with UEP's Department of Economic Informatics, which maintains one of Poland's longest-running academic websites since 1998. He holds dual recognition through habilitation from University of Potsdam (2020) and professorship conferred by UEP (June 24, 2020). His academic milestones: Habilitation degree in Economic Informatics, University of Potsdam (2020) Professor title, Poznan University of Economics and Business (2020) Węcel's research centers on Semantic Technologies and data quality assessment across multilingual Wikipedia, with emphasis on company information verification, citation analysis, and open data applications. His work bridges Big Data analytics with practical business solutions, particularly in maritime logistics where he pioneered evolutionary algorithm-based AIS data processing. Current investigations focus on generative AI's dual role in creating and combating disinformation, including ChatGPT's impact on academic writing and fake news propagation. Recent publications (2022-2025) reveal three dominant trends: First, systematic analysis of Wikipedia's reliability across languages during crises like the pandemic and Ukraine war. Second, development of AI-driven fact-checking frameworks (e.g., OpenFact project's CLEF 2023 victory). Third, exploration of generative AI's societal impact ranging from student creativity to disinformation campaigns. Scientific awards received: Best Paper Award at ICIST 2017 Conference Award for most innovative article at NATCON 2018 conference Microsoft Azure for Research Award (2016) As academic advisor, he leads the 'Semantic Technologies' diploma seminar attracting high-achieving students, with participants winning the 29th UEP Foundation Competition (2025) and Eurostat's Web Intelligence Challenge (2024). His grant portfolio includes the 'Maritime Big Brother' project (2017) for ship voyage prediction using AIS data and Microsoft Azure funding for Wikipedia quality enhancement. Ongoing initiatives include OpenFact (fake news detection) and GOBLIN projects. He actively collaborates with SKN Data Science student circle (evidenced by 2024/2025 inaugural meeting) and international consortia like CLEF and QOD workshops. Departmental leadership involves managing the OpenFact research team that achieved top results in CheckThat! Lab competitions, alongside maritime data analytics groups applying evolutionary algorithms to shipping networks.
David G. Rand is the Erwin H. Schell Professor of Management Science and Brain and Cognitive Sciences at MIT, with affiliations to the MIT Institute for Data, Systems, and Society and the Initiative on the Digital Economy. His research bridges behavioral economics and psychology, focusing on decision-making dynamics between intuitive and deliberate processes, particularly in contexts of cooperation, misinformation, political behavior, and social media. He holds a B.A. in Computational Biology from Cornell University (2004) and a Ph.D. in Systems Biology from Harvard University (2009), followed by a postdoc in Harvard’s Psychology Department. Before MIT, he served as an Assistant and then Associate Professor at Yale University. Rand’s work explores how intuitive biases and cognitive shortcuts shape beliefs about false news, political preferences, and social media behavior. His interventions using AI-driven dialogues and accuracy prompts have shown promise in reducing conspiracy beliefs and misinformation sharing. He has received prestigious awards, including the Arthur Greer Memorial Prize (2015) and recognition from the Poynter Institute (2017). His articles frequently address misinformation mitigation, AI applications, and polarization, appearing in top journals like Nature , Science , and Psychological Science . He also engages the public through popular media outlets like The New York Times and Wired . Rand leads the Applied Cooperation Team, a research group exploring cooperation and prosocial behavior. His work emphasizes scalable solutions to societal challenges through behavioral science and technology.
Dion Hoe-Lian Goh is a researcher at Nanyang Technological University, Singapore , with a focus on information science, human-computer interaction, and digital literacy. His work explores emerging technologies like deepfakes and conversational AI , gamification in education, and information behavior in social media platforms. Research trends in his recent publications include deepfake detection strategies , AI-driven educational frameworks , and social dynamics in misinformation . His studies span diverse populations such as Generation Z and senior citizens , emphasizing the role of technology in public perception and information verification. Key subfields of his work include computational thinking education , crowdsourcing , digital nudges , and user experience in gamified systems . While no formal awards are listed, his contributions to journals like J. Assoc. Inf. Sci. Technol. and conferences such as HCI and ICADL highlight his impact in academic circles.
Cristian Danescu-Niculescu-Mizil is an Associate Professor in the Department of Computer Science at Cornell University's College of Engineering, where he leads research at the intersection of computational social science and human-computer interaction. His work examines how language shapes and reflects social dynamics in digital environments, with applications for platform design and community health. His research focuses on computational social science , natural language processing , and human-computer interaction , particularly analyzing online discourse evolution, social influence mechanisms, and community dynamics. Key methodologies include large-scale data analysis, machine learning, and longitudinal modeling of language behavior across platforms like Reddit, Twitter, and Wikipedia. Analysis of his recent publications reveals strong trends in cross-platform misinformation tracking , AI-driven content moderation , and linguistic adaptation in support communities . His work consistently bridges technical NLP advances with sociological insights, emphasizing ethical implications and real-world interventions for healthier online ecosystems. Scientific recognition includes: NSF CAREER Award for foundational work in computational social science Multiple industry research awards from Google and Facebook ACM SIGCHI Best Paper Award for community health research He actively mentors doctoral students in computational social science, with several advisees now faculty at top institutions. His research is supported by NSF grants focused on social dynamics modeling and NLP for community health, often collaborating with social scientists and industry partners. Current projects investigate AI moderation systems and longitudinal language evolution in online communities through the Digital Discourse Lab .
Dr. Shirin Nilizadeh is an Associate Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington's College of Engineering. She leads the Security and Privacy Research Lab, conducting interdisciplinary research at the intersection of cybersecurity, privacy, machine learning, and social media analysis. Her work addresses critical societal issues related to online security, privacy, and safety through data-driven approaches. Dr. Nilizadeh received her PhD in Computer Science from Indiana University in 2014, followed by MS in Computer Science from Amirkabir University (2007) and BS in Computer Engineering from Islamic Azad University (2004). Her research focuses on security and privacy in systems and social networks, employing techniques from machine learning and big data analytics. She takes a highly interdisciplinary approach, integrating AI, NLP, social sciences, and public health to address societal issues in cybersecurity and privacy. Her research objectives include: (1) detecting and characterizing emerging threats in online social networks like social engineering attacks, misinformation, and online hate speech; (2) advancing the adversarial robustness and fairness of ML and NLG systems; and (3) studying humans' online behaviors through data-driven interdisciplinary research. Analysis of her recent publications reveals a strong focus on AI-generated security threats, particularly phishing scams using LLMs, NFT fraud detection, social media toxicity analysis, and content moderation systems. Her work bridges theoretical security research with practical applications, often addressing real-world security challenges through innovative technical solutions. Among her notable scientific achievements are the prestigious NSF CAREER award (2023), Comcast Innovation Awards (2022 and 2024), College of Engineering Outstanding Early Career Research award (2024), and IEEE SP 2024 Distinguished Paper Award. Her work has also received best paper and technical poster awards at eCrime 2021 and NDSS 2022. Dr. Nilizadeh has successfully mentored numerous doctoral and master's students while securing significant research funding, including multiple NSF grants and Comcast Innovation Fund awards. She leads a vibrant research group that has produced impactful work cited in official reports submitted to The Supreme Court and the EU Committee on Civil Liberties, Justice, and Home Affairs. Her lab has also received coverage from WIRED, MIT Technology Review, Orange's Hello Future, and Communications of the ACM. She serves on numerous program committees for top international conferences including ACM CCS, USENIX Security, and POPETS, and has organized outreach programs like OurCS@DFW to broaden participation of underrepresented students in computing.
Milena Stróżyna is an Assistant Professor at the Department of Economic Informatics in the University of Economics in Poznan , Poland. Her work focuses on data modeling, AI applications in disinformation detection, and maritime data analysis . Email: milena.strozyna@ue.poznan.pl Research interests span: Data Modeling & Analysis : Extracting insights from diverse data sources, ensuring quality, and implementing ERP systems Disinformation Studies : Developing AI tools for fake news detection and semantic mapping of misinformation topics Maritime Data Science : Crisis impact analysis in shipping, anomaly detection in maritime transport Scientific Contributions include: Pioneering OpenFact system for information verification Creating adversarial text detection methods Leading research on generative AI risks in information integrity Notable Awards : 2018: Most innovative article at NATCON conference Multiple first-place international competition wins with OpenFact system (2022-2024)
Azza Abouzied is Associate Professor of Computer Science at New York University Abu Dhabi and Global Network Associate Professor at the Tandon School of Engineering. She serves as Vice Provost for Faculty Advancement and Engagement at NYUAD starting September 2024. Her research bridges database systems and human-computer interaction, focusing on intuitive tools for data querying and decision-making in uncertain, collaborative environments. PhD, Yale University (2013) MPhil, Yale University MSc, Dalhousie University BSc, Dalhousie University Her research centers on human-data interaction, designing systems that make data accessible to non-experts. She combines techniques from UI design, machine learning, and databases to build tools that simplify complex data tasks. Her earlier work focused on example-driven querying and synthetic data generation, while her recent work explores in-database prescriptive analytics and decision support in domains like disinformation mitigation and epidemic planning. Her publications span database and HCI venues, with a recurring theme of enhancing usability without sacrificing scalability. She co-founded Hadapt, a Big Data analytics platform, and has led interdisciplinary research through the Human-Data Interaction Lab and the Center for Interacting Urban Networks. Her teaching includes foundational courses such as Database Systems, Operating Systems, and Data, as well as the critical thinking course Techruption. VLDB Test of Time Award (2019) Best Paper Award in Database Systems Honorable Mention in HCI Publications Azza mentors undergraduate capstone students and advises prospective PhDs, research assistants, and postdocs. She is actively involved in academic leadership, having chaired NYUAD’s faculty council in 2024 and co-chaired the SIGMOD 2025 program. Her work emphasizes empowering users to critically engage with data and AI, both in research and education.
Francisco Javier Canton Correa is a researcher at the Universidad de Granada, specializing in sociology. He earned his PhD in 2019 with the thesis Socialización digital y creatividad audiovisual , supervised by Dr. Jordi Alberich Pascual. Education: PhD in Sociology (2019), Universidad de Granada Research Interests focus on digital sociology, social media analysis, and the societal impact of artificial intelligence. His work explores disinformation combat, visual communication, and urban culture through computational methods. Recent Publications highlight AI-driven tools for misinformation detection, social media analytics, and critical perspectives on digital humanities. Key trends include cross-platform verification systems and sociological risk frameworks for global disinformation. Labs & Collaborations include Medialab UGR, where he contributes to cross-media communication experiments.
Sijia Yang is an Associate Professor at the School of Journalism and Mass Communication at the University of Wisconsin-Madison . His research focuses on message effects and persuasion in digital media, particularly in public health and science communication. He employs experimental, computational (e.g., causal machine learning, multimodal analysis), and community-engaged methods to address challenges in health communication interventions. Ph.D. in Communication: Annenberg School for Communication, University of Pennsylvania (2019) M.A. in Communication: University of Illinois at Urbana-Champaign (2012) B.A. in English Language and Literature: Renmin University of China (2010) His current research explores three key areas: (1) moralization/politicization of health issues and intervention design, (2) causal machine learning for evaluating multimodal messages, and (3) leveraging AI for pro-social messaging in underserved communities. His work bridges computational methods with societal impact, emphasizing ethical considerations and practical applications. Recent articles highlight his contributions to topics like cannabis warning labels, misinformation correction via TikTok, and AI-driven health chatbots. Despite no explicitly listed awards, his active research portfolio reflects significant scholarly engagement in health communication and digital media studies. His advising and grants focus on collaborative projects with rural communities and under-resourced populations, though specific grants are not detailed here. No lab or team affiliations are mentioned in the provided texts.
Dr. Aurelien Baillon is a Professor of Economics of Uncertainty at the Erasmus School of Economics , Erasmus University Rotterdam, specializing in the Department of Applied Economics . His research focuses on individual decision-making under risk and ambiguity, combining empirical and theoretical approaches to understand probability elicitation and expert opinion aggregation. Key research areas: Behavioral Economics, Risk Attitudes, Bayesian Modeling Major projects: Bayesian Markets , Personal Model of Trumpery , Malakoff Humanis Chair His recent publications explore ambiguity theories , cybersecurity decision-making , and linguistic deception detection . Notable grants include the ERC Starting Grant (2016) and NWO Vidi Grant (2014). Collaborations span institutions like BRiO , HITS Institute , and GATE . The Datavisualization project with Alice Havrileck demonstrates his interdisciplinary approach to uncertainty analysis.
Arjun Mukherjee is a Lecturer at the Department of Computer Science , University of Houston , where he teaches courses in Machine Learning , Data Mining , Natural Language Processing , and Data Structures . His research focuses on Bayesian Inference , Data Mining , Natural Language Processing , Sentiment Analysis , Opinion Spam , and Web Mining , with a strong emphasis on deception detection and social media analysis. His recent publications explore advanced techniques in LLM-generated content detection synthetic data applications cross-domain deception modeling temporal user behavior analysis , reflecting his commitment to addressing modern challenges in digital content authenticity and machine learning robustness. Dr. Mukherjee has developed educational materials for graduate-level courses, including a well-structured Machine Learning course (COSC 6342) covering probabilistic inference, supervised/unsupervised learning, and neural networks. He earned his Ph.D. from the University of Illinois at Chicago in 2014, with a thesis titled Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications .
Alberto Barrón-Cedeño is an Associate Professor at the Department of Interpreting and Translation, University of Bologna (Italy). He holds a PhD in Artificial Intelligence from Universitat Politècnica de València (Spain) and has served as a former Scientist at Qatar Computing Research Institute (Qatar) and an Alain Bensoussan fellow at Universitat Politècnica de Catalunya (Spain). His research focuses on computational propaganda detection, automated fact-checking systems, and natural language processing applications in combating misinformation. Education: PhD in Artificial Intelligence, Universitat Politècnica de València, Spain Research Interests: Dr. Barrón-Cedeño specializes in developing AI-driven solutions for detecting disinformation, particularly in social media and news content. His work emphasizes computational propaganda analysis, fake news detection during crises (e.g., the COVID-19 pandemic), and designing tools to assist human fact-checkers. He leads initiatives such as the CLEF CheckThat! Lab to advance interdisciplinary approaches in misinformation research. Awards: Alain Bensoussan fellowship (Universitat Politècnica de Catalunya) Advising & Grants: While no formal advisees are listed, he collaborates extensively with global teams on funded projects related to computational fact-checking and propaganda detection. Labs & Teams: Core contributor to the CLEF CheckThat! Lab, which coordinates international efforts to develop benchmarks and tools for detecting check-worthy claims and fake news.
