Find Academic Supervisors

Rebekah LarsenView profile

Assistant Professor

• Media Ecosystems
• Sociotechnical Systems
• Digital Media Research
• +6 more

Rebekah Larsen is an Assistant Professor at the Massachusetts Institute of Technology (MIT) in the Comparative Media Studies department. She is also a Faculty Associate at Harvard Law School’s Berkman Klein Center for Internet and Society and a Research Associate at the Center for Governance and Human Rights (Cambridge University). Prior to her MIT faculty role, she held positions including a Marie Curie Fellow at the University of Copenhagen and a Postdoctoral Fellow at Oslo Metropolitan University. Education: PhD in Sociology (University of Cambridge, 2022), MA in Technology Policy (Cambridge, 2015), BA in Science, Technology, and Society Studies (University of Pennsylvania, 2012) Affiliations: Berkman Klein Center (Harvard), Center for Governance and Human Rights (Cambridge), EU Marie Curie program Rebekah’s research focuses on understudied media ecosystems , particularly at the intersection of sociotechnical change , power relations , and dis/misinformation . Her work spans conservative talk radio in rural Utah , platform-funded fact-checking networks , and search engine manipulation of journalists . She employs mixed methods (ethnography, computational analysis) and critiques digital research practices. Her publications address topics like platform power , fact-checking technologies , and data privacy debates . Key themes include media accountability , hybrid digital-broadcast systems , and policy implications for news deserts . Scientific Awards: Recognized by the Association of Internet Researchers (AoIR) (2022) for her PhD dissertation’s policy relevance Awarded a Collier Grant (MIT Outdoor Club, 2019-2020) for outdoor inclusivity Marie Curie fellowship grant (EU) Rebekah has advised on journalistic responses to misinformation and taught courses at MIT, including CMS 100: Introduction to Media Studies . She contributes to peer-reviewed journals like New Media and Society and Digital Journalism , and her methodological work explores data colonialism and ethnographic challenges in partisan environments.

Gary J SchwitzerView profile

Adjunct Associate Professor

• health care journalism
• health communication
• media ethics
• +4 more

Gary J Schwitzer is an Adjunct Associate Professor at the School of Public Health , University of Minnesota , and founder of HealthNewsReview.org . With over 30 years of academic activity, his work focuses on improving health care media messages and training medical students in digital literacy. Key Research Areas : Health communication, medical journalism ethics, evidence-based teaching methods, and conflict-of-interest detection in health reporting Notable Grants : Multiple awards from Laura and John Arnold Foundation, National Inst for Health Care Management, and Henry J Kaiser Family Foundation Scientific Contributions : 2024: Developed lateral reading techniques for medical students 2019: Investigated 'spin' in health news 2018: Analyzed medical intervention reporting trends 2020: Addressed pandemic science communication Awards & Recognitions 2014 - American Medical Writers Association's John P. McGovern Award 2006 - Knight-Batten Award for Journalism Innovation 2007 - Mirror Award for Media Industry Reporting 2008-2009 - Poynter Institute Ethics Fellowship His work intersects journalism and public health, emphasizing accurate medical communication through faculty leadership and digital platform development.

Celia Sancho BelinchónView profile

Professor

• Media Literacy
• Digital Communication
• Advertising Ethics
• +3 more

Celia Sancho Belinchón is a faculty member at the School of Communication and Arts within Universidad Nebrija , specializing in Audiovisual Communication and Advertising . She holds a PhD from Universidad Complutense de Madrid (2018) for her research on social values in brand communication, focusing on Coca-Cola's advertising evolution (1971-2015). Education : PhD, Universidad Complutense de Madrid (2018). Her research explores media literacy , digital communication ethics , and social responsibility in advertising . She investigates inclusive discourse in digital spaces, AI-driven misinformation combat , and media representation of ecological transitions . Her publications highlight the interplay between social media, advertising trends, and health communication . Recent work focuses on technological innovations in media , fact-checking practices , and influencer dynamics in public health and environmental narratives. She collaborates on educational initiatives like the XX AdN film festival and uses tools like GoalPro® for career development in advertising education. Contact: csanchobe@nebrija.es

• Communication Studies
• Semiotics
• Media Ethics
• +5 more

Paulo Barroso is an Assistant Professor at the Polytechnic Institute of Viseu , with prior roles at institutions including Polytechnic Institute of Guarda and University of Algarve. He holds a Ph.D. in Philosophy from the University of Santiago de Compostela, Spain. Doctor of Philosophy (2007), University of Santiago de Compostela, Spain His research spans communication studies, semiotics, and media ethics, focusing on topics such as digital literacy, ideological representation in advertising, and the philosophical implications of media. He has extensively published on the interplay between ethics, religion, and communication, including works on Wittgenstein’s philosophy of psychology, Baudrillard’s hyperreality, and Lotman’s semiosphere. His recent publications (2022-2023) emphasize crisis communication, visual rhetoric, and the ethical challenges of misinformation. Articles like "Governmental communication strategies in crisis management: the COVID-19 pandemic in Portugal and Brazil" and "Digital literacy in the brave new e-sphere" highlight his engagement with contemporary societal issues. He has supervised Master’s theses on media impacts, including "The consumption of animal protein (meat) and the environmental impacts on private Portuguese television channels" and "The importance of using color in advertising communication."

Vincent F HendricksView profile

Professor

• Formal epistemology
• Logic
• Formal learning theory
• +6 more

Vincent F Hendricks is a Professor of Formal Philosophy at the Department of Communication, Faculty of Humanities, University of Copenhagen, where he also leads the Center for Information and Bubble Studies (CIBS). His academic career spans over two decades, with significant contributions to formal epistemology and the philosophical analysis of information dynamics in digital society. Current position: Professor of Formal Philosophy, University of Copenhagen (2009-present) Leadership: Director of Center for Information and Bubble Studies (CIBS) (2015-present) Previous positions: Professor at Roskilde University (2006-2009) Editorial roles: Former Editor-in-Chief of Synthese (2005-2015) Hendricks holds a dr. phil. from Aalborg University (2004) and a PhD in Philosophy from the University of Copenhagen (1997). His educational background includes a Candidate of Philosophy degree (1993) and an Examination in Philosophy (1991), both from the University of Copenhagen. Vincent F Hendricks specializes in formal epistemology, logic, and the philosophy of science, with particular focus on information processing and the analysis of bubbles in democratic systems. His research explores how information spreads, how collective beliefs form, and how these dynamics impact democratic processes and financial systems. He has pioneered work on 'infostorms' - the phenomenon where information cascades create distorted perceptions of reality. His current research project, 'Agency and multi-modal systems, information processing and the analysis of democracy' (IPAD), examines how digital platforms reshape democratic discourse and decision-making processes. Hendricks' work bridges formal logical frameworks with real-world applications in social media, finance, and political systems. His recent publications reveal a strong focus on contemporary challenges in the digital age, particularly concerning misinformation, attention economies, and the psychological mechanisms behind 'whataboutism' and self-centered discourse. The 2025 publications show a progression from earlier work on information bubbles toward more nuanced analyses of individual behavior within digital information ecosystems, with increasing attention to game-theoretic models of social interaction and moral philosophy. CHOICE MAGAZINE Outstanding Academic Title Prize (2006) Videnskabsministeriets Eliteforskerpris (1,000,000 DKr, 2008) Carlsbergfondet grant to establish CIBS (14.4 million Dkr, 2015) Rosenkjærprisen (2016) Lærebogsprisen for 'Tal en tanke' (2006) Hendricks has secured substantial research funding, most notably the 14.4 million Dkr Carlsbergfondet grant to establish the Center for Information and Bubble Studies. He has served as principal investigator on multiple interdisciplinary projects examining the intersection of philosophy, information science, and social dynamics. His advisory role extends to public policy, where he contributed to the TechDK Commission's reports on technology's societal impacts. Hendricks has also been active in public intellectual engagement, hosting TV and radio programs that make philosophical concepts accessible to broader audiences. As director of the Center for Information and Bubble Studies (CIBS), Hendricks leads a multidisciplinary team examining how information spreads, how bubbles form in digital environments, and how these phenomena impact democratic processes. The center brings together researchers from philosophy, computer science, psychology, and social sciences to develop both theoretical frameworks and practical interventions for improving information ecosystems. CIBS has become a hub for research on misinformation, attention economies, and the philosophical implications of digital communication platforms.

Zhijiang GuoView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Large Language Models
• +4 more

Zhijiang Guo is an Assistant Professor at the DSA Thrust, HKUST (GZ), and an Affiliated Assistant Professor of HKUST. Previously, he was a Senior Researcher at Huawei Noah's Ark Lab and a Postdoc at the Department of Computer Science and Technology at the University of Cambridge, where he was also a member of Trinity College. Dr. Guo earned his PhD in Computer Science from Singapore University of Technology and Design (SUTD) in 2020 under Professor Wei Lu. During his doctoral studies, he was a visiting student at the University of Edinburgh (2019-2020), collaborating with Professors Shay Cohen and Giorgio Satta on Structured Prediction. His undergraduate education was completed at Sun Yat-sen University. Dr. Guo's research focuses on natural language processing and machine learning, with particular emphasis on large language models (LLMs). His work explores fundamental questions about knowledge representation and reasoning capabilities in LLMs, examining how these systems understand and process information. Key research areas include logical consistency in language models, efficient code generation, autoformalization techniques, meta-reasoning benchmarks, and parameter-efficient fine-tuning methods. His work bridges theoretical insights with practical applications requiring systematic reasoning and knowledge representation. Dr. Guo's publication record demonstrates significant contributions to advancing reasoning capabilities in language models. His work spans top-tier conferences including ICML (with spotlight papers in 2025), NeurIPS (including an oral presentation in 2024), ICLR (with spotlight papers in 2024), and ACL. His research shows a clear progression from foundational LLM capabilities toward more sophisticated reasoning systems, with particular attention to evaluating and improving logical consistency, developing meta-reasoning benchmarks, and creating frameworks for automated alignment evaluation. Dr. Guo has served as an Area Chair for NeurIPS 2025, reflecting his standing in the research community. His work on MR-Ben introduced a novel benchmark for evaluating System-2 thinking in LLMs, while his contributions to AVeriTeC created an important dataset for real-world claim verification with web evidence. Dr. Guo actively seeks strong and motivated students to join his research group. His advising philosophy emphasizes fundamental research questions about LLM capabilities while maintaining practical relevance. Current projects focus on understanding the transition from System 1 to System 2 reasoning in language models, with significant implications for creating more reliable and trustworthy AI systems. Dr. Guo leads research efforts focused on the intersection of knowledge representation and reasoning in large language models. His group develops new methodologies for evaluating and improving logical consistency, knowledge composition, and reasoning processes in AI systems. Current projects include investigating the transition from intuitive to deliberate reasoning in LLMs, developing efficient fine-tuning techniques like HydraLoRA, and creating frameworks for automated alignment evaluation in complex reasoning tasks.

Amjad MaazView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +6 more

Amjad Maaz is an Assistant Professor in the Department of Computer Science at Texas Tech University. His research bridges technology and social good, focusing on AI applications in public health, education, and media integrity. He advocates for equitable education through AI-driven classroom innovations and empowers underrepresented communities in computing. Specializes in Natural Language Processing , Misinformation Detection , and Hate Speech Identification . Collaborated on AI solutions for the opioid crisis in Texas using big data approaches. Serves as Co-Editor at PLOS ONE and organized international shared tasks on fact-checking and content integrity. Promotes responsible AI integration in education to enhance student outcomes.

Eni MustafarajView profile

Assistant Professor

• Socio-technical systems
• Web credibility assessment
• Misinformation detection
• +7 more

Eni Mustafaraj is an Assistant Professor of Computer Science at Wellesley College , a women's liberal arts institution in Massachusetts. She studies web-based socio-technical systems with a focus on algorithmic transparency and credibility assessment of online sources. PhD in Computer Science (2007) from Philipps University of Marburg, Germany Previously a visiting scholar and Norma Wilentz Hess Fellow (2010-2013) Her research explores intersections of human behavior and algorithmic systems, particularly misinformation detection , web literacy tools , and data science education . She pioneered the use of MIT App Inventor in undergraduate programming education and developed TwitterTrails for tracking online misinformation. Notable awards include: NSF CAREER Grant (2018-2023) for credibility signals research Best Paper prize at Web Science Conference (2010) Mellon grant for evidence-based teaching (2017) Dr. Mustafaraj mentors undergraduate researchers in the Cred Lab and DAV Lab , focusing on web literacy, data visualization, and computational thinking. Her work has been featured in media outlets like The New York Times and The Spoke, and she actively contributes to academic discourse through public writing on platforms like Medium.

Arkaitz ZubiagaView profile

Senior Lecturer

• Natural Language Processing
• Computational Social Science
• Misinformation Detection
• +7 more

Arkaitz Zubiaga is a Senior Lecturer (Associate Professor) at Queen Mary University of London, where he co-leads the Social Data Science lab and serves as Director of Graduate Studies. He is also part of the leadership team of the Centre for Human-Centred Computing. His research sits at the intersection of Computational Social Science and Natural Language Processing, focusing on developing NLP and LLM methods for processing social media and Web data to tackle societal harms. Zubiaga's research interests concentrate on addressing problematic issues with damaging societal effects, including hate speech, misinformation, inequality, biases, and other forms of online harm. He investigates how LLMs can be misused for malicious purposes such as spreading misinformation, generating abusive content, or exacerbating societal biases. His work emphasizes detecting and addressing irresponsible AI use where content is falsely claimed to be human-generated. His publication record shows a clear trend toward addressing bias in detection systems, particularly in cyberbullying detection where swearing bias has been identified as a critical issue. His recent work explores zero-shot and few-shot learning approaches for cross-lingual applications, stance detection, and claim verification. The research spans multiple disciplines including computational linguistics, social computing, and AI ethics, with a growing focus on multimodal approaches and longitudinal model evaluation. 2024 OSNEM best survey award for work on session-based cyberbullying detection Zubiaga actively mentors PhD students, with Peiling Yi recently passing her viva (April 2025) and welcoming new PhD students Alaa Bazaid and Ali Khairallah. He serves as senior area chair for ACL 2025 and leads the HYBRIDS MSCA Doctoral Network. His work demonstrates a strong commitment to developing responsible AI systems that can detect and mitigate online harms while addressing critical issues of bias and fairness in computational approaches.

• Machine Learning
• Autonomous Driving
• Natural Language Processing
• +4 more

Tsvetomila Mihaylova is a Postdoctoral Researcher in the Department of Computer Science at Aalto University , specializing in machine learning and human-robot interaction. Her work bridges theoretical advancements in neural networks with practical applications in autonomous systems. Fields of Interest : Latent structure learning, autonomous driving, visual-language models, and natural language processing Email : tsvetomila.mihaylova@aalto.fi Research Focus : • Latent structure modeling using discrete and undirected neural networks • Autonomous driving safety through conflict simulation and trajectory prediction • Cross-modal integration in robotic vision-language systems Publication Trends : Recent work explores 2025 in structured neural architectures for autonomous vehicles and 2024 in human-robot interaction quality evaluation, with foundational contributions to natural language processing fact-checking systems since 2019 .

Lucy Lu WangView profile

Assistant Professor

• Natural Language Processing
• Health Informatics
• Accessibility Research
• +3 more

Lucy Lu Wang is an Assistant Professor at the University of Washington Information School, where she leads the Language Accessibility Research (LARCH) Lab. She holds adjunct appointments in the Paul G. Allen School of Computer Science & Engineering, Biomedical Informatics & Medical Education, and Human Centered Design & Engineering. Additionally, she serves as a Research Scientist at the Allen Institute for AI (Ai2). Dr. Wang is an NLP and health informatics researcher focusing on designing and building language technologies to improve access to and understanding of information, particularly in high-expertise domains like science and healthcare. Her work has produced techniques and tools that enhance access to scholarly content, synthesize scientific evidence, and support better health decision-making. She specializes in creating AI systems that make complex information more accessible, with particular attention to accessibility for visually impaired users and improved health communication. Her recent publications demonstrate strong trends across natural language processing for scientific literature, accessibility research, and health informatics. The work shows consistent focus on developing practical tools that bridge the gap between advanced AI capabilities and real-world information needs, particularly in scholarly and healthcare contexts. Many projects address critical issues of accessibility, reliability, and usability of AI systems for diverse user populations. Dr. Wang actively mentors a diverse group of students across multiple departments, including PhD students in the iSchool, HCDE, and CSE. She has successfully guided numerous students who have gone on to positions at institutions like UIUC, Meta, Microsoft, and Netflix. Her research is generously supported by multiple grants and gifts from Google, the National Institutes of Health, Microsoft, and various University of Washington funding programs, focusing on AI-driven accessibility solutions, healthcare applications, and research translation. At the LARCH Lab, Dr. Wang leads a team working on designing, building, and evaluating language technologies that make information more accessible and understandable for everyone, with special emphasis on high-expertise domains such as healthcare and science communication.

Dan RothView profile

Professor

• Machine Learning
• Natural Language Understanding
• Computational Learning Theory
• +4 more

Dan Roth is the Eduardo D. Glandt Distinguished Professor at the University of Pennsylvania in the School of Engineering and Applied Science , Department of Computer and Information Science . His research integrates machine learning and inference methods to advance natural language understanding, focusing on semantic modeling, information extraction, and constrained conditional models. Research Interests : Dan’s work spans machine learning , inference methods , and their applications in natural language processing , including semantic role labeling, co-reference resolution, and textual entailment. He emphasizes incidental supervision and declarative learning-based programming via tools like LBJava and Saul. Article Trends : Recent publications highlight multimodal learning , LLM uncertainty quantification , and temporal table reasoning , with keywords like Computer Science , Artificial Intelligence , and Medical Informatics . Subfields include vision-language models , hybrid SQL-LLM systems , and multilingual guardrails . Scientific Awards : DARPA Information Science and Technology Advisory Panel (2020) IJCAI John McCarthy Award (2017) Fellow of AAAS (2014) University of Illinois College of Engineering Advising Award (2013) Fellow of ACL (2012) Fellow of ACM (2011) Best Student Paper at CoNLL (2011) Best Paper at Army Science Conference (2010) Fellow of AAAI (2009) Xerox Faculty Research Award (2005) Advising and Grants : Dan has advised PhD students such as Sihao Chen and Haoyu Wang . His projects include BLINK (multimodal LLMs) and DiverseAgentEntropy (LLM uncertainty quantification). Labs and Teams : Dan leads the Cognitive Computation Group , which focuses on computational theories of intelligence and their implementation in machine learning and NLP systems.

ChengXiang ZhaiView profile

Professor

• Intelligent information systems
• Information retrieval
• Data mining
• +4 more

ChengXiang Zhai is the Donald Biggar Willett Professor in Engineering at the University of Illinois at Urbana-Champaign , holding appointments in the Department of Computer Science , Carl R. Woese Institute for Genomic Biology , and the Department of Statistics . He leads research in intelligent information systems, with a focus on information retrieval, data mining, NLP, and machine learning. His TIMAN research group and DAIS explore applications in healthcare, education, and scientific discovery. He develops MOOCs on text retrieval and mining, and has published extensively on topics including LLM alignment, user simulation, and multimodal systems. Key Research Areas : Intelligent search engines, explainable AI, human-AI collaboration, biomedical informatics Recent Trends : LLM economics, knowledge overshadowing, just-in-time recommendation systems He has received the ACM Fellow title, SIGIR Salton Award , and multiple teaching honors including Rose Award and Graduate Mentoring Award . He serves as series editor for Springer Information Retrieval Book Series .

Anqi LiuView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Distributionally Robust Learning
• +5 more

Anqi Liu (Angie) is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She is affiliated with the Data Science and AI Institute, Mathematical Institute for Data Science (MINDS), and Institute for Assured Autonomy (IAA). Her research focuses on developing principled machine learning algorithms for reliable, trustworthy, and human-compatible AI systems in high-stakes applications. PhD in Computer Science from University of Illinois Chicago Postdoctoral Research at Caltech's Department of Computing and Mathematical Sciences Her work emphasizes robustness to changing data environments, uncertainty quantification, and human-AI interaction. Key methodologies include distributionally robust learning, active learning, safe exploration, fair machine learning, and conformal prediction. Applications span healthcare (NIA/NIH-funded), robotics, and computational social science. Amazon Research Award Johns Hopkins + Amazon Initiative for AI Faculty Research Johns Hopkins Discovery Award Institute for Assured Autonomy Challenge Grant She advises PhD candidates in AI safety and fairness, with students co-advised by faculty in Human-Robot Interaction and Computational Linguistics. Collaborations include Center for Language and Speech Processing (CLSP) and Laboratory for Computational Sensing and Robotics (LCSR).

• Artificial Intelligence
• Machine Learning
• Healthcare Informatics
• +6 more

Oliver Krauss serves as an Assistant Professor at FH Upper Austria (University of Applied Sciences Upper Austria) at the Hagenberg Campus. He is affiliated with multiple research centers including the Research Center Hagenberg, Web Intelligence and Innovation Laboratory, AIST Assistive Technology Lab, Center of Excellence Medical Technology/TIMed Center, Center of Excellence for Smart Production, and Digital Media Lab. His work falls under the Strength area ICT - Information & Communication Technology with additional connections to HEAL initiatives. Dr. Krauss's research spans several interconnected domains with a strong focus on practical applications of artificial intelligence. His primary research interests include Healthcare Informatics (particularly HL7 FHIR standards), Language Models and Natural Language Processing, Web Intelligence for user behavior analysis, Assistive Technology applications, and Process Mining for business optimization. His work demonstrates consistent interdisciplinary connections between computer science fundamentals and real-world healthcare and business applications. Analysis of Krauss's recent publications (2023-2025) reveals a clear trajectory toward increasingly sophisticated AI applications. His research has evolved from foundational IoT and sensor data processing toward advanced language model applications in healthcare informatics, e-commerce personalization, and fact-checking systems. A notable pattern is his ability to bridge theoretical AI concepts with practical implementations in medical technology and business intelligence, often incorporating emerging technologies like GPT-3 and other large language models into domain-specific solutions. Dr. Krauss has received significant recognition for his work, including: FH Upper Austria Young Researcher Award (2019 and 2022) HL7 Austria Student Award for Master's Thesis (2015) Best Paper Award at Intelligent Systems and Applications 2022 His research activities include substantial project leadership and collaboration, with notable projects including MARIE (Mobile Robotic Assistance for Office Environments), CRISAM® Loop (AI-based risk management), and EDEN (Emergency Detection for Elevator Networks). He has maintained consistent publication output with 74 publications to date and has been active in scientific communities through conference participation, invited lectures (including on HL7 FHIR topics), and committee work. His h-index of 6 reflects a solid research impact with 110 citations. Dr. Krauss leads research within multiple specialized laboratories including the Web Intelligence and Innovation Laboratory where he applies language models to website visitor segmentation, the AIST Assistive Technology Lab focusing on healthcare applications, and contributes to the Center of Excellence Medical Technology/TIMed Center where his work on patient medical records analysis has significant clinical implications.