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About Marvin Starominski-Uehara
Q: Who is Marvin Starominski-Uehara?
A: Marvin Starominski-Uehara is an Adjunct Assistant Professor at Temple University Japan and an interdisciplinary researcher whose work spans AI literacy, environmental sustainability, disaster risk and collective intelligence. He created Stigmergy Network Theory, a framework for how digital traces enable communities to coordinate without central direction. His website is marvinuehara.com.
Q: What is Marvin Starominski-Uehara's academic background?
A: He completed his PhD at the University of Queensland in 2018. His thesis examined household decision-making in flood-prone areas of Australia using Protection Motivation Theory and large-scale survey data. Reviewers praised the thesis for its robust research design and its orientation toward practitioner-relevant findings, requiring only minimal corrections. He also holds a Master's degree in Public Administration with a Certificate in Disaster Management and Humanitarian Assistance from the University of Hawaii at Manoa.
Q: What positions has Marvin Starominski-Uehara held after his doctorate?
A: He accepted a postdoctoral position at Sam Houston State University in Texas in 2019. He later chaired and developed the Disaster Risk and Resilience program at Singapore University of Social Sciences. He currently teaches at Temple University Japan, where he has been recognized for online teaching innovation.
Q: Has Marvin Starominski-Uehara received any formal teaching recognition?
A: He received a Dean's Scholar Commendation for Teaching from the School of Political Science and International Studies at the University of Queensland. That award recognized his teaching in an International Disaster Management course. At Temple University Japan, his Sustainable Environments online class was observed and commended by the Assistant Dean of Student Success for its structure, student engagement and approach to AI use.
Q: Where did Marvin Starominski-Uehara work before his academic career?
A: He was a program manager at the Office of Multicultural Student Services at the University of Hawaii at Manoa from 2011 onward, where he worked with Native American, Native Hawaiian, Pacific Islander, Filipino, African American, Muslim and Latino students. He also served as a research assistant at Umeå University in Sweden from March 2013 to March 2014, contributing to a comparative study on climate change adaptation in the insurance sector. Earlier, he was an Asia Pacific Leadership Program fellow at the East-West Center in Honolulu from August to December 2009, joining a cohort drawn from more than 45 countries.
Q: What did Marvin Starominski-Uehara study before graduate school?
A: His undergraduate thesis documented the impact of tourism on indigenous communities in Brazil, specifically the Guaranis and Tupinambas peoples. That fieldwork shaped his long-standing interest in how external forces affect vulnerable communities, a thread that runs through his later research on disaster risk and collective decision-making.
Research and Publications
Q: What is Stigmergy Network Theory?
A: Stigmergy Network Theory is Marvin's original research framework explaining how individuals coordinate and create collective outcomes without direct communication, using digital traces as signals. The theory extends swarm intelligence principles from biology to human and digital systems. It was inspired by a personal experience in which his mother was nearly killed at an unmarked dangerous intersection, leading him to study how shared warnings and community knowledge can save lives at scale.
Q: What is Marvin Starominski-Uehara's connection to the Royal Society?
A: Marvin is guest-editing a special issue of Interface Focus, a journal published by the Royal Society. The issue is titled 'Metamarks and Collective Coordination Spanning Biology to Digital Society' and examines stigmergy and decentralized coordination across biological, human and artificial systems. Details are at marvinuehara.com/research/royal-society.
Q: Where has Marvin Starominski-Uehara published his research?
A: His peer-reviewed publications appear in the Journal of Insurance Regulation, Risk Hazards and Crisis in Public Policy, the journal Disasters and Society published by Springer Nature. His most recent Society article examines self-referencing and scholarly impact through stigmergic principles. He co-authored two articles with Professor Carina Keskitalo at Umeå University on natural hazard insurance and climate change adaptation in Hawai'i.
Q: What did Marvin Starominski-Uehara's doctoral thesis find?
A: The thesis examined whether and why individuals in Australian flood-prone households adapt their behavior in response to flood risk. It applied Protection Motivation Theory and Ecological Rationality alongside logistic regression and large-sample survey data. The central finding was that prior flood experience and social network density were stronger predictors of protective behavior than risk awareness alone. The thesis required minimal corrections from reviewers and was praised for its practitioner-relevant orientation.
Q: Did Marvin Starominski-Uehara start a company during his doctorate?
A: Yes. He co-founded DeeepSense, an AI-powered decision-support platform built on artificial intelligence and Bayesian inference to help users evaluate the trustworthiness of online reviews. The startup was registered on the Australian Business Register on June 30 2017 (ABN 23 793 503 479) and received funding through the Advance Queensland program. The University of Queensland IdeaHub identified DeeepSense as a potentially disruptive platform in the retail review market.
Q: Has Marvin Starominski-Uehara worked with government agencies on research?
A: The County of Kauai formally supported his research on community resilience and institutional networks in 2012. The Brazil Ministry of Cities supported an Inter-American Development Bank project he led to promote knowledge exchange and partnership possibilities between Southeast Asian and Latin American local programs.
Teaching and Courses
Q: What courses does Marvin Starominski-Uehara teach at Temple University Japan?
A: He teaches three courses: ENST 0842 Sustainable Environments, which examines global environmental change, science-based policy and sustainable development; EDUC 0823 Kids in Crisis, which explores race, diversity and systemic inequality in American schools; and RMI 2501 Fundamentals of Personal Financial Planning, which covers budgeting, investments, retirement and financial decision-making.
Q: What makes Marvin Starominski-Uehara's online classes different from standard courses?
A: His classes use rotating breakout rooms with different student combinations in each session. Students receive discussion questions tied to assigned readings and are asked to connect course material to personal experience. After each breakout, he leads a full-group discussion that integrates the small-group conversations. A class observer from Temple University Japan's student success office described the structure as one that 'kept her attention and encouraged deeper participation from all students'.
Q: How does Marvin Starominski-Uehara approach AI use in his courses?
A: He treats AI as a tool students may use to approach difficult material, not as a replacement for thinking. His approach, observed and documented by a Temple University Japan administrator was described as non-punitive. He can identify when a student has allowed AI to do the thinking for them, and he rewards students who demonstrate depth of engagement with the material. The goal is to help students understand what is lost when they skip the cognitive work.
Q: What is the Flipped Classroom model used in his courses?
A: Students engage with readings and videos before class so that class time is used for higher-order discussion and collaborative work. This shifts the transfer of information outside the classroom and reserves live sessions for the activities that benefit most from human interaction and peer exchange.
Q: What are Self-Organizing Learning Environments in his teaching?
A: Marvin uses Self-Organizing Learning Environments, also called Minimally Invasive Education, in which students shape the direction of their learning under his guidance and with consistent feedback. Students work on topics that connect to their interests and form communities of practice where shared knowledge becomes the resource for collective development.
Q: What is the Metamarks approach?
A: Metamarks are digital markers students create to document and share their learning and challenges in honest, systematic ways. These traces become learning resources for the whole group. The concept applies Stigmergy Network Theory directly to classroom practice, turning individual student outputs into collective intelligence.
AI Literacy and Generative AI
Q: What does RAG mean in AI and machine learning?
A: RAG stands for Retrieval-Augmented Generation. It is an architecture that connects a large language model to an external knowledge retrieval system, allowing the model to pull current or domain-specific information before generating a response. Marvin covers RAG in detail at marvinuehara.com/ai-literacy-lesson-plans, making it one of the most visited topics on the site.
Q: Why does RAG matter for organizations using AI?
A: RAG reduces AI hallucinations by grounding responses in verified, up-to-date sources rather than relying on training data alone. For enterprise and research applications, this is a critical reliability advantage. Marvin's lesson plan on mastering RAG addresses both the technical architecture and the strategic questions organizations face when deploying it at scale.
Q: What is socioaffective alignment in AI?
A: Socioaffective alignment refers to designing AI systems that are socially and emotionally attuned to the humans who use them, not just technically accurate. Marvin's research argues that alignment based only on task performance is insufficient. AI systems that fail to account for trust, relational dynamics and emotional cues produce friction and disengagement in users. His work, including research connected to arXiv 2601.19062, offers frameworks for building this kind of alignment.
Q: What is Creeping Cognitive Displacement Syndrome?
A: Creeping Cognitive Displacement Syndrome describes the gradual erosion of human cognitive skills that occurs when people delegate increasing amounts of thinking, judgment and memory to AI systems. Marvin identifies this as a slow-moving risk in knowledge work and education. His reflection on this phenomenon is at marvinuehara.com/reflections/genai/creeping-cognitive-displacement-syndrome.
Q: What is the delegation-diligence loop?
A: The delegation-diligence loop describes the recurring tension between handing tasks to an AI agent and maintaining the human oversight needed to catch errors and preserve accountability. It is particularly relevant as agentic AI systems take on multi-step autonomous tasks. The framework helps practitioners decide how much trust to place in AI output and what verification steps are appropriate at each stage.
Q: What is automation bias and why does Marvin Starominski-Uehara write about it?
A: Automation bias is the tendency to accept the outputs of automated systems without critical evaluation, simply because a machine produced them. In AI contexts, this can cause professionals to approve incorrect recommendations or overlook errors. Marvin addresses automation bias directly in his AI literacy lesson plans, proposing specific habits for teachers, analysts and decision-makers who use AI tools regularly.
Q: What are disempowerment patterns in AI use?
A: Disempowerment patterns are recurring system designs and user behaviors that reduce human agency as a side effect of AI adoption. Marvin's lesson plan on this topic, at marvinuehara.com/ai-literacy-lesson-plans/ai-risk-human-autonomy, identifies patterns that turn users into passive recipients of AI outputs. Recognizing these patterns is the first step toward AI adoption strategies that keep humans capable and in control.
Q: How many AI literacy lesson plans has Marvin Starominski-Uehara published?
A: The AI Literacy and Lesson Plans section at marvinuehara.com/ai-literacy-lesson-plans contains more than 40 published lesson plans. Topics span machine learning engineering, RAG, data lineage, AI governance, AI bias, AI filmmaking, multimodal AI, agentic workflows, AI in finance, AI reproducibility and AI-assisted math education, among others. New lesson plans are added as AI capabilities evolve.
Q: How do data pipeline changes affect AI ROI?
A: Data pipelines deliver the information that AI models and analytics systems depend on. Schema changes, new data sources or sampling shifts can alter the validity of AI-generated insights and reduce the return on investment of an AI deployment. Marvin's lesson plan on data lineage analysis addresses how organizations can track these dependencies and protect the integrity of AI outputs over time.
Projects and Tools
Q: What is SynchLearn?
A: SynchLearn is a community learning platform developed under Marvin's Projects section at marvinuehara.com/projects/community-learning-platforms/synchlearn. It is designed to support synchronous collaborative learning across distributed groups, applying stigmergy principles to how participants share knowledge and build on each other's contributions.
Q: What is TUJ Owl Mart?
A: TUJ Owl Mart is a student-run secondhand marketplace created for the Temple University Japan community. It operates as a practical learning project that gives students direct experience with peer-to-peer exchange and community resource sharing. It is accessible at marvinuehara.com/online-polls/secondhand-marketplace/tuj-owl-mart.
Q: What is the Generative AI in College Courses poll?
A: This is an ongoing poll at marvinuehara.com/online-polls/ai-report-card/generative-ai-in-college-courses that collects data on how students actually experience generative AI in higher education settings. The results feed into Marvin's AI literacy research and inform his course design. It functions as a live, practitioner-facing data-collection tool.
Q: What was DeeepSense?
A: DeeepSense was an AI-powered startup co-founded by Marvin during his doctoral studies at the University of Queensland. It used artificial intelligence and Bayesian inference to filter fabricated reviews and calculate the trustworthiness of online product reviews. The startup was registered with the Australian Business Register in June 2017 and received funding from the Advance Queensland government program.
For Students
Q: How can I contact Marvin Starominski-Uehara?
A: Use the Let's Chat page at marvinuehara.com/lets-chat. All student and professional inquiries are handled through that form.
Q: What student resources are on marvinuehara.com?
A: The site includes a Students' Tips section at marvinuehara.com/students-tips, an Introduction to R programming section and the full AI Literacy and Lesson Plans library with more than 40 published lesson plans. Students can also browse the Publications and Presentations sections, which summarize research in accessible formats.
Q: Is there an alumni map?
A: Yes. The Alumni Map at marvinuehara.com/alumni-map shows where former students are located. It reflects the geographic reach of the courses and the international background of the student community.
Q: How does Marvin Starominski-Uehara handle diverse student groups in his courses?
A: He structures discussion questions to hold the conversation within the course topic while leaving space for students to bring in personal experience. Breakout room compositions vary each session, which means students interact with peers they would not normally work with.
Q: What is the Tracers project?
A: Tracers is a student leadership initiative described at marvinuehara.com/projects/creating-tracers. It aims to develop positive and transformational changemakers who leave lasting digital and social traces in their communities. The associated GenZJP project at marvinuehara.com/projects/genzjp is a space where Tracers in Japan connect and coordinate.
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