Stanford University
Showing 591-600 of 1,434 Results
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Benedikt Thiggins
Visiting Scholar, Law School
BioBenedikt Thiggins (born Huggins) is a fully qualified lawyer and Research Associate at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin. Previously, he served as a Research Associate at the Institute for German and European Administrative Law (Prof. Dr. Dr. h.c. Kahl) at Heidelberg University and at the Institute for Environmental and Planning Law (Prof. Dr. Schlacke) at the University of Münster.
He studied law at the University of Münster and completed his legal traineeship (Referendariat) at the Regional Court of Münster, including placements at the German University of Administrative Sciences Speyer. His doctoral research addresses the use of artificial light at night and its impacts as an emerging, ubiquitous environmental stressor. His research interests focus on environmental and planning law, with particular emphasis on their intersections with European Union law and constitutional law. As a Visiting Scholar at Stanford University, he examines the vulnerability of academic freedom in times of democratic regression. -
Faye Shen Li Thijssen
Ph.D. Student in Environmental Social Sciences, admitted Autumn 2025
BioFaye is a PhD student in Environmental Social Sciences. Her research focuses on the political economy of the environment with a particular emphasis on how transnational corporations shape climate politics through their global political, social, and economic embeddedness. More specifically, she is currently investigating the extent to which the increasing material costs of climate vulnerabilities influence the power dynamics between pollutive industrial corporations (as employers) and the climate policy preferences of workers in these industries.
Prior to coming to Stanford, her research focused on indirect corporate influence in environmental politics, investigating executional greenwashing and its potential to affect public perceptions and preferences for regulatory policy. This research primarily employed experimental methods, with support from the Oxford Department of Politics & International Relations and the Nuffield Centre for Experimental Social Sciences. -
Candace Thille
Associate Professor (Teaching) of Education
Current Research and Scholarly InterestsCIF21 DIBBs: Building a Scalable Infrastructure for Data-Driven Discovery and Innovation in Education: Funded by the National Science Foundation.
In collaboration with Carnegie Mellon, MIT, and the University of Memphis, we are creating a community software infrastructure, called LearnSphere, which supports sharing, analysis, and collaboration across a wide variety of educational data. LearnSphere supports researchers as they improve their understanding of human learning. It also helps course developers and instructors improve teaching and learning through data-driven course redesign.
The Learning Engineering Initiative: EdHub. Funded by the Chan Zuckerberg Initiative/Silicon Valley Community Foundation.
The EdHub project is a cross-sector initiative, to engineer the creation of a novel research and development hub in the Bay Area that is designed to integrate, by design, ongoing research in the Learning Sciences with ongoing approaches to enduring problems of practice within education.
Adaptable Learning Feedback for Instructors: The Open Analytics Research System (OARS). Funded by the Stanford VPTL Innovation Grant.
The activities and embedded assessments in online courseware provide support to students and generate fine-grained student learning data. The Open Analytic Research System (OARS) collects and models student learning data and and presents information to instructors in a dashboard to guide instruction and class activities.
Next Generation Courseware Challenge: A Partnership for Iterative Excellence in Online Courseware for College Learners. Funded by The Bill and Melinda Gates Foundation.
The OLI statistics courseware was created as an open educational resource (OER) on, the now proprietary, CMU OLI platform. In moving to Stanford, I moved the courseware to Lagunita, Stanford’s OpenEdx platform so that it would once again be an OER and extended the capabilities of the Lagunita platform to support the OLI statistics course. In collaboration with multiple partner institutions, we have continued to expand and update the courseware and conducted several learning studies. We have conducted studies in "Mindset" with Carol Dweck's (Stanford Psychology) PERTS group. In collaboration with Emma Brunskill (Stanford Computer Science), we are implementing an adaptive problem solver that uses Bayesian optimization algorithms to automatically identify which items to include in a practice set, and how to adaptively select these items in order to maximize student performance on the specified set of learning objectives and skills. Additional RCT studies that we are currently conducting in the OLI statistics courseware at our partner institutions include a study on the impact of prompting and scaffolding learners to make strategic choices about their use of course resources; and a separate study that builds affect detectors into the courseware to test the impact of timing interventions to the affective as well as cognitive state of the learner. -
Sharika Thiranagama
Associate Professor of Anthropology
BioSharika Thiranagama's work has consistently explored how political mobilization and domestic life intersect, focusing on highly fraught contexts of violence, inequality, and intense political mobilization. This work and broader comparative theorization rests on understanding how people actually live together, often in highly fractious and unequal ways, situating these processes in specific historical formations of vernacular “privates” and “publics” in South Asia.
Her work is based in Sri Lanka and in Kerala, South India. In Sri Lanka, her primary research (In My Mother’s House: Civil War in Sri Lanka 2011) was on civil war, political violence, home, displacement, militarization, family particularly intergenerational and gendered relations in wartime and post war life. This work takes these themes through the lives of Sri Lankan Tamil and Sri Lankan Muslim minorities. Other work includes the history of railways, the BBC world service, masculinity, leadership and popular militancy, etc.
Her work in Kerala focuses on the long-lasting legacy on enslavement and caste in the lives on contemporary agricultural Dalit laboring families, examining caste, gender, household economies, house and neighborhood spaces through their inheritance and future affordances. Articles examine the house, public and private in India, inheritance as inequality, and caste and neighborliness.
Her future work will take an examination of inheritance, caste and family back to Sri Lanka for new fieldwork in Jaffna with Tamils and Muslims after Sri Lanka’s recent economic collapse and postwar caste conflicts. -
Humza Thobani
Postdoctoral Scholar, Pediatric Surgery
BioHumza is a Postdoctoral Research Fellow in the Division of Pediatric Surgery at Stanford University. He earned his medical degree from the Aga Khan University in Karachi, Pakistan in 2023. Prior to joining Stanford, he had completed a dedicated research fellowship in pediatric surgery, also at the Aga Khan University, where he was named Best Research Fellow in 2024.
Humza's research interests revolve around congenital surgical anomalies, pediatric solid tumors, and pediatric inflammatory bowel diseases, with a focus on leveraging big data and machine learning methods to study rare pediatric conditions. -
Imran Thobani
Postdoctoral Scholar, Ophthalmology
BioDr. Imran Thobani is a postdoctoral scholar in Ophthalmology co-advised by Dan Yamins and Andreas Tolias as part of the Enigma project. He is interested in building large-scale predictive models of the brain that he thinks will be useful for both scientific insights and downstream biomedical applications. He did his PhD at Stanford, where he was trained in both philosophy of neuroscience and computational neuroscience, applying this training to develop better methods for comparing artificial neural network models to the brain.
