HAI was established to support innovative AI research that bridges disciplines and fields. The Institute aims to appoint and support promising researchers through its fellowship programs who are working at intersections often overlooked by traditional academic departments, in addition to outstanding researchers pursuing core disciplinary topics.
HAI Postdoctoral Fellowship Program
HAI’s Postdoctoral Fellowship Program offers opportunities to explore topics, conduct research, and collaborate across disciplines related to AI technologies, applications, or impact.
HAI is currently seeking postdoctoral applicants for multiple fellowship opportunities.
Mordecai Lab focuses on the ecology of infectious disease. We are interested in how climate, species interactions, and global change drive infectious disease dynamics in humans and natural ecosystems. This research combines mathematical modeling and empirical work. Learn more
The postdoctoral fellow would develop tools for tracking the recovery and activity of the North American beaver (Castor canadensis). After being nearly hunted to extinction, followed by decades of sanctioned trapping, beavers are now increasingly recognized as solutions to a number of sustainability challenges – from water storage to fire and flood suppression to biodiversity. However, the massive growth in beaver populations is not without risk. In addition to nuisance flooding and infrastructure damage, beavers alter the hydrology of river corridors in complex ways that may change the water balance (increased evapotranspiration) and impact water quality (increased stream temperatures and metal release). Thus, although beavers are profound ecological engineers, they are not always compatible with human infrastructure. The postdoctoral fellow would address the role of North American beaver as a sustainable ecological solution by adapting approaches from computer vision to (1) recognize the ecological patterns (networks of channels, ponds, lodges, vegetation structure) of beaver activity and (2) distinguish legacy structures from recent activity using patterns of roughness, brightness, continuity and vegetation. If the ecological patterns can be learned, then we can advance to the next stage of evaluating the beaver as a tool for fostering sustainable waterways. Learn more
The “Examining Human/AI Augmentation in the Workplace: Challenges and Consequences” is a multi-sited ethnographic study that examines the organizational structures, processes, and practices that could enable (or constrain) Human-AI augmentation in the workplace, and its consequences for workers, occupational groups, and organizations. Learn more
Mineral X Initiative’s Project: Energy transition minerals and the rainforest: AI for smarter mineral exploration and sustainable mining practices. As the energy transition accelerates, AI technologies are being used to speed up critical mineral explorations. Critical minerals, including Nickel, Cobalt, Copper, and Lithium, are in high demand for building towards 100% renewable power through electrification and batteries. However, such speed-up using AI raises environmental and human concerns: a majority of battery metals such as Ni and Co come from rainforests, including the Amazon, Indonesia, and the Congo. With this project, we aim to develop AI to plan smart, responsible, and sustainable critical mineral explorations in rainforest areas. Currently, environmental and human concerns are only raised at the mining stage, which is too late. We will develop a POMDP-based sequential planner that accounts quantitatively for environmental and human concerns. This AI planner includes reward structures related to water, energy, and land uses. The purpose is to allow exploration companies to abandon projects discovered unsuitable at the exploration stage rather than the late mining stage. We aim to set a leading example for sustainable mining in the rainforest, preserving the environment and justice, and accelerating the supply for energy transitions. Learn more
Stanford Intelligent and Interactive Autonomous Systems Group (ILIAD), led by Professor Dorsa Sadigh, develops algorithms for AI agents that safely and reliably interact with people. Our mission is to develop theoretical foundations for human-robot and human-AI interaction. The goal of the proposed research is to develop caregiving robots that can provide long-term assistance with activities of daily living (ADLs) to people with mobility limitations. Based on the central tenet that robots need to optimize both physical and social interactions to provide efficient, safe, and personalized assistance for ADLs, this work will focus on developing robot-assisted feeding as a long-term caregiving solution for a person with upper-extremity disability in an unstructured, real-home environment. Successful feeding consists of bite acquisition and bite transfer; our methods integrate those activities towards the development of an intelligent and personalized robot-assisted feeding system. Our models leverage multimodal feedback to develop human-in-the-loop control policies that adapt to a range of human and environmental factors. Realizing that full autonomy can be challenging in unstructured and dynamic environments, the proposed methods will leverage expert human feedback while minimizing the cognitive load, and interweave them intelligently with autonomy to arrive at a long-term caregiving solution. Our work will have a direct impact on public life, health, and comfort. Developing policies that consider the human in the loop at every step and learn from their feedback through multiple modalities, e.g., comparisons, physical feedback, or language, will have an impact on many other human-robot interaction domains including but not limited to assistive teleoperation of robots. Learn more
The HAI Postdoctoral Fellow will lead a new project in the Remote Sensing Ecohydrology Group, led by Professor Alexandra Konings in the department of Earth System Science. This project aims to build a new hybrid AI – biogeophysical model to estimate the spatio-temporal dynamics of water flow through the soil and water uptake by plants. The approach will constrain a model of subsurface flow and vegetation water uptake with readily available global data of aboveground vegetation water content, leaf area index, and other variables to constrain patterns of belowground water uptake. Automatic differention-enabled deep learning methods will be used to ensure the satellite data are able to constrain the belowground inferences. Specific research questions addressed with the model could include but are not limited to: a) quantification of the relationship between microwave remote sensing derived proxies and vegetation hydrologic conditions across the globe, b) representation of those belowground components of the hydrologic cycle where classical understanding has been shown incorrect, and c) AI-inspired parameterization techniques to transfer the model’s findings across scales and regions. Learn more
Deployed AIs must navigate challenging tradeoffs. How much should a medical AI risk a false positive vs. a false negative? How much harm is allowable vs. the benefit provided by the AI? The research will develop algorithms, interfaces, and applications of metric elicitation from human feedback.
Metric elicitation is a framework designed to aid humans in identifying and balancing trade offs relevant to decision-making with AIs in settings where humans are the best judge of preferred outcomes, i.e., where a gold standard does not exist, and ideal outcomes depend on stakeholder preferences. Once identified, these preferences can be used for auditing decision systems, training automated decision-making models, and explaining decision-making processes. However, its success relies on developing effective interactive techniques for eliciting preferences and tradeoffs from people. Learn more.
The postdoctoral fellow will join the Stanford Sustainable Systems (S3L) Lab and work on a specific project to develop NLP models to track granular information on climate change impact on people/infrastructure as well as residential clean energy technology adoption (e.g., EV chargers, batteries, electric heating) from widely-available textual data (e.g., news articles, building permits, social media posts). The successful development and deployment of the NLP models will further enable the construction of large-scale database to uncover the spatial heterogeneity of climate change impact and decarbonization/electrification status and to identify key opportunities in adaptation and energy decarbonization to inform future decision making. The research lab and the specific project will leverage AL/ML to help improve the sustainability, resilience, and equity of people and the infrastructure and technologies they rely on amidst the growing threat of climate change. The AI/ML models developed in the project will facilitate other researchers to address similar questions for sustainability and equity, while the data and insights gained in the project will inform policy making and capital allocation to develop more equitable, resilient, and decarbonized communities. Learn more.
HAI Graduate Fellowship Program
The Institute for Human-Centered Artificial Intelligence (HAI) offers a 2-quarter program for Stanford Graduate Students. The goal of this program is to encourage interdisciplinary research conversations, facilitate new collaborations, and grow the HAI community of graduate scholars who are working in the area of AI, broadly defined. The fellowship fosters collaboration between engineers, social scientists, humanists, and others researching the future of purposeful, intentional, and human-centered AI.
HAI is seeking graduate students to participate in this program. We would like to ensure the cohort is well-rounded across disciplines.
Learn more about the Graduate Fellowship Program
Stanford Digital Economy Lab (DEL) Postdoctoral Fellowships
DEL is currently seeking Postdoctoral Fellows. Positions focus on key topic areas related to the digital economy. Each hire will have a priority project, led by Professor Erik Brynjolfsson, as well as the opportunity to work with other Stanford faculty and PIs on additional topics. Priority projects will be shaped based on shared interests, available data, and alignment with DEL’s research priorities.
In the News
Read more about the work of our Fellows.
Jazmia Henry: Building Inclusive NLP
The HAI and CCSRE fellow hopes to bring the complexity and value of African American Vernacular English to natural...
What Previous Industrial Revolutions Can Reveal about the U.S.-China Race for AI Leadership
A Stanford researcher says the key to a nation’s economic power is less about which one is first to develop a major new...
Stanford HAI Names Seven New Post and Pre-doctoral Fellows
These scholars will join 11 returning fellows to study AI technologies, applications, and societal impact.
For questions related to the Stanford Institute for Human Centered Artificial Intelligence fellowship programs, please contact HAI-Fellowships@stanford.edu.