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University research directory
Research labs at
Stanford University.
Compare 5 listed faculty-led profiles, explore their research interests, and follow the evidence in their selected work. These listings are a starting point for discovery and do not establish recruiting availability.
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Review each lab’s official website and recent publications. Compare the methods used, the questions being asked, and the practical requirements of any published opening. Unclaimed profiles are independently curated; the university has not approved or endorsed them.
How to compare research labs ↗Official university website ↗5 research-led lab profiles
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Independently curated · UnclaimedCollaborative Haptics and Robotics in Medicine (CHARM) Lab
Stanford University
Allison M. Okamura · Richard W. Weiland Professor of Engineering; Professor of Mechanical Engineering
How can a robot communicate through touch? Allison Okamura’s CHARM Lab designs the devices, models, and control methods that make physical interaction useful in robotics and medicine. Its projects connect wearable haptic garments, surgical teleoperation, soft robots, and training environments. Haptiknit explores knitted structures with distributed stiffness for wearable feedback; loop-closure grasping investigates how a robot can hold objects strongly and gently. Researchers combine mechanical design, fabrication, control, and studies of human interaction to understand both what a device does and how a person experiences it.
Independently curated · UnclaimedDan Boneh Research Group
Stanford UniversityDan Boneh · Professor of Computer Science and Electrical Engineering
Can cryptographic proofs scale to verify large model computations without revealing model parameters? The group develops succinct zero-knowledge and folding-based SNARK constructions to reduce proof size and verification cost for large computations. Researchers design homomorphic and lattice-based encryption schemes and residue-number-system techniques to enable arithmetic on encrypted large integers. They prototype verifiable computation and authenticated-data systems for scalable image and web security, combining IOPs and accumulators to enable integrity checks at scale. The lab evaluates web, mobile, and blockchain security, producing protocols for privacy-preserving airdrops and accountable threshold signatures.
Independently curated · UnclaimedNoah S. Diffenbaugh Research Group
Stanford UniversityNoah S. Diffenbaugh · William Wrigley Professor, Department of Earth System Science
What climate phenomena most directly drive impacts on people and ecosystems at regional scales? The group quantifies how fine-scale climate processes and extreme events drive impacts on water resources, agriculture, health and poverty vulnerability using numerical modeling and data analysis. Researchers combine high-resolution climate modeling, machine learning attribution, and statistical downscaling to detect and attribute recent extreme heat, precipitation, and wildfire-smoke changes. They develop integrated frameworks linking climate-driven hazards to economic and health outcomes to estimate mortality and monetized damages from wildfire smoke and extreme heat. The lab evaluates adaptation and mitigation scenarios to predict timing and probabilities of regional warming thresholds and future extremes.
Independently curated · UnclaimedRob Jackson Research Group
Stanford UniversityRob Jackson · Professor, Earth System Science
How do human activities alter greenhouse gas fluxes and the carbon cycle across ecosystems and regions? The research establishes global monitoring networks (including methane tower measurements at many sites) and develops mobility and climate models to quantify emissions. The group measures methane, carbon dioxide, and other greenhouse gases from landscapes, oil and gas infrastructure, and urban sources and combines field measurement, atmospheric inversions, and machine-learning to resolve source-sink dynamics. Researchers integrate observations into global carbon budgets and policy-relevant analyses of emissions and mitigation.
Independently curated · UnclaimedStanford Vision and Learning Lab
Stanford UniversityFei-Fei Li · Sequoia Capital Professor
How can computational models enable machines to form human-like visual interpretations of objects, scenes, and actions? SVL constructs large-scale datasets and benchmarks such as BEHAVIOR to evaluate embodied AI agents on household tasks in realistic simulation environments. The lab develops algorithms for object and activity recognition including the Multi-Object Multi-Actor (MOMA) framework and activity graphs for compositional activity parsing in videos. SVL integrates multisensory datasets like ObjectFolder with benchmarks to train models that reason about 3D object properties from sight, sound, and touch. Research combines computational geometry, automated image and video analysis, and visual reasoning methods to study foundational principles and practical implementations of high-level visual perception.
Showing 1–5 of 5 labs