About

Kincho Law is a Professor of Civil and Environmental Engineering at Stanford University. His professional and research interests focus on the application of computational and information science in engineering. His work has dealt with various aspects of computational mechanics and structural dynamics, AI and machine learning, large scale database management, Internet and cloud computing, numerical methods, and high performance computing. His research application areas include computer aided engineering, legal and engineering informatics, engineering enterprise integration, web services and supply chain management, monitoring and control of engineering systems, smart infrastructures, and smart manufacturing. He holds a PhD and an MS in Civil Engineering from Carnegie Mellon University, obtained in 1981 and 1979 respectively, and a BSc in Civil Engineering and a BA in Mathematics from the University of Hawaii, both earned in 1976.

Research topics

• Computer Science
• Engineering
• Artificial Intelligence
• Transport engineering
• Meteorology
• Geography
• Data science
• Real-time computing
• Civil engineering
• Automotive engineering
• Simulation
• Structural engineering
• Risk analysis (engineering)
• Cartography
• Environmental resource management
• Business
• Environmental science

Selected publications

• Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning

  ArXiv.org · 2026-04-25

  articleOpen access

  Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization analysis. While mobile robot teams offer the potential to actively improve observation quality, existing multi-robot monitoring and active perception approaches typically rely on coverage or visitation based objectives that are weakly aligned with the accuracy requirements of human-centric monitoring tasks. In this work, we formulate cooperative active observation as a decentralized control problem in which multiple robots adjust their motion to directly optimize monitoring accuracy under partial observability. We propose a learning-based framework for cooperative policies from decentralized observations using multi-agent reinforcement learning (MARL), supported by an architecture that handles variable numbers of humans and temporal dependencies. Simulation results across diverse indoor environments and monitoring tasks show that the proposed approach consistently outperforms classical coverage, persistent monitoring, and learning-free multi-robot baselines, while remaining robust to changes in the number of observed humans.

  PublisherOA PDF

• Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning

  arXiv (Cornell University) · 2026-04-25

  preprintOpen access

  Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization analysis. While mobile robot teams offer the potential to actively improve observation quality, existing multi-robot monitoring and active perception approaches typically rely on coverage or visitation based objectives that are weakly aligned with the accuracy requirements of human-centric monitoring tasks. In this work, we formulate cooperative active observation as a decentralized control problem in which multiple robots adjust their motion to directly optimize monitoring accuracy under partial observability. We propose a learning-based framework for cooperative policies from decentralized observations using multi-agent reinforcement learning (MARL), supported by an architecture that handles variable numbers of humans and temporal dependencies. Simulation results across diverse indoor environments and monitoring tasks show that the proposed approach consistently outperforms classical coverage, persistent monitoring, and learning-free multi-robot baselines, while remaining robust to changes in the number of observed humans.

  PublisherDOI

• Hybrid Energy Harvesting from Natural Wind and Traffic-Induced Bridge Vibrations

  Journal of Bridge Engineering · 2025-10-23 · 1 citations

  articleSenior author

  This paper describes the development and validation of a hybrid electromagnetic energy harvester capable of simultaneously scavenging energy from two different energy sources: low-speed natural wind and traffic-induced bridge vibrations. The harvester employs a cantilever structure designed to exhibit significant vibrations due to aeroelastic instability even at low wind speeds and dynamic excitation from traffic-induced bridge vibrations through an electromagnetic transduction mechanism. It is noted from the laboratory tests using a shaker and wind tunnel that the electrical load resistance significantly influences vibration amplitude, generated power, and the onset velocity of galloping in the harvester. Wind tunnel test results also reveal the necessity of adjusting load resistance according to wind speed for optimal power output. This study provides approximate analytical solutions for the optimal external load resistance and corresponding maximum power under wind and base excitation separately. These solutions are validated through comparisons with numerical simulations and wind tunnel tests. Additionally, under hybrid excitations, particularly at low wind speeds and accelerations, a notable enhancement in average power output is observed. Combining wind with vibration-only or base excitation with galloping-only harvesting significantly improves power output, ensuring enhanced energy availability even if one source is interrupted. Field tests on the bridge confirm the effectiveness of the present harvester in generating electricity from both wind-induced galloping and bridge vibrations, demonstrating its capability to harness energy from two complementary sources. Overall, this hybrid harvester presents a promising solution for maximizing energy capture from ambient natural sources, contributing to advancements in sustainable energy generation.

  PublisherDOI

• A Category-Theoretic Approach to Neural-Symbolic Task Planning with Bidirectional Search

  2025-01-01

  articleOpen accessSenior author

  We introduce a Neural-Symbolic Task Planning framework integrating Large Language Model (LLM) decomposition with categorytheoretic verification for resource-aware, temporally consistent planning.Our approach represents states as objects and valid operations as morphisms in a categorical framework, ensuring constraint satisfaction through mathematical pullbacks.We employ bidirectional search that simultaneously expands from initial and goal states, guided by a learned planning distance function that efficiently prunes infeasible paths.Empirical evaluations across three planning domains demonstrate that our method improves completion rates by up to 6.6% and action accuracy by 9.1%, while eliminating resource violations compared to the existing baselines.These results highlight the synergy between LLM-based operator generation and category-theoretic verification for reliable planning in domains requiring both resourceawareness and temporal consistency.

  PublisherOA PDFDOI

• A Mobile Robot Framework for Learning to Detect New Objects With Large Language Models

  Journal of Computing and Information Science in Engineering · 2025-08-17

  articleSenior author

  Abstract Truly autonomous mobile robots must detect both known and unknown objects. This article proposes a fast, real-time open set object detector (OSOD) that enables mobile robots to identify both known and unknown objects. By leveraging a small YOLO model with pseudolabels provided by a multimodal large language model (LLM), we develop an effective open set object detector for edge devices. We replace the traditional human-in-the-loop for interpreting novel objects with a multimodal LLM, which automatically provides semantic information (name and properties) from images, automating the information learning process. Once a mobile robot acquires the semantics of an unknown object from an LLM, a vision-language model classifies repeated instances of the object, reducing the number of slow LLM queries. Meanwhile, an object's properties provided by an LLM allow a mobile robot to operate more effectively around the new object. To facilitate incremental learning, images and labels of novel objects are stored as they are encountered. Once a sufficient number of instances of a novel object are compiled, the original object detector is retrained to include the new object in the set of known objects. Demonstrated with real mobile robots in an academic office building, the incremental learning method showcases how mobile robots can learn to detect novel objects without human intervention. Source code is available at the URL provided in Note 2.

  PublisherDOI

• Ontology-based Adaptive Knowledge System (OAKS): Adaptive and Consistent Knowledge Acquisition through LLMs for Diverse User Backgrounds

  2025-07-08

  articleSenior author

  Although most of the research on large language models (LLMs) focuses on their development and validation against datasets, significant gaps remain in their application to real-world knowledge-intensive tasks. This research addresses key challenges in using LLMs for extracting and synthesizing knowledge from unstructured sources, focusing on applications where the validity and consistency of the results are critical. We propose Ontology-based Adaptive Knowledge System (OAKS) as a holistic approach to manage the complexities of acquiring unstructured knowledge, varying user expertise, and dynamic query formulation. This research provides practical value for enabling the domain user community to leverage their technical documentation and expertise and accelerate ongoing working projects through improved literature review and cross-disciplinary insight discovery. Validated through empirical studies, our findings offer insight into best practices for the deployment of OAKS, bridging the gaps between AI capabilities and real-world needs in knowledge acquisition and research.

  PublisherDOI

• Modeling Crowd Data and Spatial Connectivity as Graphs for Crowd Flow Forecasting in Public Urban Space

  2024-01-25 · 1 citations

  articleSenior authorCorresponding

  Predicting crowd flow patterns in a physical space can be useful for infrastructure management and safety planning. A simple representation of individuals in Euclidean space is insufficient for representing people’s spatial distribution and movements over time. This paper describes a spatiotemporal graph formulation, namely crowd mobility graphs (CMGraphs), to represent the spatiotemporal data. The CMGraphs model employs dynamic node features that store temporal crowd flow information, while the time-invariant edges represent spatial connectivity of locations of interests in the surrounding space. The spatiotemporal formulation using the CMGraphs allows for crowd flow prediction. Specifically, graph neural network is used to aggregate neighborhood nodal information on CMGraphs to capture spatial connectivity. Subsequently, recurrent neural network is employed to generate future sequences of crowd flow. An experiment is conducted using a publicly available video dataset at a train station to demonstrate the effectiveness of the proposed CMGraph formulation for crowd flow forecasting.

  PublisherDOI

• Identification and Interpretation of Melt Pool Shapes in Laser Powder Bed Fusion with Machine Learning

  Smart and Sustainable Manufacturing Systems · 2024-04-08 · 3 citations

  articleSenior author

  ABSTRACT Laser powder bed fusion (LPBF) is a popular additive manufacturing process with many advantages compared with traditional (subtractive) manufacturing. However, ensuring the quality of LPBF parts remains a challenge in the manufacturing industry. This work proposes the use of unsupervised learning, specifically, the k-means clustering method, to identify unique melt pool shapes produced during LPBF manufacturing. Melt pools are a key process signature in LPBF and can assist in the evaluation of process quality. k-means is employed multiple times sequentially to produce clusters of melt pools, and the silhouette value is used to identify the optimal number of clusters. The clusters produced by k-means are used as labels to train a deep neural network to classify the melt pool shapes. By inputting the melt pool image and the corresponding LPBF machine process parameters into the neural network, the neural network identifies the melt pool shape to aid human analysis and provide insight into part quality. The trained neural network is interpreted using explainable artificial intelligence (XAI) methods to investigate the relationships between process parameters and the melt pool shape. Using layer-wise relevance propagation, the process parameters that most significantly influence the melt pool shapes are identified. The relationship between process parameters and melt pool shapes can be useful for selecting the process parameters to produce the desired melt pool shapes. In summary, this study describes an approach that combines unsupervised machine learning and XAI methods to effectively enable the analysis and interpretation of melt pools.

  PublisherDOI

• The role of life-cycle civil engineering practices in smart and sustainable cities

  2023-06-28

  book-chapterOpen accessSenior author

  Globally, civil infrastructure and our built environment is being embedded with dense sensing networks. These networks form the technological foundation of “smart cities” and enable a new, data-driven era of infrastructure engineering for increased lifecycle performance. But smart cities require more than simply embedding IT hardware into existing urban infrastructure, or the application of artificial intelligence to analyze municipal data streams. This paper discusses the set of physical infrastructures, digital technologies, regulations and policies, financing mechanisms, community outreach, businesses and business models, partnerships, institutions, and other engagement mechanisms that must be established in concert with each other to provide a high quality of life in smart and sustainable cities. Specifically, the role of infrastructure life-cycle evaluation is studied in the context of smart city traffic management systems. Results show that smart city traffic management can result in significant reductions in life cycle impacts associated with urban mobility and transportation.

  PublisherOA PDFDOI

• Explainability of Laser Powder Bed Fusion Melt Pool Classification Using Deep Learning

  2023-08-20 · 5 citations

  article

  Abstract Laser powder bed fusion (LPBF) has shown enormous potential for metal additive manufacturing in recent years. However, the relationship between the LPBF process parameters and part quality is not yet fully understood. Some LPBF machines now use cameras to monitor melt pools during manufacturing. Machine learning techniques have been proposed to analyze the melt pool data and to evaluate the quality of the manufacturing process. However, these machine learning techniques often appear as a black box and the underlying decisions made by the machine learning models are unknown. This paper proposes a neural network to classify the melt pool shapes using melt pool images and process parameters as model inputs. With both process parameters and the melt pool image being included, an explainable artificial intelligence (XAI) approach is developed to interpret the neural network and understand the relationships between the melt pool shape and the process parameters. Specifically, layer-wise relevance propagation (LRP) is used to reveal the relevance of process parameters in the neural network’s decision-making. Using LRP, relationships between the process parameters and melt pool shapes are revealed without explicit knowledge of the underlying physics. These relationships can potentially be used to adjust the process parameters and improve the quality of LPBF manufactured parts. This paper demonstrates how neural networks and XAI can effectively identify relationships between process parameters and LPBF melt pools.

  PublisherDOI

Recent grants

• Decentralized Structural Control Strategies with Wireless Sensing and Actuation

  NSF · $250k · 2008–2013

• Interoperation, Mediation and Composition of Engineering Services

  NSF · $350k · 2006–2010

• III-CXT-Small: Collaborative Research: REGNET - Information Management and Compliance Assistance for Patent Laws and Regulations

  NSF · $258k · 2008–2014

• CPS: Synergy: Collaborative Research: Enhanced Structural Health Monitoring of Civil Infrastructure Systems by Observing and Controlling Loads using Cyber-Physical Systems

  NSF · $279k · 2015–2018

Frequent coauthors

• Jerome P. Lynch

  Duke University

  88 shared

• Hoon Sohn

  Korea Advanced Institute of Science and Technology

  62 shared

• Anne S. Kiremidjian

  42 shared

• Steven J. Fenves

  42 shared

• Osama Abudayyeh

  41 shared

• Gloria T. Lau

  Stanford University

  41 shared

• William Rasdorf

  North Carolina State University

  41 shared

• Hubo Cai

  China Electric Power Research Institute

  38 shared

Education

• Ph.D., Civil Engineering

  Stanford University

  1990

• M.S., Civil Engineering

  Stanford University

  1985

• B.S., Civil Engineering

  University of California, Berkeley

  1983