In this talk, Prof. Max will begin with a broad overview of current and recent research initiatives within the Laboratory for Air Transportation, Infrastructure, and Connected Environments (LATTICE) at the University of Michigan. The presentation will then delve into two research projects in greater technical depth. The first project addresses distributional uncertainty in strategic air traffic management, particularly in airport ground delay programs. By incorporating robust machine learning predictions of airport capacities, this approach reduces the cost of delays even under overly optimistic forecasts. The second project explores congestion management frameworks for Urban/Advanced Air Mobility (UAM/AAM). It proposes a bi-level optimization strategy that operates within a coordinated architecture of fleet operators, airspace service providers, and central traffic control authorities, offering insights into future scalable traffic systems.

Max Li is an Assistant Professor of Aerospace Engineering at the University of Michigan, Ann Arbor, with courtesy appointments in Civil and Environmental Engineering and Industrial and Operations Engineering. He earned his PhD in Aerospace Engineering from MIT in 2021 and holds MSE and BSE degrees from the University of Pennsylvania.

His research spans air transportation systems, airport and airline operations, Advanced Air Mobility, and networked systems, with a strong methodological foundation in optimization and control. He is also the director of the LATTICE lab, where interdisciplinary insights drive innovation for the future of air mobility.

AI and machine learning have drawn significant interests in recent years and have found many applications in engineering. The purpose of this presentation is to discuss the potential uses of machine learning models for a variety of applications.  The presentation covers a broad review of AI research and development in engineering and introduces selected examples to illustrate the broad applications of the AI from natural language interface, regulation management, machine diagnostic, and control problems to smart manufacturing and mobility technologies for the built environment. 

Kincho H. Law received his B.Sc. in Civil Engineering and B.A. in Mathematics from the University of Hawaii in 1976, and M.S. and Ph.D. in Civil Engineering from Carnegie Mellon University in 1979 and 1981, respectively.  After serving as Assistant Professor at Rensselaer Polytechnic Institute from 1982 to 1988, he joined Stanford University in 1988 and is currently Professor of Civil and Environmental Engineering.  Prof. Law’s professional and research interests focus on computational and information science in engineering.  His research has dealt with various aspects of high performance computing; sensing, monitoring and control of engineering systems; legal and engineering informatics; enterprise integration; smart manufacturing; web services and cyber-physical systems.  He has authored and co-authored about 500 articles in journals and conference proceedings.   

Prof. Law was the recipient of the ASCE Computing in Civil Engineering Award in 2011 and the Excellence in Research Award by ASME’s Division of Computers and Information in Engineering in 2023.  He has received a number of best paper awards from the American Society of Civil Engineers (ASCE), American Society of Mechanical Engineers (ASME), the Institute of Electrical and Electronics Engineers (IEEE), Digital Government Society and others. He has been on the advisory boards for a number of start-up companies on Data Analytics, IoT Platform for Manufacturing, Autonomous Vehicles, and others.  Prof. Law was elected Distinguished Member of ASCE in 2017, Fellow of ASME in 2017, Life Member of ASCE in 2018, and Senior Member of IEEE in 2019.

In this seminar, we introduce SimMobility, an agent-based simulation platform that designed to help urban planners and policymakers predict the impacts of mobility services and technologies at an urban scale. We present key applications that evaluate the impacts of introducing Automated Mobility-on-Demand (AMOD) services, designing demand-responsive adaptive transit systems, and integrating passenger-freight mobility across user, operator, and network performance. Furthermore, a recent case study in Korea demonstrates the potential of combining this simulation platform with deep generative models to accurately predict population distribution and travel demand over the large-scale metropolitan region.

Simon Oh is an Associate Professor of Transportation and Mobility in the Department of Mobility Science and Engineering at Korea University, Principal Investigator of the Smart Mobility Lab, and Vice President of the Research and Business Foundation Sejong Campus. He earned PhD degree in Civil and Environmental Engineering from KAIST in 2015 and worked as a Senior Postdoctoral Associate at the Future Urban Mobility IRG in Singapore-MIT Alliance for Research and Technology (SMART) from 2016 to 2021, where he developed behavioral models and integrated demand and supply models into an agent-based urban mobility simulation platform, SimMobility. His expertise includes traffic simulation modeling, calibration and optimization, automated mobility-on-demand (AMOD) systems, and big data for predictive analytics.