Fengqi Li Joins CASE as Assistant Professor
September 15th, 2025
CASE is thrilled to welcome Assistant Professor Dr. Fengqi (Frank) Li to the CASE team this semester. Dr. Li earned his Ph.D. at CASE from which he went on to become an R&D Associate Staff Member in the Grid-Interactive Controls Group at Oak Ridge National Laboratory (ORNL). His work sits at the intersection of architecture, computational design, and energy systems—advancing urban building energy modeling (UBEM), AI-driven urban and energy planning, and community-scale electrification and climate resilience.
We were lucky enough to do an interview with Frank where he shares insights about his research, his inspiration and his goals for his new role at CASE. Read on to get to know our newest faculty member.
Q: What is your research area of expertise?
Dr. Li: Urban Building Energy Modeling and Calibration, System Design and Optimization, Reinforcement Learning Model Development, Integrated Building Technology Design, Urban and Grid System Integration, Future Weather Analysis, Big Data Management, Agent-based Modeling, Interaction Design.
Q: Tell us a little bit about your background and how your research has evolved throughout your career
Dr. Li: I was trained as an architect, and my interdisciplinary work is driven by visible paradigm shifts—climate change, AI, IoT, high-performance computing, renewable technologies, and increasingly decentralized patterns of living. I study how these forces reshape the built environment and, in turn, how we might responsibly imagine futures for cities. At ORNL, I worked in the Grid Interactive Controls group as a computational developer, building software and managing large datasets for national-scale building-energy simulations, while also taking on broader computational support and project management as projects required.
At Syracuse University and The Cooper Union, I pivoted to systems thinking and human–environment computation—pursuing Wall Parley (AI-enabled installation, ACADIA 2016) and VEDA (VR-based energy analytics for Urban Energy System analyses). During my Ph.D. at RPI’s Center for Architecture Science and Ecology (CASE), my research focused on developing Infomophism, an urban planning framework that integrates local energy exchange networks to maximize a city’s ability to absorb, store, and locally share renewable energy. Using Manhattan as a testbed, the computational framework aims to optimize energy flow, supply, and demand and open a new avenue for managing collective urban form and energy infrastructure as a coherent system.
Q: What inspired you to study urban energy systems?
Dr. Li: The question of what a future city could be—and why—has long challenged architectural discourse. Urban form evolves through policies and regulations that respond to social, economic, technological, and environmental forces; negotiating these forces continues to reshape how we imagine cities in the future. Today, energy has moved to the center of planning conversations: climate changes and rapid population growth demand efficiency, electrification, and integration of renewables at urban scale. Leading assessments now position city-scale energy planning as a primary lever for emissions reduction and resilience.
At the same time, centralized grids are no longer the city’s only operating base. Distributed energy resources and microgrids have become objects of design, reframing infrastructure as something to be placed, optimized, and governed in concert with urban zoning, mobility, and public space. Planners and engineers are building new tools to guide renewable integration—and the policy, equity, and reliability questions that come with it.
Against this backdrop, my research focuses on developing computational frameworks that combine system-optimization and AI-based learning to co-design collective urban form and new urban energy systems. Conceptually, it proceeds from the view that renewable-energy potential functions as a shared urban commons; accordingly, it advances planning processes that secure equitable access to distributed resources and their benefits.
Q: What classes are you teaching this semester?
Dr. Li: I am teaching the "Design Research Studio" and "System Prototyping" this semester.
Q: What do you hope to achieve through your research in this new chapter at CASE? What types of research do you hope develop?
Dr. Li: In this new chapter at CASE, my goal is to build an open, multi-scale design-and-optimization platform for urban decision-making—spanning energy, transportation, health, land use, and related domains—that co-optimizes urban form and distributed energy resources to strengthen both city and grid resilience, and that translates those insights into policy and regulatory guidance so development aligns with emerging energy initiatives. Conceptually, the work treats decentralization as a design variable, examining how edge/IoT and DER-rich infrastructures reconfigure urban system operations and governance.
Methodologically, the potential agenda advances AI-assisted optimization and learning models, data-oriented urban analytics, and a FAIR-aligned data-sharing layer so results are interoperable and reusable across projects. The intended outcome is a decision-support stack—platform, models, datasets, and policy playbooks—that helps cities stage the future energy transition responsibly while managing risk and distributing benefits. In general, my research will probe the relationship between decentralized systems and emerging technologies, and develop computational models and AI applications that interrogate dynamics between an evolving built environment and the technological advances emerging within it.
CASE is grateful to welcome Dr. Li back as a distinguished Alumnus to continue his research, mentor students and share his expertise in alternative planning frameworks and computational tools for urban design and planning that optimize urban form, infrastructure roles, and emergent urban topologies. Visit our people page to read a full description of Dr. Li’s background and achievements.
Images from left to right:
Envisioning a Human-Machine Collaboration, 2018
The loop: Information-based urban infrastructure system design, 2017
Intelligent Architectural Element Design, 2016
Media Contact:
Kathie Brill, Program Manager
Brillk@rpi.edu