Professor Jain's research focuses on the development of data-driven and socio-technical solutions to sustainability problems facing the urban built environment. His work lies at the intersection of civil engineering, data analytics and social science. Recently, his research has focused on understanding the socio-spatial dynamics of commercial building energy usage, conducting data-driven benchmarking and sustainability planning of urban buildings and characterizing the coupled dynamics of urban systems using data science and micro-experimentation. For more information, see the active projects on his lab (Stanford Urban Informatics Lab) website.

Academic Appointments

  • Assistant Professor, Civil and Environmental Engineering

Honors & Awards

  • CAREER Award, National Science Foundation (2019)
  • Science, Engineering and Education for Sustainability (SEES) Fellow, National Science Foundation (2014)

Professional Education

  • PhD, Columbia University, Civil Engineering
  • MS, Columbia University, Civil Engineering
  • BS, University of Texas at Austin, Civil, Environmental & Architectural Engineering


  • Data-driven Sustainable Upgradation of Dharavi Informal Settlement (Mumbai, India), Stanford University


    Mumbai, India


    • Ronita Bardhan, Assistant Professor, Indian Institute of Technology - Bombay

Stanford Advisees

  • Poojan Patel
  • Doctoral Dissertation Reader (AC)
    Gitanjali Bhattacharjee, Alissa Cooperman
  • Postdoctoral Faculty Sponsor
    Marco Miotti
  • Doctoral Dissertation Advisor (AC)
    Rohan Aras, Alex Nutkiewicz
  • Master's Program Advisor
    Abdul Aleem, Benjamin Amoh, Thomas Dougherty, Jinglin Duan, Kaitlin Highstreet, Sabrina Mengrani, Kopal Nihar
  • Doctoral Dissertation Co-Advisor (AC)
    Ranjitha Shivaram
  • Doctoral (Program)
    Abigail Andrews

All Publications

  • Automated identification of urban substructure for comparative analysis. PloS one Aras, R. L., Ouellette, N. T., Jain, R. K. 2021; 16 (1): e0245067


    Neighborhoods are the building blocks of cities, and thus significantly impact urban planning from infrastructure deployment to service provisioning. However, existing definitions of neighborhoods are often ill suited for planning in both scale and pattern of aggregation. Here, we propose a generalized, scalable approach using topological data analysis to identify barrier-enclosed neighborhoods on multiple scales with implications for understanding social mixing within cities and the design of urban infrastructure. Our method requires no prior domain knowledge and uses only readily available building parcel information. Results from three American cities (Houston, New York, San Francisco) indicate that our method identifies neighborhoods consistent with historical approaches. Additionally, we uncover a consistent scale in all three cities at which physical isolation drives neighborhood emergence. However, our methods also reveal differences between these cities: Houston, although more disconnected on larger spatial scales than New York and San Francisco, is less disconnected at smaller scales.

    View details for DOI 10.1371/journal.pone.0245067

    View details for PubMedID 33444347

  • SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods APPLIED ENERGY Roth, J., Martin, A., Miller, C., Jain, R. K. 2020; 280
  • Harnessing smart meter data for a Multitiered Energy Management Performance Indicators (MEMPI) framework: A facility manager informed approach APPLIED ENERGY Roth, J., Brown, H., Jain, R. K. 2020; 276
  • Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications ENERGY AND BUILDINGS Azar, E., O'Brien, W., Carlucci, S., Hong, T., Sonta, A., Kim, J., Andargie, M. S., Abuimara, T., El Asmar, M., Jain, R. K., Ouf, M. M., Tahmasebi, F., Zhou, J. 2020; 224
  • One approach does not fit all (smart) cities: Causal recipes for cities' use of "data and analytics" CITIES Ruhlandt, R., Levitt, R., Jain, R., Hall, D. 2020; 104
  • Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference IEEE EMBEDDED SYSTEMS LETTERS Sonta, A. J., Jain, R. K. 2020; 12 (2): 41–44
  • Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective ENERGY POLICY Roth, J., Lim, B., Jain, R. K., Grueneich, D. 2020; 139
  • Drivers of Data and Analytics Utilization within (Smart) Cities: A Multimethod Approach JOURNAL OF MANAGEMENT IN ENGINEERING Ruhlandt, R., Levitt, R., Jain, R., Hall, D. 2020; 36 (2)
  • Energy-cyber-physical systems APPLIED ENERGY Jin, M., Jain, R., Spanos, C., Jia, Q., Norford, L. K., Kjaergaard, M., Yan, J. 2019; 256
  • Computational Approaches to Enable Smart and Sustainable Urban Systems JOURNAL OF COMPUTING IN CIVIL ENGINEERING Jain, R. K., Abraham, D. 2019; 33 (6)
  • Understanding the adoption and usage of data analytics and simulation among building energy management professionals: A nationwide survey BUILDING AND ENVIRONMENT Srivastava, C., Yang, Z., Jain, R. K. 2019; 157: 139–64
  • Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems JOURNAL OF COMPUTING IN CIVIL ENGINEERING Gupta, K., Yang, Z., Jain, R. K. 2019; 33 (2)
  • DUE-A: Data-driven Urban Energy Analytics for understanding relationships between building energy use and urban systems Yang, Z., Gupta, K., Jain, R. K., Yan, J., Yang, H. X., Li, H., Chen ELSEVIER SCIENCE BV. 2019: 6478–83
  • Optimizing Neighborhood-Scale Walkability Sonta, A. J., Jain, R. K., Cho, Y. K., Leite, F., Behzadan, A., Wang, C. AMER SOC CIVIL ENGINEERS. 2019: 454–61
  • Spatial and Temporal Modeling of Urban Building Energy Consumption Using Machine Learning and Open Data Roth, J., Bailey, A., Choudhary, S., Jain, R. K., Cho, Y. K., Leite, F., Behzadan, A., Wang, C. AMER SOC CIVIL ENGINEERS. 2019: 459–67
  • Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India APPLIED ENERGY Nutkiewicz, A., Jain, R. K., Bardhan, R. 2018; 231: 433–45
  • A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation APPLIED ENERGY Khosrowpour, A., Jain, R. K., Taylor, J. E., Peschiera, G., Chen, J., Gulbinas, R. 2018; 218: 304–16
  • DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis ENERGY AND BUILDINGS Yang, Z., Roth, J., Jain, R. K. 2018; 163: 58–69
  • Data-Driven, Multi-metric, and Time-Varying (DMT) Building Energy Benchmarking Using Smart Meter Data Roth, J., Jain, R. K., Smith, I. F., Domer, B. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 568–93
  • Inferring Occupant Ties Automated Inference of Occupant Network Structure in Commercial Buildings Sonta, A. J., Jain, R. K., Ramachandran, G. S., Batra, N. ASSOC COMPUTING MACHINERY. 2018: 126–29
  • OESPG: Computational Framework for Multidimensional Analysis of Occupant Energy Use Data in Commercial Buildings JOURNAL OF COMPUTING IN CIVIL ENGINEERING Sonta, A. J., Jain, R. K., Gulbinas, R., Moura, J. M., Taylor, J. E. 2017; 31 (4)
  • Data-driven planning of distributed energy resources amidst socio-technical complexities Nature Energy Jain, R. K., Qin, J., Rajagopal, R. 2017

    View details for DOI 10.1038/nenergy.2017.112

  • Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow Nutkiewicz, A., Yang, Z., Jain, R. K., Yan, J., Wu, J., Li, H. ELSEVIER SCIENCE BV. 2017: 2114–19
  • Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity Cell Stem Cell Yan, K., Gevaert, O., Zheng, G., Anchang, B., Probert, C., et al 2017; 21 (1): 78 - 90.e6


    Several cell populations have been reported to possess intestinal stem cell (ISC) activity during homeostasis and injury-induced regeneration. Here, we explored inter-relationships between putative mouse ISC populations by comparative RNA-sequencing (RNA-seq). The transcriptomes of multiple cycling ISC populations closely resembled Lgr5+ISCs, the most well-defined ISC pool, but Bmi1-GFP+cells were distinct and enriched for enteroendocrine (EE) markers, including Prox1. Prox1-GFP+cells exhibited sustained clonogenic growth in vitro, and lineage-tracing of Prox1+cells revealed long-lived clones during homeostasis and after radiation-induced injury in vivo. Single-cell mRNA-seq revealed two subsets of Prox1-GFP+cells, one of which resembled mature EE cells while the other displayed low-level EE gene expression but co-expressed tuft cell markers, Lgr5 and Ascl2, reminiscent of label-retaining secretory progenitors. Our data suggest that the EE lineage, including mature EE cells, comprises a reservoir of homeostatic and injury-inducible ISCs, extending our understanding of cellular plasticity and stemness.

    View details for DOI 10.1016/j.stem.2017.06.014

    View details for PubMedCentralID PMC5642297

  • A Data Integration Framework for Urban Systems Analysis Based on Geo-Relationship Learning Yang, Z., Gupta, K., Gupta, A., Jain, R. K., Lin, K. Y., ElGohary, N., Tang, P. AMER SOC CIVIL ENGINEERS. 2017: 467–74
  • Towards Automated Inference of Occupant Behavioral Dynamics Using Plug-Load Energy Data Sonta, A. J., Simmons, P. E., Jain, R. K., Lin, K. Y., ElGohary, N., Tang, P. AMER SOC CIVIL ENGINEERS. 2017: 290–97
  • Poster Abstract: Towards City-Scale Building Energy Performance Benchmarking Yang, Z., Roth, J., Jain, R. K., ACM ASSOC COMPUTING MACHINERY. 2016: 241–42
  • Data-Driven Benchmarking of Building Energy Performance at the City Scale Yang, Z., Roth, J., Jain, R. K., ACM ASSOC COMPUTING MACHINERY. 2016
  • Poster abstract: A data-driven design framework for urban slum housing - Case of Mumbai Debnath, R., Bardhan, R., Jain, R. K., ACM ASSOC COMPUTING MACHINERY. 2016: 239–40
  • Modeling the determinants of large-scale building water use: Implications for data-driven urban sustainability policy SUSTAINABLE CITIES AND SOCIETY Kontokosta, C. E., Jain, R. K. 2015; 18: 44-55
  • BizWatts: A modular socio-technical energy management system for empowering commercial building occupants to conserve energy APPLIED ENERGY Gulbinas, R., Jain, R. K., Taylor, J. E. 2014; 136: 1076-1084
  • The impact of combined water and energy consumption eco-feedback on conservation ENERGY AND BUILDINGS Jeong, S. H., Gulbinas, R., Jain, R. K., Taylor, J. E. 2014; 80: 114-119
  • Big Data plus Big Cities: Graph Signals of Urban Air Pollution IEEE SIGNAL PROCESSING MAGAZINE Jain, R. K., Moura, J. M., Kontokosta, C. E. 2014; 31 (5): 130-136
  • Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy APPLIED ENERGY Jain, R. K., Smith, K. M., Culligan, P. J., Taylor, J. E. 2014; 123: 168-178
  • Network Ecoinformatics: Development of a Social Ecofeedback System to Drive Energy Efficiency in Residential Buildings JOURNAL OF COMPUTING IN CIVIL ENGINEERING Gulbinas, R., Jain, R. K., Taylor, J. E., Peschiera, G., Golparvar-Fard, M. 2014; 28 (1): 89-98
  • Can social influence drive energy savings? Detecting the impact of social influence on the energy consumption behavior of networked users exposed to normative eco-feedback ENERGY AND BUILDINGS Jain, R. K., Gulbinas, R., Taylor, J. E., Culligan, P. J. 2013; 66: 119-127
  • Investigating the impact eco-feedback information representation has on building occupant energy consumption behavior and savings ENERGY AND BUILDINGS Jain, R. K., Taylor, J. E., Culligan, P. J. 2013; 64: 408-414
  • Block Configuration Modeling: A novel simulation model to emulate building occupant peer networks and their impact on building energy consumption APPLIED ENERGY Chen, J., Jain, R. K., Taylor, J. E. 2013; 105: 358-368
  • Assessing eco-feedback interface usage and design to drive energy efficiency in buildings ENERGY AND BUILDINGS Jain, R. K., Taylor, J. E., Peschiera, G. 2012; 48: 8-17