![Rishee Jain](https://profiles.stanford.edu/proxy/api/cap/profiles/62078/resources/profilephoto/350x350.1557265362715.jpg)
Rishee Jain
Associate Professor of Civil and Environmental Engineering
Bio
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
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Associate Professor, Civil and Environmental Engineering
Honors & Awards
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Eugene L. Grant Teaching Award, Stanford University (2023)
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CAREER Award, National Science Foundation (2019)
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Science, Engineering and Education for Sustainability (SEES) Fellow, National Science Foundation (2014)
Professional Education
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PhD, Columbia University, Civil Engineering
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MS, Columbia University, Civil Engineering
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BS, University of Texas at Austin, Civil, Environmental & Architectural Engineering
Projects
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Data-driven Sustainable Upgradation of Dharavi Informal Settlement (Mumbai, India), Stanford University
Location
Mumbai, India
Collaborators
- Ronita Bardhan, Assistant Professor, Indian Institute of Technology - Bombay
2023-24 Courses
- Building Systems Design & Analysis
CEE 156, CEE 256 (Win) - Racial Equity in Energy
CEE 130R, CEE 330 (Aut) -
Independent Studies (10)
- Advanced Engineering Problems
CEE 399 (Aut, Win, Spr, Sum) - Directed Reading in Environment and Resources
ENVRES 398 (Aut, Win, Spr, Sum) - Directed Reading or Special Studies in Civil Engineering
CEE 198 (Win, Spr, Sum) - Directed Research in Environment and Resources
ENVRES 399 (Aut, Win, Spr, Sum) - Independent Project in Civil and Environmental Engineering
CEE 199L (Aut, Win, Spr, Sum) - Independent Project in Civil and Environmental Engineering
CEE 299L (Aut, Win, Spr, Sum) - Independent Study in Civil Engineering for CEE-MS Students
CEE 299 (Aut, Win, Spr, Sum) - Report on Civil Engineering Training
CEE 398 (Aut, Win, Spr, Sum) - Undergraduate Honors Thesis
CEE 199H (Aut, Win, Spr, Sum) - Undergraduate Research in Civil and Environmental Engineering
CEE 199 (Aut, Win, Spr, Sum)
- Advanced Engineering Problems
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Prior Year Courses
2022-23 Courses
- Building Systems Design & Analysis
CEE 156, CEE 256 (Win) - Intro to Urban Sys Engrg
CEE 243 (Spr) - Racial Equity in Energy
AFRICAAM 131, CEE 130R, CEE 330 (Spr)
2021-22 Courses
- Building Systems Design & Analysis
CEE 156, CEE 256 (Win) - Intro to Urban Sys Engrg
CEE 243 (Aut) - Racial Equity in Energy
CEE 130R, CEE 330 (Aut)
2020-21 Courses
- Building Systems Design & Analysis
CEE 156, CEE 256 (Spr) - Racial Equity in Energy
CEE 130R, CEE 330 (Aut)
- Building Systems Design & Analysis
Stanford Advisees
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Poojan Patel -
Doctoral Dissertation Reader (AC)
Tess Hegarty, Junwen Zheng -
Doctoral Dissertation Advisor (AC)
Eleanor Ho, Dinesh Moorjani, Kopal Nihar, Juliet Nwagwu Ume-Ezeoke -
Master's Program Advisor
Yasemin Agi, Daniel Krashin, Adeline Leung, Spencer Lytle, Isandro Malik, Jayce Martinez, Juliet Nwagwu Ume-Ezeoke, Michaella Park, Kayla Ryan, Vaishnavi Thumuganti -
Doctoral Dissertation Co-Advisor (AC)
Ranjitha Shivaram -
Doctoral (Program)
Devan Addison-Turner, Ryan Auker, Lauren Excell, Eleanor Ho, Yun-Dam Ko, Kristi Maisha, Dinesh Moorjani, Juliet Nwagwu Ume-Ezeoke, Dipashreya Sur
All Publications
- Utilizing wearable technology to characterize and facilitate occupant collaborations in flexible workspaces 2024
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Examining the impact of energy efficiency retrofits and vegetation on energy performance of institutional buildings: An equity-driven analysis
APPLIED ENERGY
2024; 357
View details for DOI 10.1016/j.apenergy.2023.121722
View details for Web of Science ID 001147553100001
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Evaluating building decarbonization potential in U.S. cities under emissions based building performance standards and load flexibility requirements
JOURNAL OF BUILDING ENGINEERING
2023; 76
View details for DOI 10.1016/j.jobe.2023.107375
View details for Web of Science ID 001058931900001
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Quantifying the pedestrian access potential of suburban street network retrofits
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
2023
View details for DOI 10.1177/23998083231190974
View details for Web of Science ID 001037149700001
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Tropical climates and the interplay between IEQ and energy consumption in buildings: A review
BUILDING AND ENVIRONMENT
2023; 242
View details for DOI 10.1016/j.buildenv.2023.110551
View details for Web of Science ID 001031559800001
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Natural ventilation versus air pollution: assessing the impact of outdoor pollution on natural ventilation potential in informal settlements in India
ENVIRONMENTAL RESEARCH: INFRASTRUCTURE AND SUSTAINABILITY
2023; 3 (2)
View details for DOI 10.1088/2634-4505/acc88f
View details for Web of Science ID 001059253500001
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TOM.D: Taking advantage of microclimate data for urban building energy modeling
ADVANCES IN APPLIED ENERGY
2023; 10
View details for DOI 10.1016/j.adapen.2023.100138
View details for Web of Science ID 001030813800001
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The impact of urban form on daily mobility demand and energy use: Evidence from the United States
APPLIED ENERGY
2023; 339
View details for DOI 10.1016/j.apenergy.2023.120883
View details for Web of Science ID 000966600000001
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A barrier too far: Understanding the role of intersection crossing distance on bicycle rider behavior in Chicago
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
2023
View details for DOI 10.1177/23998083221147922
View details for Web of Science ID 000909520900001
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Invisible walls: Exploration of microclimate effects on building energy consumption in New York City
SUSTAINABLE CITIES AND SOCIETY
2023; 90
View details for DOI 10.1016/j.scs.2022.104364
View details for Web of Science ID 000916218600001
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Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking
APPLIED ENERGY
2022; 327
View details for DOI 10.1016/j.apenergy.2022.119989
View details for Web of Science ID 000886052300007
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Optimizing pipe network design and central plant positioning of district heating and cooling System: A Graph-Based Multi-Objective genetic algorithm approach
APPLIED ENERGY
2022; 325
View details for DOI 10.1016/j.apenergy.2022.119844
View details for Web of Science ID 000860341400003
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A Global Building Occupant Behavior Database.
Scientific data
2022; 9 (1): 369
Abstract
This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants' schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.
View details for DOI 10.1038/s41597-022-01475-3
View details for PubMedID 35764639
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Cool roofs can mitigate cooling energy demand for informal settlement dwellers
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
2022; 159
View details for DOI 10.1016/j.rser.2022.112183
View details for Web of Science ID 000786655300003
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Exploring Use Cases for an Hourly Building Energy Benchmarking Platform
ASSOC COMPUTING MACHINERY. 2022: 303-304
View details for DOI 10.1145/3563357.3567756
View details for Web of Science ID 001066191100048
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Context-aware Urban Energy Analytics (CUE-A): A framework to model relationships between building energy use and spatial proximity of urban systems
SUSTAINABLE CITIES AND SOCIETY
2021; 72
View details for DOI 10.1016/j.scs.2021.102978
View details for Web of Science ID 000672607600001
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Exploring the influence of urban context on building energy retrofit performance: A hybrid simulation and data-driven approach
ADVANCES IN APPLIED ENERGY
2021; 3
View details for DOI 10.1016/j.adapen.2021.100038
View details for Web of Science ID 001022693500003
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Data-driven optimization of building layouts for energy efficiency
ENERGY AND BUILDINGS
2021; 238
View details for DOI 10.1016/j.enbuild.2021.110815
View details for Web of Science ID 000636221500001
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Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification
ENERGIES
2021; 14 (5)
View details for DOI 10.3390/en14051481
View details for Web of Science ID 000628134300001
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Automated identification of urban substructure for comparative analysis.
PloS one
2021; 16 (1): e0245067
Abstract
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
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SCHMEAR: Scalable Construction of Holistic Models for Energy Analysis from Rooftops
ASSOC COMPUTING MACHINERY. 2021: 111-120
View details for DOI 10.1145/3486611.3486666
View details for Web of Science ID 000945948100012
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SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods
APPLIED ENERGY
2020; 280
View details for DOI 10.1016/j.apenergy.2020.115981
View details for Web of Science ID 000594128100004
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Harnessing smart meter data for a Multitiered Energy Management Performance Indicators (MEMPI) framework: A facility manager informed approach
APPLIED ENERGY
2020; 276
View details for DOI 10.1016/j.apenergy.2020.115435
View details for Web of Science ID 000571441800005
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Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications
ENERGY AND BUILDINGS
2020; 224
View details for DOI 10.1016/j.enbuild.2020.110292
View details for Web of Science ID 000571218200030
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One approach does not fit all (smart) cities: Causal recipes for cities' use of "data and analytics"
CITIES
2020; 104
View details for DOI 10.1016/j.cities.2020.102800
View details for Web of Science ID 000541151900012
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Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference
IEEE EMBEDDED SYSTEMS LETTERS
2020; 12 (2): 41–44
View details for DOI 10.1109/LES.2019.2937316
View details for Web of Science ID 000541147900003
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Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective
ENERGY POLICY
2020; 139
View details for DOI 10.1016/j.enpol.2020.111327
View details for Web of Science ID 000528255000035
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Drivers of Data and Analytics Utilization within (Smart) Cities: A Multimethod Approach
JOURNAL OF MANAGEMENT IN ENGINEERING
2020; 36 (2)
View details for DOI 10.1061/(ASCE)ME.1943-5479.0000762
View details for Web of Science ID 000508191300010
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Exploring the integration of simulation and deep learning models for urban building energy modelling and retrofit analysis
INT BUILDING PERFORMANCE SIMULATION ASSOC-IBPSA. 2020: 3209-3216
View details for DOI 10.26868/25222708.2019.210264
View details for Web of Science ID 000709431303035
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Learning socio-organizational network structure in buildings with ambient sensing data
DATA-CENTRIC ENGINEERING
2020; 1
View details for DOI 10.1017/dce.2020.9
View details for Web of Science ID 000851324300009
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Energy-cyber-physical systems
APPLIED ENERGY
2019; 256
View details for DOI 10.1016/j.apenergy.2019.113939
View details for Web of Science ID 000497981300051
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Computational Approaches to Enable Smart and Sustainable Urban Systems
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
2019; 33 (6)
View details for DOI 10.1061/(ASCE)CP.1943-5487.0000850
View details for Web of Science ID 000486180000002
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Understanding the adoption and usage of data analytics and simulation among building energy management professionals: A nationwide survey
BUILDING AND ENVIRONMENT
2019; 157: 139–64
View details for DOI 10.1016/j.buildenv.2019.04.016
View details for Web of Science ID 000471114800014
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Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
2019; 33 (2)
View details for DOI 10.1061/(ASCE)CP.1943-5487.0000806
View details for Web of Science ID 000457272200001
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DUE-A: Data-driven Urban Energy Analytics for understanding relationships between building energy use and urban systems
ELSEVIER SCIENCE BV. 2019: 6478–83
View details for DOI 10.1016/j.egypro.2019.01.114
View details for Web of Science ID 000471031706128
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Optimizing Neighborhood-Scale Walkability
AMER SOC CIVIL ENGINEERS. 2019: 454–61
View details for Web of Science ID 000485219700058
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Spatial and Temporal Modeling of Urban Building Energy Consumption Using Machine Learning and Open Data
AMER SOC CIVIL ENGINEERS. 2019: 459–67
View details for Web of Science ID 000485354700059
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Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India
APPLIED ENERGY
2018; 231: 433–45
View details for DOI 10.1016/j.apenergy.2018.09.002
View details for Web of Science ID 000452345400033
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Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow
ELSEVIER SCI LTD. 2018: 1176–89
View details for DOI 10.1016/j.apenergy.2018.05.023
View details for Web of Science ID 000438181000090
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Understanding building occupant activities at scale: An integrated knowledge-based and data-driven approach
ELSEVIER SCI LTD. 2018: 1–13
View details for DOI 10.1016/j.aei.2018.04.009
View details for Web of Science ID 000438320300001
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A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation
APPLIED ENERGY
2018; 218: 304–16
View details for DOI 10.1016/j.apenergy.2018.02.148
View details for Web of Science ID 000430994500026
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DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis
ENERGY AND BUILDINGS
2018; 163: 58–69
View details for DOI 10.1016/j.enbuild.2017.12.040
View details for Web of Science ID 000428485100006
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Data-Driven, Multi-metric, and Time-Varying (DMT) Building Energy Benchmarking Using Smart Meter Data
SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 568–93
View details for DOI 10.1007/978-3-319-91635-4_30
View details for Web of Science ID 000482715500030
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Inferring Occupant Ties Automated Inference of Occupant Network Structure in Commercial Buildings
ASSOC COMPUTING MACHINERY. 2018: 126–29
View details for DOI 10.1145/3276774.3276779
View details for Web of Science ID 000458157100016
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OESPG: Computational Framework for Multidimensional Analysis of Occupant Energy Use Data in Commercial Buildings
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
2017; 31 (4)
View details for DOI 10.1061/(ASCE)CP.1943-5487.0000663
View details for Web of Science ID 000399894800017
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Data-driven planning of distributed energy resources amidst socio-technical complexities
Nature Energy
2017
View details for DOI 10.1038/nenergy.2017.112
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Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow
ELSEVIER SCIENCE BV. 2017: 2114–19
View details for DOI 10.1016/j.egypro.2017.12.614
View details for Web of Science ID 000452901602041
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Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity
Cell Stem Cell
2017; 21 (1): 78 - 90.e6
Abstract
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
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A Data Integration Framework for Urban Systems Analysis Based on Geo-Relationship Learning
AMER SOC CIVIL ENGINEERS. 2017: 467–74
View details for Web of Science ID 000425807900056
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Towards Automated Inference of Occupant Behavioral Dynamics Using Plug-Load Energy Data
AMER SOC CIVIL ENGINEERS. 2017: 290–97
View details for Web of Science ID 000425807900035
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Poster Abstract: Towards City-Scale Building Energy Performance Benchmarking
ASSOC COMPUTING MACHINERY. 2016: 241–42
View details for DOI 10.1145/2993422.2996408
View details for Web of Science ID 000433381300040
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Data-Driven Benchmarking of Building Energy Performance at the City Scale
ASSOC COMPUTING MACHINERY. 2016
View details for DOI 10.1145/3007540.3007541
View details for Web of Science ID 000391514000001
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Poster abstract: A data-driven design framework for urban slum housing - Case of Mumbai
ASSOC COMPUTING MACHINERY. 2016: 239–40
View details for DOI 10.1145/2993422.2996406
View details for Web of Science ID 000433381300039
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Modeling the determinants of large-scale building water use: Implications for data-driven urban sustainability policy
SUSTAINABLE CITIES AND SOCIETY
2015; 18: 44-55
View details for DOI 10.1016/j.scs.2015.05.007
View details for Web of Science ID 000367397500005
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BizWatts: A modular socio-technical energy management system for empowering commercial building occupants to conserve energy
APPLIED ENERGY
2014; 136: 1076-1084
View details for DOI 10.1016/j.apenergy.2014.07.034
View details for Web of Science ID 000345725800104
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The impact of combined water and energy consumption eco-feedback on conservation
ENERGY AND BUILDINGS
2014; 80: 114-119
View details for DOI 10.1016/j.enbuild.2014.05.013
View details for Web of Science ID 000343949400011
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Big Data plus Big Cities: Graph Signals of Urban Air Pollution
IEEE SIGNAL PROCESSING MAGAZINE
2014; 31 (5): 130-136
View details for DOI 10.1109/MSP.2014.2330357
View details for Web of Science ID 000346043600014
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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
2014; 123: 168-178
View details for DOI 10.1016/j.apenergy.2014.02.057
View details for Web of Science ID 000336017400017
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Network Ecoinformatics: Development of a Social Ecofeedback System to Drive Energy Efficiency in Residential Buildings
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
2014; 28 (1): 89-98
View details for DOI 10.1061/(ASCE)CP.1943-5487.0000319
View details for Web of Science ID 000333446100009
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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
2013; 66: 119-127
View details for DOI 10.1016/j.enbuild.2013.06.029
View details for Web of Science ID 000327904200013
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Investigating the impact eco-feedback information representation has on building occupant energy consumption behavior and savings
ENERGY AND BUILDINGS
2013; 64: 408-414
View details for DOI 10.1016/j.enbuild.2013.05.011
View details for Web of Science ID 000323629100043
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Block Configuration Modeling: A novel simulation model to emulate building occupant peer networks and their impact on building energy consumption
APPLIED ENERGY
2013; 105: 358-368
View details for DOI 10.1016/j.apenergy.2012.12.036
View details for Web of Science ID 000316831800036
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Assessing eco-feedback interface usage and design to drive energy efficiency in buildings
ENERGY AND BUILDINGS
2012; 48: 8-17
View details for DOI 10.1016/j.enbuild.2011.12.033
View details for Web of Science ID 000302669300002