Yuan Lai a Lecturer in Urban Science and Planning at MIT Department of Urban Studies and Planning. His expertise lies at the intersection of urban information, applied data science, and urban systems. Yuan is interested in the future connection between computer science and urban planning to address socio-technical complexities of cities. His work has been featured at the United Nations Global Pulse, Bloomberg Technology, Data for Good Exchange, NYC Media Lab, American Planning Association, American Society of Civil Engineers, and Urban Design Forum.
Prior to coming to MIT, Yuan was a research affiliate at NYU Marron Institute of Urban Management and NYU Center for Urban Science and Progress (CUSP). His work involves applied analytics and machine learning using large volume and variety of data related to urban environment, population health, social media, sensing network, and economic transactions. Yuan also practiced in architecture and urban design at Safdie Architects, where he worked on large scale mixed-use development projects worldwide. He holds a Ph.D. in Civil Engineering with a concentration in urban systems and informatics from NYU, a M.S. in Applied Urban Science and Informatics from NYU CUSP, as well as a Master of Urban Planning and a Bachelor of Landscape Architecture.
MIT Faculty Page LinkedIn Google Scholar
RESEARCH PROJECTS
ADAPTIVE CITIES
The thematic structure and spatial-temporal patterns of building renovation.
POLLEN SCAPE
The impact of urban street tree species on air quality and respiratory illness.
QUANTIFYING PLACES
Analyzing the drivers of pedestrian activity in dense urban environment.
CITY DATA NETWORK
Network analysis on open data and city agency operation.
OPERATION ANALYTICS
Informatics for business improvement district operation with Grand Central Partnership.
NEIGHBORHOOD PROFILE
Quantifying neighborhood growth and data-driven decision support for capital planning.
PREDICTING DENGUE FEVER RISK
Machine learning for spatial epidemiology in Singapore.
FUTURE EMR SYSTEMS
Disrupting Electronic Health Records Systems.
PHYSICAL COMPUTING
Combine Physical Material with Data Computing and Human Movement.
COURSES / WORKSHOPS
NEWS
11/10/2020: NEW ARTICLE WITH MICROSOFT RESEARCH
09/20/2020: NEW ARTICLE ON COVID-19 RESEARCH
08/04/2020: NEW BOOK CHAPTER AVAILABLE!
07/01/2020: WHAT IS THE COVID-19 DATA TSUNAMI TELLING POLICYMAKERS?
05/11/2020: JOIN US IN MIT COVID-19 DATATHON!
04/16/2020: OPEN DATA SCIENCE CONFERENCE
03/25/2020: IEEE PERVASIVE COMPUTING CONFERENCE
01/30/2020: MIT URBAN SCIENCE IAP WORKSHOP
11/15/2019: OUR BOOK HAS A CHINESE VERSION!
11/08/2019: APPAM CONFERENCE
10/20/2019: EXPLORING URBAN SCIENCE
10/18/2019: NOMA CONFERENCE
11/16/2018: APA ANNUAL CONFERENCE
10/19/2018: MIT+TMU HACKATHON, TAIPEI
10/15/2018: METROLAB NETWORK ANNUAL SUMMIT
04/18/2018: URBAN DESIGN FORUM, NYC
10/31/2017: DATA SCIENCE TO SOLVE REAL-WORLD PROBLEMS
09/16/2017: BLOOMBERG D4GX IMMERSION PROGRAM
09/16/2017: STREET TREE DATA FOR URBAN HEALTH
08/01/2017: BLOOMBERG AR PROTOTYPING FELLOWSHIP
05/02/2017: FOREFRONT FELLOW: URBAN DESIGN FORUM
03/04/2016: DEVELOP TECH IDEAS INTO REALITY
WE WON HACKNYU 2016!
PUBLICATIONS
Peer-reviewed Journal Articles
Lai, Yuan, Marie-Laure Charpignon, Leo A. Celi. 2020. "Unsupervised learning for county-level typological classification for COVID-19 research" Intelligence-Based Medicine. vol. 1-2.Eva M. Luo, Sarah Newman, Maelys Amat, Marie-Laure Charpignon, Erin R Duralde, Shrey Jain, Aaron R Kaufman, Igor Korolev, Yuan Lai, Barbara D Lam, Megan Lipcsey, Alfonso Martinez, Oren J Mechanic, Jack Mlabasati, Liam G McCoy, Freddy T Nguyen, Matthew Samuel, Eric Yang, Leo A. Celi. 2020. "MIT COVID-19 Datathon: data without boundaries" BMJ Innovations.0:1–4
Lai, Yuan, Wesley Yeung, and Leo A. Celi. 2020. "Urban intelligence for pandemic response: Viewpoint" JMIR Public Health & Surveillance.
Lai, Yuan and Constantine E. Kontokosta. 2019. "Topic modeling to discover the thematic structure and spatial-temporal patterns of building renovation and adaptive reuse in cities." Computers, Environment, and Urban Systems vol.78.
Lai, Yuan and Constantine E. Kontokosta. 2019. "The impact of urban street tree species on air quality and respiratory illness: A spatial analysis of large-scale, high-resolution urban data," Health and Place, Vol. 56, 2019.
Lai, Yuan and Constantine E. Kontokosta. 2018. "Quantifying place: Analyzing the drivers of pedestrian activity in dense urban environment," Landscape and Urban Planning, Vol. 180.
Celi, Leo A., Jeggery D. Marshall, Yuan Lai and David J. Stone. 2015. "Disrupting electronic health records systems: The next generation," Journal of Medical Informatics 3(4):e34.
Yin, Li, Samina Raja, Xiao Li, Yuan Lai, Leonard Epstein, and James Roemmich. 2013. "Neighborhood for playing: Using GPS, GIS, and accelerometry to delineate areas within which youth are physically active," Urban Studies 2013; 1(18).
Peer-reviewed Conference Proceedings
Lai, Yuan. 2020. "Hyper-local urban contextual awareness through open data integration." 18th IEEE International Conference on Pervasive Computing and Communications, Austin, TX.Khmaissia, Fadoua, Pegah Sagheb Haghighi, Aarthe Jayaprakash, Zhenwei Wu, Sokratis Papadopoulos, Yuan Lai, and Freddy T Nguyen. 2020. "An unsupervised machine learning approach to assess the ZIP code level impact of COVID-19 in NYC," Proceedings of the 2020 International Conference on Machine Learning.
Kontokosta, Constantine E., Yuan Lai, Bartosz Bonczak, Sokratis Papadopoulos, Boyeong Hong, Awais Malik, and Nicholas Johnson. 2018. "A dynamic spatial-temporal model of urban carbon emissions for data-driven climate action by cities," Proceedings of the Bloomberg Data for Good Exchange, New York, NY.
Lai, Yuan and Constantine E. Kontokosta. 2017. "Analyzing the drivers of pedestrian activity at high spatial resolution," Proceedings of the 2017 Conference ASCE International Conference on Sustainable Infrastructure, New York, NY.
Lai, Yuan and Constantine E. Kontokosta. 2017. "Measuring the impact of urban street trees on air quality and respiratory illness: A data-driven approach to environmental justice," Proceedings of the Bloomberg Data for Good Exchange 2017, New York, NY.
Peer-reviewed Book Chapters
Lai, Yuan and David J. Stone. 2020. "Integrated Data Intelligence for Urban Health," Book Chapter in Data Science and Global Health. Harvard-MIT Health Sciences and Technology. Springer.Lai, Yuan and Constantine E. Kontokosta. 2019. "Urban Data Mining: Sources, Types, and Limits," Book Chapter in Urban Intelligence: How Data and Information Can Shape Urban Planning, Design, and City Operations. London: Routledge. (In contract)
Lai, Yuan, Edward Moseley, Francisco Salgueiro, and David J. Stone. 2016. "Integrating Non-clinical Data with EHRs" in Secondary Analysis of Electronic Health Records, MIT Critical Data Group, ed. Springer International Publishing AG.
Stone, David J., Justin Rousseau, and Yuan Lai. 2016. "Pulling It All Together: Envisioning a Data-Driven, Ideal Care System" in Secondary Analysis of Electronic Health Records, MIT Critical Data Group, ed. Springer International Publishing AG.
News Articles, Technical Report, and Working Paper
Yuan Lai 2020. "How to see the inflection point? Application of data science in public health emergencies." Financial Times China.Kontokosta, Constantine E., Yuan Lai, Sokratis Papadopoulos, Jacob Sagi, Franz Fuerst, and Gary Pivo. 2019. "Estimating Office and Multifamily Building Energy Retrofit Hurdle Rates and Risk Arbitrage in Energy Efficiency Investments." Working Paper for Real EstateResearch Institute & Lawrence Berkeley National Laboratory Research Grant.
Kontokosta, Constantine E., Yuan Lai, Bartosz Bonczak, Sokratis Papadopoulos, Boyeong Hong, Awais Malik, and Nicholas Johnson. 2017. "Urban Physiology: A Dynamic Spatial-Temporal Model of Urban Carbon Emissions to Drive Climate Action by Cities." Technical report for the United Nations Data for Climate Action Challenge.
Yuan Lai, Sreoshy Banerjea, Alison Von Glinow.2017. "Arrival House: How can we redesignand rethink housing to better integrate the arrival of immigrants to their new city?" Tech-nical report for Urban Design Forum Design for Arrival Program.
NYC Department of City Planning Capital Planning Division and NYU Center for Urban Science and Progree, 2016. "Neighborhood Profiles: Planning and Visualizing for Strategic Growth." Technical report for Applied Urban Science and Informatics master capstone project.
Invited Talks, Conference Presentations, and Media Coverage
Panel speaker, "Integrating Urban Open Data for Public Good", Open Data Science Conference (ODSC) East, Boston, MA. Apr 2020.Panel paper presenter, "Using Big Data and Social Media to Understand Neighborhood Conditions", Association for Public Policy Analysis and Management (APPAM) Annual Fall Research Conference. Denver, CO. Nov 8, 2019.
Invited roundtable discussion with American Express, 13th Annual Machine Learning Symposium, The New York Academy of Sciences. New York. Mar 1, 2019.
Guest Lecture, “Urban Informatics and Big Data for Quality-of-Life.” for a graduate course HST.936: Leveraging Data Science in Global Health. Harvard-MIT Health Sciences and Technology (HST). Feb 8, 2019.
“Arrival House: an Integrated Co-Living Model for New Arrivals to NYC”, American Planning Association New York Metro Annual Conference, New York City, November 2018.
“Big Data for Local Climate Change”, MetroLab Network Summit, Newark, October 2018.
“Applied Analytics in Cities”, invited lecture, Taipei Medical University, College of Management Graduate Institute of Data Science, Taipei, October 2018.
“Design for Arrival: A co-live housing scenario”, Urban Design Forum, New York, March 2018.
U.S. Foreign Policy Colloquium 2017: National Committee on United States–China Relations
“Informatics for business improvement district operation: Grand Central Partnership”, conference panel, Bloomberg Data for Good Exchange Immersion Day, Bloomberg L.P., October 2017.
“Data for Good: Bloomberg supports data scientists’ work with nonprofits and municipalities to solve real-world problems” by NYC Media Lab on October 2017.
“Measuring the impact of urban street trees on air quality and respiratory illness”, conference presentation, Bloomberg Data for Good Exchange, October 2017.
“Data interface with AR in future work environment”, Tech at Bloomberg, August 2017.
“Bloomberg AR Fellows Prototype Possible Future for Augmented Reality in the Enterprise”, Tech at Bloomberg, August 2017.
“Analyzing the drivers of pedestrian activity at high spatial resolution”, International Conference on Sustainable Infrastructure, American Society Of Civil Engineers, 2017.
“Urban informatics and interpretable data”, invited lecture, Graduate School of Architecture, Planning and Preservation, Columbia University, February 2017
“Students Develop Tech Ideas into Reality at HackNYU 2016”, featured in NYU, March 2016
“Urban Design with Big Data”, invited presentation, MIT Senseable City Lab, July 2014.
来源博士任职于美国麻省理工学院建筑与城市规划学院(MIT DUSP)，负责教学城市科学与应用数据科学的相关课程。他的研究关注于城市信息学，数据科学，和城市系统工程学的交叉领域，旨在结合计算机科学与规划设计研究以解决城市管理中的复杂问题。目前，他于麻省理工负责“城市科学与计算机科学”专业的课程设计与教育规划工作，同时还是工程学院“新工程教育转型”计划中数字城市方向的负责人之一。他的研究成果曾发表于多个国际研究期刊，会议演讲，以及媒体报道，其中包括联合国 (UN Global Pulse)，彭博科技 (Bloomberg Technology)，纽约多媒体实验室 (NYC Media Lab)，美国规划师协会 (APA)，美国土木工程师协会 (ASCE)，公共政策分析与管理协会 (APPAM)，以及城市设计论坛 (Urban Design Forum)。
在麻省理工之前，来源曾在纽约大学城市管理研究所(NYU Marron Institute of Urban Management) 和 城市科学与发展中心 (NYU Center for Urban Science and Progress)担任研究工作。他的研究涉及城市数据的跨学科整合与创新应用分析，包括利用数据科学和机器学习来分析城市环境，人口健康，社交媒体舆情，传感网络监测，以及基于城市经济活动大数据的政策分析。在城市开发与设计方面，他曾在波士顿萨夫迪建筑设计事务所 (Safdie Architects) 从事建筑和城市设计工作，参与了全球范围内多个大型城市综合体的开发项目，其中包括新加坡金沙湾酒店，中国国家美术馆(竞标)，和重庆朝天门来福士广场。来源于纽约大学城市科学与发展中心获得工程学博士(城市系统和信息学方向)，以及应用城市科学与信息学理学硕士。他还获得城市规划学硕士学位 (纽约州立大学)与风景园林学士学位 (北京林业大学)。他的跨学科研究证明，城市作为一个复杂的“物理-社会-生态-技术”系统，其问题需要一套结合区域规划、城市设计、计算机科学、与系统工程学的综合方法。