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| Tianyu Su |
Data Science | Location Tech Product | User Experience
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| Geospatial Product + Data Science + User Research |

Place AI

Place AI; 

Harvard Innovation Lab;

AMS Institute


Place AI is an urban AI startup I co-founded with a team of designers, engineers, and data scientists. In this venture, I serve as the Research and Product Lead. Using large-scale social media data and AI, Place AI measures various park-related activities and offers a unique dashboard that presents various behavioral metrics to assist in smart city management. Place AI represents the productization of my doctoral research conducted at Harvard University.

# User research, data science, machine learning, programming (R, Python), rapid prototyping, product design, product management, etc.


User Research Findings, Product Roadmap and Prioritization, Product Prototypes, (Nerdy) ML Models and Algorithms, and Media Coverage.


Reports on User Experience and Satisfaction, Presentations to Leadership, Publications (on Survey Analysis; on User Segmentation), User Segments, and Product Strategies.

| Quantitative User Research + Data Science |

Tailor to Fit

MIT Office of Sustainability

MIT Urban Mobility Lab

2019 - 2020

The 'Tailor to Fit' project focused on providing incentives to target users for Mobility-as-a-Service (MaaS) products. I led the quantitative research and applied a variety of research methods, including large-scale surveys, statistical modeling, and user segmentation, to assess the potential impact and guide product and organizational decision-making.


The project has had a lasting impact, resulting in the redesign of MIT's MaaS products, the introduction of new incentives, and the promotion of more sustainable mobility choices among MIT employees.

# Quantitative user research, survey analysis, statistical modeling (e.g., linear and logistic regressions), log data analysis, user segmentation, urban analytics, data visualization, etc. 


| Data Science + Productization |


City Science Group, 

MIT Media Lab


LivingLine Shanghai is a novel version of the MIT CityScope AI platform for urban management. I led the Wi-Fi data science team and performed a series of behavioral analyses to understand user behavior in cities. Additionally, I was part of the productization team, building the tangible augmented-reality (AR) platform used to support decision-making in a dynamic, iterative, evidence-based process.

# Data science, machine learning, decision trees, augmented reality, 3D design and modeling


Data Analysis and Visualization, Product Prototypes, Publications, Presentations, and Spin-Off.


User Research Findings, Products, Written and Visual Reports, and Media Coverage.

| User Research + Product Design & Management |


Place AI;

Sasaki Foundation

2019 - 2020

Rentify Chinatown, funded by the Sasaki Foundation and the Barr Foundation, is an initiative leveraging the power of digital tools and spatial data analytics to help the Chinese and Chinese American neighborhoods in American cities document place identity, visualize housing challenges and opportunities, and incubate a sense of local belonging. I was the team lead and oversaw all research, data, and product design efforts.

# In-depth user research (1:1 interviews, focus groups, & testing), product design, product management, stakeholder management.

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| Data Science |

Rhythm of Streets

MIT Media Lab


How can we measure the 'rhythm' of streets? In this project, I offer a novel classification for urban streets using large-scale (1M+) high-res temporal behavioral data. Using mobility data, we measure the rhythm of the streets, that is, the temporal patterns of activity in street segments of urban areas.

# Data science, machine learning, log data analysis, urban analytics, spatial-temporal analysis, clustering analysis.


Publications, Conference and Company Presentations (e.g., AMS Institute, IBM Research), Project Website.

| Data Science |

MIT Parking Prediction

MIT Department of Urban Studies & Planning


In this project, we focus on predicting the number of daily MIT gated parking coming from each census tract in Parking Year 2017-2018 (Sep. 16, 2016 - Sep. 15, 2018), as well as the overall number of daily parking of all parking permit holders. We trained a dual-stage

attention-based recurrent neural network ( DA-RNN) model with the campus parking history and exogenous variables such as weather, holidays, and transit health, and use the model to predict the daily number of parking.

# Machine learning, deep learning, urban analytics, mapping


| Data Science + Design|


MIT Department of Urban Studies & Planning


Immigrants play an increasingly pivotal role in the U.S. economy. While there is contentious political debate regarding if immigrants are taking away jobs from the locals, there is little discussion about how immigrants could contribute to job generation and economic growth.


In this study, we explored the immigrants' entrepreneurship activities in the last decades by looking at their geographical and industrial distribution. And we argue that a startup visa could create a pathway to draw foreign-born founders to the US and improve its competitiveness when other countries are launching more start-ups.

# Data analysis, data visualization, web development, interactive design

| Product Design & Management |


Arts @ MIT


Eco-live attempts to address the issue of unequal distribution of education resources in different school districts and communities, improve learning experience, and create a sharing education platform that promotes cross-district education equity, through a combination of available augmented reality technology, simulation tools, and open source online learning materials.

# Augmented reality (AR), 3D design and modeling, product management, product design, entrepreneurship

First Place

MIT Connect Arts, Community, and Computing Challenge

Honorable Mention

Design Intelligence Award | Digital Interaction

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| Design + Data Science |

as a FIELD

New Balance Innovation Studio


Shoe as a Field project experimented with a holistic data-driven approach to redesign the midsole. Outsole and insole pressure data were applied to shape the midsole units' form and distributions.

# Data analysis and visualization, 3D design and modeling, computational design

Tianyu Su
Harvard Doctorate with expertise in data science, user experience, and productization of emerging technologies (e.g., Urban AI, LLMs, & AR/VR)
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Doctorate (Quantitative Social Science) | Harvard University
Master in City Planning (Urban Informatics) | MIT

Master of Architecture, Bachelor of Architecture | Tsinghua University
Senior Researcher Intern, IBM
Quantitative Researcher | Harvard University

Data Science

- Inferential Statistics (e.g.,
t-tests, ANOVA, Regressions)
- Descriptive Statistics
- Computational Statistics
- Multivariate Data Visualization
- Log Data Analysis
- Segmentation
- Text Analytics
- Machine Learning

Programming & Tools

- Programming: R, SQL, Python
- Data Science Tools: Git, Power BI, Google Forms, Excel
- Others: Miro, Figma, Kanban

Research Methods

- Survey Design and Analysis
- Interviews
- Focus Groups
- Concept Testing
- Field Studies
- Contextual Inquiry
- Measurement
- Literature Review


- Product Management
- Team & Project Management
- Presentation & Communication
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Email: | Tel: +1 857-799-0705
48 Quincy St, Cambridge, MA 02138 
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