I am a postdoc fellow in Department of Computer Science in CICS
at UMass Amherst starting from 2021 Fall. I am affiliated
with HCI-VIS Lab and Center for Data Science.
I was awarded with
Postdoctoral Fellowship in Data Science at UMass Amherst.
Previously, I was selected as a
Presidential Fellow at
School of Data Science, and a
Praxis Fellow of the
Digital Humanities at University of Virginia.
I also served as a graduate GIS technician at UVA's
Scholars' Lab Spatial Tech group.
I received my Ph.D. in
Constructed Environment from
University of Virginia, Masters from
University at Buffalo, and B.Eng. from
Sichuan University.
My research is highly interdisciplinary and focuses on empowering AI and data-informed decision-making for social good.
My research interests include a variety of topics about conversational AI, data science, and smart cities,
including human-computer interaction, responsible conversational AI for social good, computational social science,
data-smart transportation, human mobility information retrieval with text mining, machine learning in travel behavior analysis.
GPA: 3.95/4
University of Virginia
GPA: 3.89/4
University at Buffalo, The State University of New York
GPA: 3.82/4
Sichuan University
deeplearning.ai
University of California San Diego
Stanford University
University of Illinois at Urbana-Champaign
New York University
Stanford University
University at Buffalo, The State University of New York
Best Paper Award
University of Virginia
2020 Spring PLAN 2111 GIS for Planners
# of enrollment: 20
Undergraduate students from multiple departments (urban planning, architecture, engineering, political science, economics, sociology, etc.) across UVA
This course provides an introduction to geographic information systems (GIS) concepts and software.
In this course, I introduced the concepts of GIS as well as practical trainings on ESRI's ArcGIS suite and Open-source mapping, both in-person and online teaching.
My students successfully completed the course and obtained general familiarity with the major functionality of GIS and spatial data visualization.
University of Virginia
2018 Spring PLAN 6040 Quantitative Methods for Planners
Abstract: Increased travel as a result of urbanization and population growth has led to the need for safer, more efficient transportation in US cities. We examines whether the public believes driverless transportation systems could meet this demand by analyzing public social media data from Twitter. Using mined Twitter data (2012 - present), we conduct a two-pronged approach to understand public perceptions of driverless technology. We used a Natural Language Processing approach using topic modeling to infer latent topics of interest related to driverless technology, and developed a sentiment analysis model to uncover the public dominant emotions towards each area of interest. Through topic modeling, we uncovered a set of 5 latent themes consisting of Safety Perceptions, Technology Development, Industrial / System Integration, Milestone and Vision, and Ethics and Policy as well as their classified opinions of positive and negative. The findings indicate that safety, technological progress, and industrial and urban integration are of major concern that largely affect the public’s acceptance of driverless technology. Driverless vehicle developers can leverage these results to influence what functionalities they should improve upon, and how they can shape their marketing campaigns to cater to customers’ needs and expectations. More importantly, the findings will help public transport operators and city planners as they attempt to integrate autonomous vehicles into the urban transportation system.
Computer Language: R, Python, Octave, CSS, HTML
Data Analysis: ArcGIS, ArcPy, SPSS Statistics, SQL, Microsoft Access
Data Visualization: Tableau, Mapbox, Photoshop, AutoCAD
Email: zhiqiujiang@gmail.com
Website: https://zhiqiujiang.com