Jinda Han
CS PhD student @ UIUC

About Me

I am a Ph.D. student in the Department of Computer Science at the University of Illinois at Urbana-Champaign. I work with Professor Ranjitha Kumar in her Data-Driven Design Group @ Human-Computer Interaction (HCI) Lab.

Prior to my Ph.D. journey, I received my B.S. and M.S. degrees from the (same) Department of Computer Science at the University of Illinois at Urbana-Champaign, where I worked with Professor Chengxiang Zhai in his Text Information Management and Analysis Group (TIMan) @ Data and Information Systems (DAIS) Lab.

Besides, I have worked at some companies such as Apple (2018, 2019), Synopsys (2016), Accenture (2014), and SEDAC (2017). In addition, I was the Chair of ACM SIGGRAPH at the University of Illinois chapter from May 2015 to May 2016. What's more, before I came to US, I was an opera singer (see "Hai gia vinta la causa" in 2008).


My research interest lies in interdisciplinary areas, which leveraging techniques such as large scale Text Data Mining, Applied Machine Learning, and NLP to solve the Fashion problems on Social Media Networks in the direction of Human-Computer Interaction.

  • Fashion Trends Prediction, Analysis, and Visualization
  • Social Networks Based Fashion Recommendation Systems
  • Intelligent Data-Driven/Information Systems



  • Text Data Mining, Applied Machine Learning, Social Network Quantitative and Qualitative Analysis, NLP, HCI Experimental Methods, Data Visualization, etc.
  • Full-Stack Web Development: PHP (LAMP), React/Angular+Node.js, Flask/Django, MongoDB, MySQL, etc.
  • Mobile Development: Java (Android), Swift (iOS), React Native (Android/iOS), etc.



Current Projects

The Study of Fashion Influencers on Social Media

The fashion industry is characterized by the ebb and flow of trends. With the rise of social media, fashion blogs, and fast-fashion movement, bottom-up fashion trends are emerging at an ever-increasing rate. Identifying new influencers and trends as they happen is challenging for retailers. As a first step, this paper presents a classifier for identifying fashion-related accounts on social media. To develop this classifier, we collected a dataset of 10k Twitter accounts using a content-based snowball sampling approach, and crowdsourced ground-truth labels for these accounts. We train a classifier that identifies whether a Twitter account is fashion-related and evaluate the efficacy of our method. We hope to leverage this classifier to identify key fashion influencers and conduct large-scale monitoring of fashion trends.

Previous Projects

Predicting Fashion Choices Using Unimportant Domains: Personality vs Fashion Questionnaire Systems

Recently, in light of the current emphasis on security and privacy, people have become more aware of the data that companies gather from them. However, companies need this data to create recommendations that will be useful to their customers. Our idea is to develop knowledge of a person’s preferences using data that does not gather sensitive information such as viewing or buying history. Many people already take fun quizzes on Facebook and other web platforms to learn more about themselves, so why not take the answers collected from these quizzes instead? In this project, we aim to connect answers from fun questions such as “what would you rather do on a saturday morning?” and “which instrument would you like to learn?” to choice of fashion styles.

Large-scale Study on Heterogeneous (Social) Information Networks

SetSearch+: Web System 1, Web System 2

Landuse Evolution and Impact Assessment Model

"Better tools are needed to manage regional dynamics, not just as economic systems or static inventories of resources, but as complex systems that are part of regional and global networks. However, effective management requires both that we understand the systems to be administered and that we understand the implications of our strategies. We have attempted here to outline an approach for understanding the dynamics of urban systems and the potential implications of urban policy and investment management decisions. We described one modeling approach — LEAM — that utilizes cellular automata and other technological advances in spatial simulation modeling to help improve a community’s ability to make ecologically and economically sound decisions. LEAM was intended to enable users to capture stochastic influences and view the reported probable consequences of intended events in a scenario-based format that is comprehensible by local experts, decision-makers, and stakeholders.
The LEAM Model, its development, and its application to several regions within the continental United States is conducted and managed by a team of faculty, staff, and students at the University of Illinois at Urbana-Champaign. This team brings together expertise in substantive issues, modeling, high-performance computing, and visualization coming from the departments of Landscape Architecture, Urban Planning, Geography, Economics, Natural Resources and Environmental Sciences, the National Center for Supercomputing Applications (NCSA), the United States Army Corp of Engineers, and private industry. The mission of the LEAM group is to help others understand the relationships between human economic/cultural activities and biophysical cycles from a changing land use perspective. All of us must realize that these interacting systems behave in very complex and dynamic ways. Understanding the extent of how one system affects another will allow us to make better land use management decisions in the future."

Mobile Element Search Engine

[Search Engine Demo]
Identify the elements and icons on Android UI, enable the basic search function to categories these elements.

Virtual Reality For Personal Interior Design

[floor plan mining demo]
Mining the floor plans on the web, and the goal was to generate the floorplan layout design into a 3D model automatically.

Enterprise Information Update and Alert System

Many companies today need a system to gather useful online information about products or customers, and then, such companies will improve their products or services consequent to this information and improve the functionality of the company. Thus, receiving a large amount and accurate information becomes the first important step in this process, which is followed by storing and retrieving such amounts of data. The next step in the process involves comparing these with the existing system database to update out-of-date data. Finally, future predictions and automatic information updates on available alert will be the last important step. Currently, there is a little research work on how to build such a system. In this thesis, I propose an Update and Alert System (EIUAS) for companies to maintain their products database. This system contains many parts, including, among others, a web crawler, data extractor, data verifier, data integration, dynamic database design, data retrieval engine, updatable checker, predict module, and updatable alert. The system would enable a company to receive all of the useful information about products or customers from public space. To evaluate the usefulness of this system as a baseline, we perform a calculation on the updatable percentage. Additionally, qualitative assessment by a domain expert also confirmed the system as very useful and with updatable information.

  • EIUAS: An Enterprise Information Update and Alert System. Jinda Han. MS Thesis. 2016.

  • Intelligent Medical Diagnosis System


  • XSearch: An Intelligent Medical Diagnosis System. Jinda Han. BS Thesis. 2014.