Jian Zhao
Ph.D Candidate

Department of Computer Sience, University of Toronto
Rm 3302, 10 King's College Rd, Toronto, Ontario, Canada M5S 3G4

  • KronoMiner: Using Multi-Foci Navigation for the Visual Exploration of Time-Series Data

    By Jian Zhao, Fanny Chevalier, and Ravin Balakrishnan

    KronoMiner proposes a generic time-series exploration framework with fluid interactions and basic analytical capabilities, where users can drill down and compare multiple time segments simultaneously organized in a dynamic radial hierarchy.

  • Exploratory Analysis of Time-Series with ChronoLenses

    By Jian Zhao, Fanny Chevalier, Emmanuel Pietriga, and Ravin Balakrishnan

    ChronoLenses support more elaborate time-series analysis tasks by providing on-the-fly transformations of the data in multiple focus regions (shown with a lens metaphor) and progressive construction of advanced analytical pipelines with flexible user operations on creating lens hierarchies.

  • Facilitating Discourse Analysis with Interactive Visualization

    By Jian Zhao, Fanny Chevalier, Christopher Collins, and Ravin Balakrishnan

    This paper presents a multi-focus interactive visualization system, name DAViewer, for computational linguistics researchers to explore, compare, evaluate, and annotate the results of document discourse tree parsers, thus assisting researchers in discourse analysis for hypotheses verification and algorithm development.

  • TimeSlice: Interactive Faceted Browsing of Timeline Data

    By Jian Zhao, Steven Drucker, Danyel Fisher, and Donald Brinkman

    TimeSlice presents a novel interface for visual comparison of multi-faceted events timelines that are generated by users performing dynamic queries over a set of meta-data attributes represented in a tree structure.

  • TrailMap: Facilitating Information Seeking in a Multi-Scale Digital Map via Implicit Bookmarking

    By Jian Zhao, Daniel Wigdor, and Ravin Balakrishnan

    TrailMap is a web-based application to support online map information seeking tasks, such as neighborhood exploration, with automatic bookmark generation, facilitating users with rapid revisits of earlier views during map navigation.

  • Interactive Exploration of Implicit and Explicit Relations in Faceted Datasets

    By Jian Zhao, Christopher Collins, Fanny Chevalier, and Ravin Balakrishnan

    This paper presents, PivotSlice, an interactive visualization for exploring relationships in multi-faceted data, which allows users to visually construct two sets of dynamic queries over the data, forming a multi-focus and multi-scale tabular subdivision of the entire information space with customized semantics.

  • PEARL: An Interactive Visual Analytic Tool for Understanding Personal Emotion Style Derived from Social Media

    By Jian Zhao, Liang Gou, Fei Wang, and Michelle Zhou.

    PEARL is timeline-based visual analytic tool that allows users to interactively discover and examine a person's emotional styles derived from this person's social media text.

  • #FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

    By Jian Zhao, Nan Cao, Zhen Wen, Yale Song, Yu-Ru Lin, and Christopher Collins.

    FluxFlow is an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media, which incorporates advanced machine learning algorithms to detect anomalies, and offers a set of novel visualization designs for presenting the detected threads for deeper analysis.

About Me

I'm a PhD student candidate supervised by Prof. Ravin Balakrishnan in Dynamic Graphic Project (DGP) lab at Department of Computer Science, University of Toronto. My research interest is Information Visualization and Human Computer Interaction. Before coming to UofT, I received my Bachelor's Degree (with honour) in Engineering at College of Computer Science, Zhejiang University in June, 2009 (I was also affiliated with the Chu Kochen Honors College).

Learning painting persistently in my childhood gave me the invaluable magic of discovering and creating beauties of the nature. What is more, four years of undergraduate education in Computer Science not only granted me the solid foundation of knowledge and skills but also made me desire to have the same magic on computers. And I found the perfect blend was in Visualization and HCI.

My research falls in specific topics about designing novel interactive visualization systems supporting multi-focus visual exploration of complicated real-world datasets, developing new methods for information seeking and navigation on web, creating interaction techniques for mobile phones, and quantitatively modelling user performance on touch screens.

Publications

Conference Full Papers and Journal Articles

[J5]

Jian Zhao, R. William Soukoreff, Xiangshi Ren, Ravin Balakrishnan.
A Model of Scrolling on Touch-Sensitive Displays.
IJHCS: International Journal of Human-Computer Studies, 2014 (In Press).
Abstract

Abstract: Scrolling interaction is a common and frequent activity allowing users to browse content that is initially off-screen. With the increasing popularity of touch-sensitive devices, gesture-based scrolling interactions (e.g., finger panning and flicking) have become an important element in our daily interaction vocabulary. However, there are currently no comprehensive user performance models for scrolling tasks on touch displays. This paper presents an empirical study of user performance in scrolling tasks on touch displays. In addition to three geometrical movement parameters --- scrolling distance, display window size, and target width, we also investigate two other factors that could affect the performance, i.e., scrolling modes --- panning and flicking, and feedback techniques --- with and without distance feedback. We derive a quantitative model based on four formal assumptions that abstract the real-world scrolling tasks, which are drawn from the analysis and observations of user scrolling actions. The results of a control experiment reveal that our model generalizes well for direct-touch scrolling tasks, accommodating different movement parameters, scrolling modes and feedback techniques. Also, the supporting blocks of the model, the four basic assumptions and three important mathematical components, are validated by the experimental data. In-depth comparisons with existing models of similar tasks indicate that our model performs the best under different measurement criteria. Our work provides a theoretical foundation for modeling sophisticated scrolling actions, as well as offers insights into designing scrolling techniques for next-generation touch input devices.

[J4,C9]

Jian Zhao, Nan Cao, Zhen Wen, Yale Song, Yu-Ru Lin, Christopher Collins.
#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media.
TVCG: IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2014), Dec 2014 (To Appear).
Abstract

Abstract: We present FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. Everyday, millions of messages are created, commented, and shared by people on social media websites, such as Twitter and Facebook. This provides valuable data for researchers and practitioners in many application domains, such as marketing, to inform decision-making. Distilling valuable social signals from the huge crowd's messages, however, is challenging, due to the heterogeneous and dynamic crowd behaviors. The challenge is rooted in data analysts' capability of discerning the anomalous information behaviors, such as the spreading of rumors or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. FluxFlow incorporates advanced machine learning algorithms to detect anomalies, and offers a set of novel visualization designs for presenting the detected threads for deeper analysis. We evaluated FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy. Through quantitative measurements of the algorithmic performance and qualitative interviews with domain experts, the results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model.

[C8]

Jian Zhao, Liang Gou, Fei Wang, Michelle Zhou.
PEARL: An Interactive Visual Analytic Tool for Understanding Personal Emotion Style Derived from Social Media.
VAST'14: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, Nov 2014 (To Appear).
Abstract

Abstract: Hundreds of millions of people leave digital footprints on social media (e.g., Twitter and Facebook). Such data not only disclose a person's demographics and opinions, but also reveal one's emotional style. Emotional style captures a person's patterns of emotions over time, including his overall emotional volatility and resilience. Understanding one's emotional style can provide great benefits for both individuals and businesses alike, including the support of self-reflection and delivery of individualized customer care. We present PEARL a timeline-based visual analytic tool that allows users to interactively discover and examine a person's emotional style derived from this person's social media text. Compared to other visual text analytic systems, our work offers three unique contributions. First, it supports multi-dimensional emotion analysis from social media text to automatically detect a person's expressed emotions at different time points and summarize those emotions to reveal the person's emotional style. Second, it effectively visualizes complex, multi-dimensional emotion analysis results to create a visual emotional profile of an individual, which helps users browse and interpret one's emotional style. Third, it supports rich visual interactions that allow users to interactively explore and validate emotion analysis results. We have evaluated our work extensively through a series of studies. The results demonstrate the effectiveness of our tool both in emotion analysis from social media and in support of interactive visualization of the emotion analysis results.

[C7]

Ji Wang, Jian Zhao, Sheng Guo, Chris North, Naren Ramakrishnan.
ReCloud: Semantics-based Word Cloud Visualization of User Reviews.
GI'14: Proceedings of the Graphics Interface Conference, pp. 151-158, May 2014.
Abstract Paper Bibtex

Abstract: User reviews, like those found on Yelp and Amazon, have become an important reference for decision making in daily life, for example, in dining, shopping and entertainment. However, large amounts of available reviews make the reading process tedious. Existing word cloud visualizations attempt to provide an overview. However their randomized layouts do not reveal content relationships to users. In this paper, we present ReCloud, a word cloud visualization of user reviews that arranges semantically related words as spatially proximal. We use a natural language processing technique called grammatical dependency parsing to create a semantic graph of review contents. Then, we apply a force-directed layout to the semantic graph, which generates a clustered layout of words by minimizing an energy model. Thus, ReCloud can provide users with more insight about the semantics and context of the review content. We also conducted an experiment to compare the efficiency of our method with two alternative review reading techniques: random layout word cloud and normal text-based reviews. The results showed that the proposed technique improves user performance and experience of understanding a large number of reviews.

[J3,C6]

Jian Zhao, Christopher Collins, Fanny Chevalier, Ravin Balakrishnan.
Interactive Exploration of Implicit and Explicit Relations in Faceted Datasets.
TVCG: IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2013), pp. 2080-2089, Dec 2013.
Abstract Paper Bibtex Video YouTube Slides Prototype

Abstract: Many datasets, such as scientific literature collections, contain multiple heterogeneous facets which derive implicit relations, as well as explicit relational references between data items. The exploration of this data is challenging not only because of large data scales but also the complexity of resource structures and semantics. In this paper, we present PivotSlice, an interactive visualization technique which provides efficient faceted browsing as well as flexible capabilities to discover data relationships. With the metaphor of direct manipulation, PivotSlice allows the user to visually and logically construct a series of dynamic queries over the data, based on a multi-focus and multi-scale tabular view that subdivides the entire dataset into several meaningful parts with customized semantics. PivotSlice further facilitates the visual exploration and sensemaking process through features including live search and integration of online data, graphical interaction histories and smoothly animated visual state transitions. We evaluated PivotSlice through a qualitative lab study with university researchers and report the findings from our observations and interviews. We also demonstrate the effectiveness of PivotSlice using a scenario of exploring a repository of information visualization literature.

[C5]

Jian Zhao, Daniel Wigdor, Ravin Balakrishnan.
TrailMap: Facilitating Information Seeking in a Multi-Scale Digital Map via Implicit Bookmarking.
CHI'13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3009-3018, May 2013.
Abstract Paper Bibtex Video YouTube Slides

Abstract: Web applications designed for map exploration in local neighborhoods have become increasingly popular and important in everyday life. During the information-seeking process, users often revisit previously viewed locations, repeat earlier searches, or need to memorize or manually mark areas of interest. To facilitate rapid returns to earlier views during map exploration, we propose a novel algorithm to automatically generate map bookmarks based on a user's interaction. TrailMap, a web application based on this algorithm, is developed, providing a fluid and effective neighborhood exploration experience. A one-week study is conducted to evaluate TrailMap in users' everyday web browsing activities. Results showed that TrailMap's implicit bookmarking mechanism is efficient for map exploration and the interactive and visual nature of the tool is intuitive to users.

[J2,C4]

Jian Zhao, Fanny Chevalier, Christopher Collins, Ravin Balakrishnan.
Facilitating Discourse Analysis with Interactive Visualization.
TVCG: IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2012), 18(12), pp. 2639-2648, Dec 2012.
Abstract Paper Bibtex Video YouTube Slides Prototype

Abstract: A discourse parser is a natural language processing system which can represent the organization of a document based on a rhetorical structure tree---one of the key data structures enabling applications such as text summarization, question answering and dialogue generation. Computational linguistics researchers currently rely on manually exploring and comparing the discourse structures to get intuitions for improving parsing algorithms. In this paper, we present DAViewer, an interactive visualization system for assisting computational linguistics researchers to explore, compare, evaluate and annotate the results of discourse parsers. An iterative user-centered design process with domain experts was conducted in the development of DAViewer. We report the results of an informal formative study of the system to better understand how the proposed visualization and interaction techniques are used in the real research environment.

[J1,C3]

Jian Zhao, Fanny Chevalier, Emmanuel Pietriga, Ravin Balakrishnan.
Exploratory Analysis of Time-Series with ChronoLenses.
TVCG: IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2011), 17(12), pp. 2422-2431, Dec 2011.
Abstract Paper Bibtex Video YouTube Slides

Abstract: Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of timeseries, e.g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines.

Chronolenses has been referenced as one of the most interesting papers at InfoVis 2011.

[C2]

William Soukoreff, Jian Zhao, Xiangshi Ren.
The Entropy of a Rapid Aimed Movement: Fitts' Index of Difficulty versus Shannon's Entropy.
INTERACT'11: Proceedings of 13th IFIP TC 13 International Conference on Human Computer Interaction, Vol Part 4, pp. 222-239, Sep 2011.
Abstract Paper Bibtex

Abstract: A thought experiment is proposed that reveals a difference between Fitts' index of difficulty and Shannon's entropy, in the quantification of the information content of a series of rapid aimed movements. This implies that the contemporary Shannon formulation of the index of difficulty is similar to, but not identical to, entropy. Preliminary work is reported toward developing a model that resolves the problem. Starting from first principles (information theory), a formulation for the entropy of a Fitts' law style rapid aimed movement is derived, that is similar in form to the traditional formulation. Empirical data from Fitts' 1954 paper are analysed, demonstrating that the new model fits empirical data as well as the current standard approach. The novel formulation is promising because it accurately describes human movement data, while also being derived from first principles (using information theory), thus providing insight into the underlying cause of Fitts' law.

[C1]

Jian Zhao, Fanny Chevalier, Ravin Balakrishnan.
KronoMiner: Using Multi-Foci Navigation for the Visual Exploration of Time-Series Data.
CHI'11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1737-1746, May 2011.
Abstract Paper Bibtex Video YouTube Slides

Abstract: The need for pattern discovery in long time-series data led researchers to develop interactive visualization tools and analytical algorithms for gaining insight into the data. Most of the literature on time-series data visualization either focus on a small number of tasks or a specific domain. We propose KronoMiner, a tool that embeds new interaction and visualization techniques as well as analytical capabilities for the visual exploration of time-series data. The interface design has been iteratively refined based on feedback from expert users. Qualitative evaluation with an expert user not involved in the design process indicates that our prototype is promising for further research.

Conference Short Papers

[S2]

Jian Zhao, Steven Drucker, Danyel Fisher, Donald Brinkman.
TimeSlice: Interactive Faceted Browsing of Timeline Data.
AVI'12: Proceedings of the International Working Conference on Advanced Visual Interfaces, pp. 433-436, May 2012.
Abstract Paper Bibtex Video Slides

Abstract: Temporal events with multiple sets of metadata attributes, i.e., facets, are ubiquitous across different domains. The capabilities of efficiently viewing and comparing events data from various perspectives are critical for revealing relationships, making hypotheses, and discovering patterns. In this paper, we present TimeSlice, an interactive faceted visualization of temporal events, which allows users to easily compare and explore timelines with different attributes on a set of facets. By directly manipulating the filtering tree, a dynamic visual representation of queries and filters in the facet space, users can simultaneously browse the focused timelines and their contexts at different levels of detail, which supports efficient navigation of multi-dimensional events data. Also presented is an initial evaluation of TimeSlice with two datasets - famous deceased people and US daily flight delays.

[S1]

Jian Zhao, R. William Soukoreff, Ravin Balakrishnan.
A Model of Multi-touch Manipulation.
GRAND'11: Proceedings of the 2nd annual Grand Conference, May 2011.
AbstractPaper

Abstract: As touch-sensitive devices become increasingly popular, fundamentally understanding the human performances of multi-touch gestures is critical. However, there is currently no mathematical model for interpreting such gestures. In this paper, a novel model of multi-touch interaction is derived by combining the Mahalanobis distance metric and Fitts' law. The model describes the time required to complete an object manipulation task that includes translocation, rotation, and scaling. Empirical data is reported that validates the new model (R2>0.9). Linear relationship between the difficulty and time elapsed is revealed indicating that the model can provide guidelines for interface designers for empirically comparing gestures and devices.

Posters, Work-in-Progress, and others

[O2]

Ji Wang, Jian Zhao, Sheng Guo, Chris North.
Clustered Layout Word Cloud for User Generated Review.
Yelp Dataset Challenge (Grand Prize Winner), Oct 2013.

[O1]

Jian Zhao.
A Particle Filter Based Approach of Visualizing Time-varying Volume.
LDAV'12: IEEE Symposium on Large-Scale Data Analysis and Visualization, Oct 2012.
AbstractPaper

Abstract: Extracting and presenting essential information of time-varying volumetric data is critical in many fields of sciences. This paper introduces a novel approach of identifying important aspects of the dataset under the particle filter framework in computer vision. With the view of time-varying volumes as dynamic voxels moving along time, an algorithm for computing the 3D voxel transition curves is derived. Based on the curves which characterize the local data temporal behavior, this paper also introduces several post-processing techniques to visualize important features such as curve clusters by k-means and curve variations computed from curve gradients.

See my other interesting unpublished projects!

Employment

Research Intern, Adobe Research, San Francisco, Jun. 2014 - Sep. 2014
Research Intern, IBM Almaden Research Center, San Jose, Jun. 2013 - Sep. 2013
Research Intern, Microsoft Research, Redmond, Jun. 2011 - Sep. 2011

Major Awards

Wolfond Fellowship, University of Toronto, $10,000, 2012
Wolfond Scholarship, University of Toronto, $5,000, 2010, 2011
University of Toronto Art & Science Fellowship, ~$30,000/year, 2009-2014
Chinese National Scholarship, ¥8,000/year, 2006, 2008
1st Class Zhejiang University Academic Scholarship, ¥5,000/year, 2006-2008

Patents

[P2]

Liang Gou, Fei Wang, Jian Zhao, Michelle Zhou.
Personal Emotion State Monitoring from Social Media. Filed in 2014.

[P1]

Jian Zhao, Steven Drucker, Danyel Fisher, Donald Brinkman.
Relational Rendering of Multi-Faceted Data. US 20130194294 A1, Aug. 2013.

Teaching Assistantships

CSC108 Introduction to Computer Programming   (Fall 2009, Spring 2010, Fall 2011)
CSC148 Introduction to Computer Science   (Fall 2010)
CSC318 Design of Interactive Computational Media   (Spring 2011, Spring 2012, Fall 2013)
CSC309 Programming on the Web   (Fall 2012)
CSC428/2514 Human-Computer Interaction   (Spring 2014)

Talks

Visual Data Exploration: A Multi-Focus Approach UOIT, Oshawa, ON, 2013.12
Visual Analytics of Online Social Media IBM Research Almaden, San Jose, CA, 2013.9
TrailMap: Facilitating Information Seeking in a Multi-Scale Digital Map via Implicit Bookmarking ACM CHI, Paris, France, 2013.5
TrailMap: Facilitating Information Seeking in a Multi-Scale Digital Map via Implicit Bookmarking ToRCHI seminar, Toronto, ON, 2013.4
Facilitating Discourse Analysis with Interactive Visualization IEEE VisWeek, Seattle, WA, 2012.10
Exploratory Analysis of Time-series with ChronoLenses IEEE VisWeek, Providence, RI, 2011.10
TimeSlice: Interactive Faceted Browsing of Timeline Data Microsoft Research, Redmond, WA, 2011.8
KronoMiner: Using Multi-Foci Navigation for the Visual Exploration of Time-Series Data ACM CHI, Vancouver, BC, 2011.5
A Model of Multi-touch Manipulation GRAND, Vancouver, BC, 2011.5
Modeling Scrolling Interactions on Touch Screens Jilin University, China, 2010.12
KronoMiner: Using Multi-Foci Navigation for the Visual Exploration of Time-Series Data KMDI seminar, University of Toronto, 2010.9

Services

Student Volunteers
IEEE VisWeek2010, IEEE VisWeek2011, CHI2014.

Reviewers
CHI 2011-2014, APCHI 2012, InfoVis/VAST 2012-2014, UIST 2013-2014, PacificVis 2014, MobileHCI 2014, TVCG.

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