Michelle X. Zhou


Michelle is a co-founder and CEO of Juji, Inc. Prior to starting Juji, Michelle was a senior manager of the User Systems and Experience Research (USER) group, part of IBM Research, Almaden and then part of the newly formed IBM Watson Group. She also worked at IBM T. J. Watson Research Center and was on an international assignment at IBM Research, China. Michelle received a Ph.D. in Computer Science from Columbia University. Her expertise is in the interdisciplinary areas of intelligent user interaction, smart visual analytics (2D/3D), and people-centric information systems. Her work has been incorporated into or created over a dozen IBM products and solutions. She has also published over 80 peer-reviewed, refereed scientific articles, and filed over 35 patents in above areas.

Michelle is an ACM Distinguished Scientist and is active in several research communities, including intelligent user interfaces (IUI), information visualization and visual analytics, and multimedia (MM), where she has co-organized/co-chaired conferences and workshops, and often serves on the technical program committees for key conferences in these areas. She was the general conference co-chair for ACM Recommender Systems 2014 and ACM IUI 2007, and is the technical program co-chair for ACM MM 2009 and IUI 2010. Currently she chairs the IUI steering committee, and has been on the editorial board of three ACM journals: ACM Transactions on Intelligent Systems and Technology (TIST), ACM Transactions on Interactive Intelligent Systems (TiiS), and ACM Transactions on Multimedia Computing, Communications, and Applications ( TOMCCAP).

Previous Work

Although Michelle's research interests have evolved over the years, she has always been interested in the interdisciplinary area of intelligent user interaction (IUI) and its applications to real-world problems. In particular, her works has fallen into several areas.

Smart Visualization

One picture is worth a thousand words. For thousands of years, people have been using information graphics—visual representation of data—to comprehend and analyze information. However, creating high-quality visualization is a daunting task especially for people who are neither graphic artists nor computer scientists. To democratize the use of visualization, Michelle has been investigating how to automate the design and generation of visualization since her Ph.D. thesis.

  • As part of her thesis work, Michelle developed a system called IMPROVISE, which used an AI planning-based approach to automatically design and create a visual discourse, a connected series of animated visual illustrations for explaining complex information to users (e.g., patient briefings for health caregivers or network traffic analysis to network administrators).
  • After joining IBM T. J. Watson Research Center, Michelle and her colleagues co-developed IMPROVISE+, which used a case-based learning engine to automatically generate interactive visual responses by examples and tailor the responses to highly dynamic user interaction situations and unanticipated information. IMPROVISE+ has been used in IBM's engagement with a U.S. government agency and in other IBM products/solutions.
  • With the advances in text mining and analytics, more recently, Michelle has initiated and led the research in the area of advanced visual text analytics, which combined state-of-the-art text analytics with novel interactive visualization to empower average business users to analyze massive amounts of textual data. With her colleagues at IBM Research China, they developed an interactive visual text summarization system, called TIARA, which combined unsupervised topic modeling (e.g., LDA) and novel visual metaphors to help users examine what is inside a text collection (e.g., email, news, and emergency room patient records) and discover topic patterns and trends in such a collection. The core technology of TIARA went into three IBM analytics products released in 2010: IBM eDiscovery V2.2, IBM Content Analytics V2.2, and Cognos Consumer Insights.
  • In addition to helping users detect patterns and make discoveries from massive amounts of text, Michelle and her colleagues at Almaden also built OpinionBlocks, investigating how to use interactive visual text analytics to facilitate user decision making (e.g., making a purchase decision based on the visual text analysis of extensive consumer reviews or voting on a proposition based on others' opinions).

Mixed-initiative Human-Computer Interaction (HCI)

Licklider in his Man-Computer Symbiosis said: "Man-computer symbiosis is an expected development in cooperative interaction between men and electronic computers. In the anticipated symbiotic partnership, men will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations. Computing machines will do the routinizable work that must be done to prepare the way for insights and decisions in technical and scientific thinking.”

Michelle believes that the future of HCI is to facilitate the development of such a man-computer symbiosis where both humans and machines can leverage their strengths and avoid their weaknesses. One of such developments is the support of mixed-initiative interaction where both users and systems can take initiatives during a complex interaction process.

  • Part of her earlier work at IBM T. J. Watson was on developing a mixed-initiative intelligent information system (RealHunter), where the humans and computers work together collaboratively in an information seeking process. In particular, users take initiatives to freely express (almost!) their information requests in context using multimodal input (e.g., natural language expressions and visual query). To respond to a user's request, the system automatically generates a multimedia response tailored to the context (e.g., query context and retrieval results). To maximize the efficiency of human-computer interaction (e.g., minimizing the number of steps taken to find the desired information) in such a process, the system takes initiatives whenever needed (e.g., filling in the blanks when a user's request is vague or suggesting alternative information if user-requested data is not found).
  • With rapidly increasing people online activities, Michelle also became interested in the development of social recommender systems, another class of mixed-initiative interaction systems. Working with my colleagues at IBM Research China, we developed a system called Pharos, which automatically summarizes user online social behavior over time and presents users with a social map of the site (i.e Marauder's Map of online social sites). Using the derived social map, the users can learn about the site dynamics easily and also take initiatives to navigate the site and engage in social activities.

To enable computers to take sensible initiatives and push this class of systems to main stream applications, Michelle was particularly interested in developing novel and practical computational approaches to the problem. So far she has investigated the use of optimization-based approaches and developed a suite of algorithms to address a range of fundamental challenges in the space (e.g., an algorithm for dynamically determining data content in response to a user data query in context and a graph-matching algorithm for media allocation). Going forward, Michelle is interested in exploring new interaction paradigms where users can interact with complex system responses (e.g., system-derived text summarization results) and the use of interactive machine learning in support of an adaptive, mixed-initiative human-computer interaction, where both humans and computers can learn from each other.

Opportunistic Social Computing

The use of social software (e.g., social networking, micro-blogging, and online forums) has penetrated the masses. Michelle was inspired by the phenomena and interested in finding out how it will change people's daily lives as well as its long-term impact on the world. In particular, Michelle was interested in how social computing will bring people with opportunistic information and collaboration partners whenever they need them but without subjecting themselves to "constant availability and instant intimacy" as they do today. For example, when a person has a question regarding car repair, through social media she should be able to locate a person/group who has perhaps just had his/her car repaired and is able to provide her with the most accurate information to her inquiry; if someone wants to find a person to share a rental car at a tourist destination, again through social media they should be able to find such a partner whom they do not know before but they now share the same interest/need at that moment. To support these scenarios, Michelle believes there are fundamental research issues to be addressed. They include but not limited to:

  • Understanding, modeling, and automatically deriving social profile of a person, a community, or an organization based on their digital footprints (i.e., online behavior);
  • Monitoring social channels (e.g., facebook and twitter) and detecting which social channels would be the most valuable source(s) for extracting social intelligence (e.g., knowledge about car repair or the consumer complains/needs);
  • Analysis and mining of social messages to distill useful insight (information or people) for opportunistic information sharing (e.g., sharing the extracted consumer complains), knowledge acquisition (e.g., asking target audience to voice their problems and suggest their solutions), and crowd-sourced problem solving (e.g., soliciting and analyzing information submitted by a crowd who is at or near the scene of an accident for crime investigation).

Personality Analytics

At IBM Research, Almaden, Michelle initiated and led her team in developing System U, a personality analytics system that can automatically derive an individuals' personality traits from the individual's linguistic footprints (e.g., tweets, blogs, and reviews). Such derived traits can then be used to help an individual to better understand him/herself as well as others to obtain or deliver hyper-personalized experience (e.g., self discovery/assessment, social engagements, product or career recommendations). Not only such traits can be used to improve human-computer-human interaction, but they can also be used to facilitate human-computer interaction, as one's psychological traits, may aid one's information tasks, e.g., information navigation and visual perception/interaction of information. System U now is known as Personality Insights offered by the IBM Watson Group.

Current Activities

Since System U has showed great promises in deep people understanding, Michelle believes that the biggest value of such technology lies in its applications, especially those that can benefit hundreds of millions of individuals directly. Imagine a world where everyone knows who they really are, easily find opportunities that value their individuality, and discover experiences that delight them. To pursue her passion about applying deep people analytics to helping individuals, she decided to co-found Juji last fall. If you are interested in hearing more about her ideas on democratizing people analytics for the masses, join her at her panel at SXSW 2015: "Are you in a social media experiment?"