Michelle Zhou Email and Phone Number
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I am passionate about enabling the best human-AI teaming and ultimately creating human-machine symbiosis, where everyone will have their own Artificial Intelligence (AI) agent or companion, who can truly understand each of us as a unique individual and help us in our personal and professional life. Toward this goal, I love to work with individuals and teams who share the same passion to develop and democratize human-centered AI technologies so *anyone* can create and manage empathetic and responsible AI beings to help themselves help others. While the recent advances in generative AI like ChatGPT have demonstrated amazing NLU and NLG capabilities, my focus is on augmenting such capabilities to power AI agents with advanced human soft skills, such as interactional intelligence (the abilities to handle nuanced personal interactions), personal intelligence (the abilities to read people including their personality), and a sense of purpose (e.g., aiding difficult decision making) to automate high-touch services and deliver deeply personalized, interactive experiences. Moreover, I am interested in developing AI agent tools that combine generative AI with task-driven and knowledge-rich AI agent framework to democratize and accelerate the development and customization of practical and interactive AI agents. If you are interested in collaborating with us on "using AI to generate AI", feel free to reach out! Speaker bio for conference events/media:Dr. Michelle Zhou is a Co-founder and CEO of Juji, Inc., a California-based company that powers Cognitive Artificial Intelligence (AI) Assistants in the form of chatbots. She is an expert in the field of Human-Centered AI, an interdisciplinary area that intersects AI and Human-Computer Interaction (HCI). Zhou has authored more than 100 scientific publications and 45 patent applications on subjects including conversational AI, personality analytics, and interactive visual analytics of big data. Earlier in her career, she spent 15 years at IBM Research and the IBM Watson Group, where she managed the research and development of Human-Centered AI technologies and solutions, including IBM Watson Personality Insights. Zhou serves as Editor-in-Chief of ACM Transactions on Interactive Intelligent Systems (TiiS) and an Associate Editor of ACM Transactions on Intelligent Systems and Technology (TIST), and was formerly the Steering Committee Chair for the ACM International Conference Series on Intelligent User Interfaces. She received a Ph.D. in Computer Science from Columbia University and is an ACM Distinguished Member.
Accenture
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Managing Director, Center For Advanced AiAccentureSan Jose, Ca, Us
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Co-Founder & CeoJuji, Inc. Jan 2018 - PresentSan Jose, California, UsLead the development + commercialization of Juji products and solutions, the world's only maker of cognitive AI beings--AI agents with advanced human soft skills (e.g., reading between the lines) and with a sense of purpose (e.g., eliminating concerns and doubts). Given a task or workflow, Juji combines generative AI (i.e., ChatGPT++) and computational psychology to enable any organizations to rapidly generate and operate their custom AI beings, currently in the form of chatbots, to deliver millions versions of interactive experiences to different users and aid their decision making. These AI beings have been used and automate high-touch services empathetically and responsiblyin domains like Education, Human Capital Management, and Healthcare. -
Editor-In-Chief Of Acm Transactions On Interactive Intelligent Systems (Tiis)Acm, Association For Computing Machinery Feb 2016 - Dec 2022New York, Ny, UsACM TiiS is the premier journal publishing state-of-the-art research at the intersection of Artificial Intelligence and Human-Computer Interaction (https://tiis.acm.org/) -
Co-Founder & CeoJuj.Io 2015 - Sep 2018Investigated factors impacting Human-Machine trust and how to enable trustworthy (empathetic + responsible) machines and their applications.
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Head Of DepartmentIbm Watson Group / Ibm Research, Almaden Jan 2010 - Aug 2014Armonk, New York, Ny, UsIndividualized Experiences at ScaleInitiated and led a multi-year research effort, coded System U, which focuses on researching and developing methodology (empirical and computational), algorithms, and systems to (1) analyze the cognitive, psychological, and social characteristics of individuals and groups based on massive people-generated data (e.g., social media), and to (2) recommend the next-best-actions based on the derived traits to optimize human-computer and human-computer-human interaction (e.g., marketers and consumers).Opportunistic Crowd SourcingWorking on addressing the fundamental research issues to support opportunistic crowdsourcing---finding the right people at the right time for accomplishing a particular task. The work includes: (1) Understanding, modeling, and automatically deriving profile of a person, a community, or an organization based on their digital footprints (i.e., social media and web interaction behavior) to gauge individuals' and community's "fitness" for a task; (2) Use of the derived profiles to establish opportunistic engagements or collaborations among individuals and organizations; and (3) Monitoring social channels (e.g., enterprise social platforms and twitter) and detecting which social channels would be the most valuable source(s) for identifying and engaging crowd.Mixed-Initiative Visual Analytics of Big DataResearching and developing methodology, algorithms, and interactive visual analytic systems that can leverage both the intelligence of users and machines to aid users in analyzing massive data. On the one hand, such systems can automatically guide users to perform their data analytic tasks by recommending suitable visualization and discovery paths in context. On the other hand, users can interactively disseminate, verify, and improve analytic results, which in turn helps the system to learn from users' behavior and improve its quality over time. -
Senior Research ManagerIbm Watson Group / Ibm Research, Almaden Jun 2008 - Dec 2009Armonk, New York, Ny, UsAnalytics-Driven Social Computing Initiated and led a research effort on developing people and content analytics and intelligent user interaction techniques to facilitate social computing in an enterprise setting.(1) Community analytics. Modeling and analyzing key characteristics of online communities, including summarization of activity patterns, discovery of latent communities, and analysis of cultural influences. Facilitating people recommendation, community management and development, serendipitous community discovery, and community recommendation especially from a “cold start” state. (2) Collaboration task analytics. Modeling and analyzing users’ collaboration tasks, in par-ticular, collaborative document creation and editing. Developed intelligent user in-teraction mechanisms (e.g., automatic email and wiki coordination) in support of task-specific user collaboration.Advanced Visual Text AnalyticsInitiated and led the research of tightly coupling interactive visualization with advanced text analytics (e.g., topic modeling and opinion summarization). Developed novel visualization and advanced analytics to improve visual text analysis. Produced the 1st system that creates topic-based, interactive visual summaries of textual documents.
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Research ManagerIbm T J Watson Research Center Apr 2001 - May 2008Context-Sensitive Conversational Information InteractionInitiated and led a multi-year research effort to develop a conversational game framework for information interaction. Developed a new class of interactive intelligent information systems that support end users to access, navigate, and analyze large data sets by engaging them in a dynamically generated multimodal conversation that is tailored to their tasks, customized to their personal preferences, and dynamically adapted to their context. This work made three major scientific advances: (1) Adaptive and robust interpretation of diverse user information requests in context with the combination of visual and natural language dialogs; (2) Automated generation of customized, interactive visual and verbal presentations of user-requested information and analysis results; and (3) Integration of above in support of analytic provenance to handle highly dynamic, unpredictable user information interaction scenarios in the real world. The establishment and development of optimization-based methodology is a key innovation to several fundamental IUI challenges (e.g., dynamic content selection and media allocation). Not only does it open up new research topics in IUI, but it also pushes IUI technologies to main-stream applications.
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Research Staff MemberIbm T J Watson Research Center Nov 1998 - Mar 2001Automated Graphics Generation (aka Visualization Recommendation)Developed the 1st theoretical and computational framework for automated information visualization generation. The theoretical framework included a formal representation of an interactive visualization by its syntax (visual features), semantics (data features), and pragmatics (user and task features). The computational framework included a set of novel engines for practical, automated generation of visualizations for real-world applications: (1) An example-based, machine learning engine for automated generation of interactive visualizations to encode unanticipated, large sets of information retrieval results in highly dynamic user interaction situations; and (2) Optimization-based approaches to dynamic data transformation and visual context management for creating quality and coherent visual displays.
Michelle Zhou Skills
Michelle Zhou Education Details
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Columbia UniversityComputer Science -
Michigan State University
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Fudan University
Frequently Asked Questions about Michelle Zhou
What company does Michelle Zhou work for?
Michelle Zhou works for Accenture
What is Michelle Zhou's role at the current company?
Michelle Zhou's current role is Managing Director, Center for Advanced AI.
What is Michelle Zhou's email address?
Michelle Zhou's email address is mz****@****ibm.com
What schools did Michelle Zhou attend?
Michelle Zhou attended Columbia University, Michigan State University, Fudan University.
What skills is Michelle Zhou known for?
Michelle Zhou has skills like Machine Learning, Algorithms, Analytics, Computer Science, Big Data, Data Mining, Human Computer Interaction, Software Development, Java, User Interface, Artificial Intelligence, Information Retrieval.
Who are Michelle Zhou's colleagues?
Michelle Zhou's colleagues are John Carlo Alcantara, Shota Tanaka, Megha Hiremath, Shivani Khadtar, Raúl Ortiz Ramos, Ace Carlo Martinez, Sanchit Mathur.
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