Bain & Company since Jan 2010
Consultant
Mosaic Investments Aug 2009 - Jan 2010
Intern
UC Berkeley Jan 2009 - Aug 2009
Green Purchasing Associate
Bain & Company Jun 2008 - Aug 2008
Summer Associate
BearingPoint 2000 - 2007
Manager
Education:
University of California, Berkeley - Walter A. Haas School of Business 2007 - 2009
MBA, General Management
Tsinghua University 1992 - 1997
2nd Middle school of Beijing Normal University
Texas A&M University
Skills:
Tmt Corporate Strategy Joint Ventures Strategic Partnerships Private Equity Industrial Goods Mergers and Acquisitions Due Diligence Business Planning Business Strategy Strategy Corporate Development Business Development Finance Investments Financial Modeling Management Consulting Management Program Management Leadership Consulting Cloud Computing Competitive Analysis
Wayne Kao - Mountain View CA, US Bo Zhang - San Francisco CA, US Francis Luu - San Francisco CA, US Ming Hua - Mountain View CA, US
International Classification:
G06F 17/30
US Classification:
707740, 707E17089, 707E17032
Abstract:
A social networking system provides a personalized set of bookmarks to a user based on the user's interactions with the bookmarks and/or the items associated with the bookmarks. The personalized set of bookmarks is grouped by category, and the categories are ordered in accordance with rankings for the categories. The rankings for the categories are determined based on the highest ranked item from each category of item, and the ranking of the items is determined based on the user's interactions with the bookmarks and/or associated items.
Contextually Relevant Affinity Prediction In A Social Networking System
A tunable affinity function serves one or more processes running in a social networking environment, where each process may request a measure of affinity for a particular user. A module that implements the affinity function computes the requested measure of affinity by combining (e.g., adding) a weighted set of predictor functions, where each predictor function predicts whether the user will perform a different action. The weights are provided by the process that requests the measure of affinity, which allows the requesting process to weight the predictor functions differently and thus tune the affinity function for its own purpose.
Generating Card Stacks With Queries On Online Social Networks
- Menlo Park CA, US Ming Hua - Palo Alto CA, US Michael Vernal - San Francisco CA, US Yang Qin - Belmont CA, US Dan Ionut Fechete - Mountain View CA, US Xinpan Xiao - Pacific Palisades CA, US Yu Huang - San Carlos CA, US Lu D. Chen - Menlo Park CA, US Saurabh Prafulla Chakradeo - San Francisco CA, US Dharmesh A. Bhatt - E. Palo Alto CA, US Alex Himel - San Francisco CA, US
In one embodiment, a method includes receiving, from a client system of a first user, an input from the first user indicating a query-domain and one or more query-filters, generating a card cluster comprising a plurality of cards referencing a plurality of objects corresponding to the indicated query-domain and one or more query-filters, ranking the plurality of cards based on one or more user-engagement factors, and sending, to the client system in response to the input from the first user, instructions for displaying the card cluster to the first user, the cards of the card cluster being ordered based on the rankings associated with the cards.
Personalized Bookmarks For Social Networking System Actions Based On User Activity
- Menlo Park CA, US Bo Zhang - San Francisco CA, US Francis Luu - San Francisco CA, US Ming Hua - Mountain View CA, US
International Classification:
G06F 17/30 G06Q 50/00
Abstract:
A social networking system provides a personalized set of bookmarks to a user based on the user's interactions with the bookmarks and/or the items associated with the bookmarks. The personalized set of bookmarks is grouped by category, and the categories are ordered in accordance with rankings for the categories. The rankings for the categories are determined based on the highest ranked item from each category of item, and the ranking of the items is determined based on the user's interactions with the bookmarks and/or associated items.
- Menlo Park CA, US Ming Hua - Palo Alto CA, US Michael S. Vernal - San Francisco CA, US Yang Qin - Menlo Park CA, US Dan lonut Fechete - Mountain View CA, US
International Classification:
G06F 17/30
Abstract:
In one embodiment, a method includes receiving, from a client system of a first user of the communication system, an input from the first user to access a card-stack interface, generating a card cluster comprising a plurality of cards, each card comprising a suggested query referencing a query-domain and one or more query-filters, wherein each query-filter references one or more objects associated with the communication system, and wherein each card in the card cluster is ranked within the card cluster based on a predicted click-thru rate (CTR) for the card based on one or more user-engagement factors, and sending, to the client system in response to the input from the first user, the card-stack interface for display to the first user, wherein the card-stack interface comprises the card cluster, the cards of the card cluster being ordered based on the rankings associated with the cards.
Object Recommendation Based Upon Similarity Distances
- Menlo Park CA, US Ming Hua - Palo Alto CA, US Yang Qin - Menlo Park CA, US
International Classification:
G06F 17/30
Abstract:
Exemplary methods, apparatuses, and systems receive a candidate object with which a user can interact within a network service. For each of a first plurality of objects with which the user has had a positive interaction, a first value representing a commonality between the candidate object and each of the first plurality of objects is determined. For each of a second plurality of objects with which a user has had a negative interaction, a second value representing a commonality between the candidate object and each of the second plurality of objects is determined. An aggregate positive distance is determined using a plurality of the first values. An aggregate negative distance is determined using a plurality of the second values. The candidate object is displayed or not displayed to the user as a recommendation based upon a difference between the aggregate positive distance and the aggregate negative distance.
Grouping Recommended Search Queries In Card Clusters
- Menlo Park CA, US Ming Hua - Palo Alto CA, US Saurabh Prafulla Chakradeo - Sunnyvale CA, US
International Classification:
G06F 17/30 G06F 3/0482 H04L 29/08 G06F 3/0484
Abstract:
In one embodiment, a method includes receiving, from a client system of a first user of a communication network, an input from the first user to access a card-stack interface, generating one or more card clusters from a plurality of cards, each card comprising a query referencing a query-domain associated with the communication network and zero or more query-filters for the query-domain, wherein each query-filter references one or more objects of the communication network, each card cluster comprising one or more cards from the plurality of cards, the cards being formed into card clusters based on a card-affinity between the cards, and sending, to the client system in response to the input from the first user, the card-stack interface for display to the first user, wherein the card-stack interface comprises one or more of the card clusters.
Personalized Bookmarks For Social Networking System Actions Based On User Activity
- Menlo Park CA, US Bo ZHANG - San Francisco CA, US Francis LUU - San Francisco CA, US Ming HUA - Mountain View CA, US
International Classification:
G06F 17/30
Abstract:
A social networking system provides a personalized set of bookmarks to a user based on the user's interactions with the bookmarks and/or the items associated with the bookmarks. The personalized set of bookmarks is grouped by category, and the categories are ordered in accordance with rankings for the categories. The rankings for the categories are determined based on the highest ranked item from each category of item, and the ranking of the items is determined based on the user's interactions with the bookmarks and/or associated items.
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Atlanta’s Chinese community has especially deep worry about coronavirus
Others dont go that far. Ming Hua, who serves on a campus board for the Atlanta Contemporary Chinese Academy, said he believes few families locally have been to China in recent weeks or hosted visitors from there. He said he sees avoiding Chinese businesses as an overreaction.