Engineer Data
Resume:
Lingyi Wu
************@*****.***
East Lansing, MI 48823
Career Objective: Software Engineer
Education
Michigan State University East Lansing, MI
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M.S. in Computer Science; GPA:3.67 Aug. 2012 – May. 2014
Peking University Beijing, China
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M.E. in Software Engineering; GPA:3.72 Sep. 2010 – July. 2012
Wuhan University Wuhan, China
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B.E. in Software Engineering; GPA:3.51 Sep. 2006 – July. 2010
Self Assessment
Highly skilled in Java, MATLAB, Web Development(J2EE, HTML, JavaScript, PHP), MySQL,
Hadoop, Python, Weka, L TEX, Vim
A
Extensive training in data mining, computer vision, machine learning, and massive data processing.
Excellent skills of verbal and written communication:English(fluent), Chinese(native), Japanese(basic).
Intern Experience
Aliyun Cloud Computing, Alibaba Group Beijing, Hangzhou, China
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Product Design Intern Jun.2010-Aug.2010
– Worked with the Data Platform Group. Collected and specified the requirements.
– Used Talend, Eclipse, and UML to define use cases, test cases and flow charts.
Projects
Big Data: Clustering of Cities with Similar Tweet Topics
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Goal: Analyze Tweet data from different cities with clustering method Feb.2014-May.2014
– With Python Streaming API, collected Twitter hashtags for users from different cities.
Extracted the cities and corresponding Tweet information from raw data with Hadoop.
– Using HashMap, discovered the Most Frequent 1000 hashtags. Built 1000-dimensional
vetors for more than 500 cities accordingly with the frequencies of the 1000 hashtags.
– Employed K-Means Algorithm to perform clustering analysis on the 1000-dimensional
vectors of cities.
Crowdsoucing Pedestrian Location Analysis
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Goal: Explore a real time location prediction solution with data mining tech Oct.2013-Dec.2013
– On Android ADT, designed and implemented pedestrian location information user
interface.
– Proposed the RSSI (Received Signal Strength Indication) based location-prediction approach:
Building the Correlation between RSSI values and possible pedestrian locations.
– Applied KNN Algorithm on RSSI value-location pairs to make a prediction.
Part-based Face Recognition using Supervised Learning Model
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Goal: Implement a part-based face verification model Feb.2013-May.2013
– Warped images with Face Alignment methods (Active Appearance Model) to synthesize
front view images from profile images. (dataset: Labeled Faces in the Wild)
– Applied LBP features on specific portions(eg: eyes, mouths, noses) from face images.
– Employed Supervised Learning methods, like SVM to train classifiers for face recognition.
A Local Feature-driven Approach to Unconstrained Face Alignment
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Goal: Propose a face alignment approach in unconstrained environment Nov.2012-Feb.2013
– Employed a local detector (CanAff Detector) to represent a face image as a set of SIFT
descriptors. (dataset: Labeled Face Parts in the Wild)
– With Clustering and Warping of local features from the image domain to the mean shape,
a probability density map (PDP) was obtained to indicate the possible local appearances on
face area.
– Given a test image, the shape parameter was updated so as to Maximize the Joint
Probability on PDP.
Smart Home System on IBM SaaS Platform
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Goal: Develop a web application prototype for family users of Smart Home Nov.2011-Feb.2012
– Participated in designing database and system modules.
– Used MySQL as the backend, developed the user service registration system with Spring,
Struts, Hibernate Framework.
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