Lingyi Wu

517-***-****

ace7g8@r.postjobfree.com

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

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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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