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Machine Learning Engineer at Apple
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Virginia Commonwealth University
Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering
January 1, 2012 – January 1, 2016
Peking University
Master's degree, Electronics Engineering and Computer Science
January 1, 2009 – January 1, 2012
East China Normal University
Bachelor's degree, Applied Psychology (Minor)
January 1, 2006 – January 1, 2009
East China Normal University
Bachelor's degree, Telecommunications (Major)
January 1, 2005 – January 1, 2009
Apple
Machine Learning Engineer
June 1, 2020 – Present
Cupertino, California, United States
Software Engineer, Machine Learning
November 1, 2017 – May 1, 2020
United States
Recommender Systems
May 1, 2017 – Present
• Used collaborative filtering to predict movie ratings for the movies that users have not yet rated. • Trained the collaborative filtering model using Matlab and used this model to recommend the movies with the highest predicted ratings to the user.
Image Compression
May 1, 2017 – Present
• Used K-means to find 16 colors that best cluster the pixels in the 3D RGB space, and used the 16 colors to replace the pixels in the original image. The image was compressed from 393, 216 bits to 65, 920 bits. • Used principal component analysis (PCA) to find a low-dimensional representation of face images. The number of features was reduced from 1024 to 100.
Hand-written Digits Recognition
April 1, 2017 – Present
• Implemented a neural network to recognize handwritten digits using Matlab. • Built a 3-layer neural network, in which the hidden layer contains 25 units. • Implemented feedforward propagation and backpropagation algorithms to compute cost function and gradient. • Trained parameters and predicted using this neural network with 92.8% testing accuracy.
Range Search and Nearest Neighbor Search
April 1, 2017 – Present
• Implemented a 2d-tree to represent a set of points in 2D space using Java. • Found all points in a query axis-aligned rectangle in O(R+logN) time on average, where R denotes the number of points returned and N denotes the number of points in the set. • Found closest point to query point in O(logN) time on average.
Spam Classification
April 1, 2017 – Present
• Built a vocabulary list by choosing the most frequently occurring words, and used this list to extract features. • Built a spam classifier using a linear support vector machine (SVM), and classified test set with 98.7% accuracy.
Color Learning and Target Detection
March 1, 2017 – Present
• Collected color samples of yellow ball from training images using Matlab. • Estimated two models for ball color: 2D Gaussian model and Gaussian Mixture Model (GMM). • Detected yellow ball and estimated its position using Maximum Likelihood Estimate (MLE) for 2D Gaussian model and Expectation-Maximization (EM) for GMM respectively.
Target Tracking and Location Prediction
March 1, 2017 – Present
• Implemented a Kalman filter for ball tracking in 2D space using Matlab. • Predicted ball’s location 330ms ahead constantly to plan soccer robot’s next motion.
Occupancy Grid Mapping and Pose Tracking
March 1, 2017 – Present
• Implemented occupancy grid mapping algorithm for a 2D floor map using Matlab. • Built a map by incorporating range sensor readings and its poses at each of the measurement times. • Implemented a particle filter for pose tracking in 2D space.
Acoustic Ranging System
November 1, 2015 – July 1, 2016
• Estimated distance between two computers using C/C++ by transmitting Wi-Fi and audio signals. • UDP was used to broadcast IDs of computers through Wi-Fi. • The direct sequence spread spectrum was adopted to distinguish audio signals sent from different computers. • Matched filter was used to decrypt audio signals and estimate their traveling time.
Data Classification
November 1, 2013 – December 1, 2013
• Implemented SVM and decision tree using Matlab and R to classify the quality data of wine, and tuned the parameters using cross-validation technique. • The SVM and decision tree were carefully compared using the same tuning, validation and test data. • The results showed that SVM performs better than decision tree and the wine’s quality can be predicted by wine’s attributes.
Cultural Fit Analysis
The candidate's background is heavily focused on machine learning and signal processing, which aligns well with data-intensive roles. The project diversity indicates adaptability and a broad interest in different problem domains. However, the target role is 'Data Analyst', which might be a slight mismatch given the candidate's senior-level Machine Learning Engineer experience. While data analysis is a core component of ML, the role title suggests a potentially different focus (e.g., business intelligence, reporting, dashboarding) which is not explicitly covered in the projects. The candidate's experience is more geared towards model development and deployment rather than pure data analysis for business insights.
Soft Skills & Operational Fit
The candidate's project descriptions suggest a strong analytical and problem-solving mindset. The academic background and industry experience imply a structured approach to complex challenges. However, specific soft skills like teamwork, leadership, or communication in a team setting cannot be directly assessed from the provided data.