Caiyi Zhu has a Master's degree in Applied Science from the University of Toronto with a specialization in machine learning, optimization, and wireless communications. He has over 3 years of experience in machine learning and data science as a software engineer. His skills include Python, Java, C++, and machine learning algorithms such as deep learning, regression, clustering, and natural language processing. He has experience building models for financial market prediction, credit risk analysis, and topic modeling on Twitter data.

1. Caiyi Zhu
Contact
Information
Tel: (647) 891-7370 E-mail: zhucaiyibupt@gmail.com, Loc: Downtown of Toronto
Education University of Toronto, Toronto, Canada
Master of Applied Science Aug. 2013 – Sept. 2015
• Specialization: Machine learning, Optimization, Wireless Comm. GPA: 3.85/4.0.
• Relevant courses: Machine Learning, Convex Optimization, Statistical Communication
Theory, Non-Linear Optimization, Integer Programming.
Beijing University of Posts and Telecommunications, Beijing, China
Bachelor of Engineering Sept. 2009 – July 2013
• Major: Electrical Engineering, GPA: 91.2/100, Math GPA: 98/100
Computer
Skills
Python (3 years), Java(2 years), C++(2 year), Scala, Spark, MongoDB, MySQL
Machine learning algorithms:
• Deep learning models including CNN, feed-forward Neural Nets, RNN/LSTM;
• Classic algorithms including logistic regression, linear regression, SVM, EM, GMM,
Naive-Bayes, KNN, KMeans, decision tree/random forest;
• Dimension reduction algos including PCA and its variants, t-SNE (data visualization),
auto-encoder;
• Natural Language algorithms including bag of words, word embedding(word2vec), LDA;
• Ensemble algorithms including bagging and boosting, data augmentation.
Machine learning libraries: Tensorflow, theano, keras, sci-kit learn, NLTK, gensim and etc..
Professional
Experience
QEData.io (A CSI Group Company and FinTech Partner), Toronto, Canada
Software Engineer, Machine Learning Nov. 2015 – present
• Built a general-purpose deep learning model using python, implemented stacked auto-
encoder for model pre-training. The model has been used to predict the financial market
movement, bank customer credit risk and corporate cash flow.
• Built a tweet sentiment analyzer using Convolution Neural Nets (Keras, Tensorflow) and
rule-based method using Vader lexicon.
• Built topic modeling system using LDA and clustering methods to provide topic trending
on twitter.
• Use Apache Spark framework to speed up model training.
Agilent Technology Inc., Beijing, China
Software Engineer Intern Sept. 2012 – Mar. 2013
• Tested the Interchangeable Virtual Instrumentation driver of LTETDD Module (C#).
Academic
Projects
Analysis of Clustering Algorithms in CRAN
Research project of Master Thesis Aug. 2013 – Sept. 2015
• Studied cloud-based radio access networks (CRAN) in wireless communication systems.
• Analyzed system performance under various clustering schemes with stochastic geometry.
• Simulated wireless communication systems and optimized resource allocation of the net-
work.
Numerical Optimization Solvers
Course project of Numerical Optimization Sept. 2013 – Dec. 2013
• Implemented steepest descent algorithm, Newton’s method and quasi-Newton’s methods
including Broyden family and Alternate Broyden family.
• Implemented different line search algorithm including Armijos rule, dichotomous search,
bisection search, Fibonacci search and golden search.

2. • Built a constrained optimization solver using barrier method, penalty method, augment-
ed Lagrangian method, and successive quadratic programming (SQP) including rudi-
mentary SQP (RSQP), SQP-BFGS, merit SQP (MSQP).
Sentiment Analyzer
Course project of Pattern Recognition and Machine Learning Jan. 2012 – June. 2012
• Used Bag of word method to do text vectorization
• Used term-frequency, inverse-document frequency to transform the vectorized text.
• Built a sentiment analyzer using Naive Bayes algorithm and support vector machine
algorithm, got 90% on IMDb movie review dataset.
Honors and
Awards
Rogers Scholarship, University of Toronto. 2014
Mathematical Contest in Modeling, second prize, Beijing. 2012
National Mathematical Contest, first prize, Beijing. 2011
Publication [1] Caiyi Zhu, Wei Yu, “Modeling and Analysis of User-centric Cooperation in Network
MIMO Systems,” submitted to IEEE Transaction on Wireless Communications.
[2] Kianoush Hosseini, Caiyi Zhu, Wei Yu, and Raviraj S. Adves, “Optimizing Large-Scale
MIMO Cellular Downlink: Multiplexing, Diversity, or Interference Nulling?,” submitted to
IEEE Transaction on Wireless Communications.
[3] Caiyi Zhu, Wei Yu, “Stochastic Analysis of User-Centric Network MIMO,” in 2016 IEEE
17th International Workshop on Signal Processing Advances in Wireless Communications
(SPAWC).