Master in Computational Data Science
Coursework: Advanced Introduction to Machine Learning, Machine Learning with Large Datasets, Probability Graphical Model, Multimedia Database and Data Mining, Distributed System, Search Engine, Data Science Seminar, Convex Optimization, Topics in Deep Learning
Bachelor in Information Engineering
Majid Mahzoon, Christy (Yuan) Li, Xin Li and Pulkit Grover
in IEEE Journal on Selected Areas in Communications, vol. 34, no. 5, pp. 1417-1430, May 2016.
Christy (Yuan) Li, X. Li and P. Grover
8th International Conference on Communication Systems and Networks (COMSNETS), Bangalore, 2016, pp. 1-6.
Simon Du, Yichong Xu, Christy (Yuan) Li, Hongyang Zhang, Pulkit Grover and Aarti Sign
International Conference on Machine Learning Workshop, June 2016
Christy (Yuan) Li, Tadas Baltrusaitis, Louise-Phillipe Morency
submitted to 12th IEEE International Conference on Automatic Face and Gesture Recognition
Christy (Yuan) Li*, Deepak Gopinath*, Jayanth Koushik*, Louise-Phillipe Morency
submitted to 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Research Intern on Facial Landmark Tracking in Vide
Combined Project-Out Cascaded Regression with 3D shape model for detecting landmarks on images. Ex- tended the system to predict landmarks in video by exploiting temporal features.
Research Intern on Energy-Efficient Machine Learning on Distributed Sensor Network
Proposed a rate-allocation strategy and an adaptive learning algorithm for learning distribution-parameters of sensors under communication constraint. Applied the algorithms for Brain-Computer Interaction
Research Intern on 2 view to multi-view conversion of 3D images
Implemented algorithms for generating 3D representations of images, including Block Motion Estimation and Compensation using SIFT features, and image warping by its depth map
Develop E-Signature online deposit system. Hands-on experience in J2SE, J2EE, C3, Database, Web ser- vices, DAO, UML diagramming, Struts, Spring, Hibernate, HTML, etc.
Developped Multimodal Embedding model and End-to-End deep learning model to extract scene in movies or noisy clips that corresponds to given language descriptions, as well as learn compact representation of video content and language content. Created a noval dataset called Align-MVAD. Code writtern in Python and Theano.
Implemented Mixture of Bernoullis model, Neural Network, Restricted Boltzman Machine (RBM), Deep Restricted Boltzman Machine (DRBM), Autoencoder, Denoising Autoencoder, and Neural Networks with pertained RBM/Autoencoder initialization. Trained all models for classifying MNIST images and compared their performance. Code written in Python.
Implemented Recurrent Neural Network (RNN) in Python for training character-level language model with tiny-shakespeare dataset.
Implemented Streaming Naive Bayes algorithm using Java, Hadoop and GuineaPig respectively, and Gibbs Sampling LDA on Parameter Server for efficient training of large-scale document classification
Implemented large-scale matrix factorization with distributed stochastic gradient descent in Spark for text modeling
Developped a facial expression system that is robust for Asian and Western facial expressions, as well as created a novel large dataset on Asian expression. The system used random forest, SVM and deep neural network
Implemented snowball algorithm for a large-scale graph in Python and used Gephi visualization software to visualize the entire graph
Extended graphMiner package with a k-core algorithm using Python and SQL. Conducted experiments on real world large graphs to discover graph patterns
Implemented a neural network for classification using Python. Proposed a threshold-based algorithm and randomized structural learning method to optimize neural network architecture
Implemented a distributed system supporting RPC between remote server and clients with robust scalability in real time environment by using caching, schedueling, network data analysis, and two-phase commit. Writtern in C and Java.
Implemented a search engine with boolean retrieval, ranked retrieval, strutured queries, query expansion, federated search, aggregated search, vertical search, selective search, and supervised learning algorithms for improving performance.
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