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Zhou Xu
XXXXXX 54, Sunderland, MA 01375
• Physics /PhD / GPA 3.7 / University of Massachusetts Amherst
Sept. 2013-Expected 2020
• Computer Science / M.S. / GPA 3.5 / University of Massachusetts Amherst Sept. 2018-Expected 2020
• Physics / B.S. / GPA 3.5 / Nanjing University
Sept. 2009-July 2013
Algorithms for Data Science, Coding Theory and Applications, Distributed Operating System, Formal Language
Theory, Introduction to Natural Language Processing, Machine Learning, Math Statistics I, Monte Carlo Techniques,
Optimization for Computer Science, Software Engineer :Synthesis & Development , Database Design &
Implementation, Intelligent Visual Computing
Programming Languages: Python, C++, Java, SQL, JavaScript, CSS, XML, PyTorch
Distributed Coordination System for Smart Home (Python)
The system can accomplish automation tasks that are related to energy saving and home security. The system can
add new devices and sensors freely
• Built a client-server distributed system that can be deployed in a “smart” home with sequential consistency,
strong availability and fault tolerance
• Used servers as a distributed coordinator to maintain the state, store the data and dispatch the task for each
smart sensor and device
• Implemented the vector lock to maintain the causal order of events, replication of two servers to improve write
availability, failover mechanism to handle the server crash
Similarity in Paired Questions (Python)
The project goal was to predict whether two given questions had the same intent or not. The data collected were
• Implemented Long Short-Term Memory (LSTM) and XGBoost to train the model
• Implemented a baseline method using bag-of-words, and Cosine similarity to compare with the two methods
• Achieved 78.21% the best accuracy for LSTM, and 75.74% for XGBoost with good result and much less computing
cost. The results beat most people’s modeling in this Kaggle challenge
Classification of Cuisine with Recipe Ingredients (Python)
• Implemented a classification system to predict the type of cuisine (e.g. American, Korean) from the recipe and
visualized the networks for different cuisines
• Pre-processed the data with Bag of word and Word2vec
• Built the training model with multiple machine learning algorithms e.g. Support Vector Machine, logistic
regression, neural network and Ensemble learning
• Achieved the best accuracy 78% by logistic regression classifier based on bag of word. Found Korean and
Jamaican cuisines share the most similarities according to the final network plot EXPERIENCE
CarPoO Studio Co-founder (WeChat app, frontend design & architecture)
July 2017-Sept 2019
• Co-founded CarPoO targeting long-distance, appointment ride share market for Chinese students
• Dominated western Mass and central Connecticut market with 5000+ unique customers
• Facilitated tens of thousands of shared rides
• Engineered ride info sharing, mutual match detection and messaging
• Implemented MVC architecture to build up the app. Developed the frontend app using native JavaScript, XML
and CSS.
Dynamic Touch-based Bacteria-Device Two-Way Communication
• Studied the bacterial sense of touch to establish the new research area of sensory-based bacterial
• Build the flow cell system to capture E. Coli on different surfaces using electrostatic force or depletion force
• Developed python code for particle counting and tracking
Physics of Intelligence/Neuron Computing
• Cooperating with microbiologists to implement basic computing with the bacteria called geobactor
• Designed and built the device to interact with geobactor based on beagleboard black
• Developed python code to control the whole system
Behavior of a Non-equilibrium Self-Organizing System: A Potential Means to Enhance Energy Efficiency
with Functional Intelligence
• Built the device which can apply high electric field to drive conductive beads forming tree-like pattern
• Implemented video and electrical measurements to investigate the transient and steady-state behavior of self-
organizing primitive dissipative structure under a range of initial and driving conditions
• Developed python code to perform particle tracking
• Simulated the motion of conductive beads under electric field by python code
Rapid Electrostatic Capture of Rod-Shaped Particles on Planar Surfaces: Standing up to Shear
• Investigated the electrostatically driven capture of flowing rod-shaped and spherical silica particles onto a flow
chamber wall that carries the opposite electrostatic charge from the particles
• Developed python code for particle counting
“Rapid Electrostatic Capture of Rod-Shaped Particles on Planar Surfaces: Standing up to Shear,” Langmuir 2019, 35,
40, 13(XXX) XXX-XXXX7


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