Projects
This page contains most of my outstanding contributions to previous scientific research projects
Leveraging Soft Functional Dependencies for Indexing Multi-dimensional Data
Research Internship, SCALE Lab, Imperial College London
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Explored more situations where Soft Functional Dependencies could be applied, investigated the possible connections between the dependencies of high-dimensional data and machine learning or deep learning models.
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Completed the theoretical proof of the efficiency of Soft Functional Dependencies in the paper “Leveraging Soft Functional Dependencies for Indexing Multi-dimensional Data”.
In-Patient Therapy Analytics
Research Assistant, Computer Science and Health Care Center, the Johns Hopkins University
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Made Machine Learning model-based predictions of the readmission distribution of patients on a time scale based on patients' therapy and demographics data. Cooperated with hospitals and gave clinical guidance based on the analysis.
Simulating Taxi Ride Sharing at Scale
Visiting Scholar, Center for Urban Science + Progress (CUSP) and Big Data Interaction (BDI) Lab, NYU
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Investigated the unmet taxi demand problem in New York City using big data techniques, focused on the study of ride-sharing at a scale that can simulate ride-sharing scenarios throughout the five boroughs of NYC based on a rich set of constraints specified by drivers and riding passengers, applied Bipartite Method (Hungarian Algorithm) into the simulation.
Bilateral Control and suppressing traffic flow instabilities
Research Assistant, Intelligent Transportation Research Center, EECS, MIT
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Included platooning effects and chaos system when dealing with the stability of a new bilateral system. Cooperated with Post-doc Associate Liang Wang in the EECS department and built a new semi-discrete simulation under the instructions of prof. Horn.
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Completed paper “Why Do We Need Bilateral Control”, which was accepted by IEEE UV Conference 2018.
