Summary of skills



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Zhuoyao Wang




(505)750-2518

wzy0791@gmail.com



935 Buena Vista Dr SE Apt E105, Albuquerque, NM 87106



SUMMARY OF SKILLS

  • Seven plus years of coding experience in C/C++, Python, Java, Matlab

  • Experienced in software development GUIs and APIs including: Eclipse, Android SDK and Qt

  • Professional background in developing numerical analysis and simulation tools in MATLAB

  • Practical experience on HTML5 and web applications

  • Routinely working on Linux OS

  • Strong analytical, problem solving and communication skills

  • In-depth knowledge of distributed/cloud computing systems and power-grid systems

  • Rich experience in probabilistic modeling and mathematical analysis

  • Excellent team worker and fast-learner


EDUCATION

University of New Mexico Ph. D candidate in EE expected graduation in early Fall 2015

University of New Mexico M.S. in Electrical Engineering Dec. 2011

Jilin University, China B.E. in Electrical Engineering June 2008

RESEARCH

Cloud Computing

  • Developed a probabilistic multi-tenant model for characterizing performance of a group of workloads (for example, multi-tier applications) serving on modern cloud platforms, such as Amazon AWS EC2.

  • The proposed model is of great benefit for cloud brokers or small-to-mid enterprises to operate and manage their own cloud services by giving the provisioning balance between performance and pricing

  • Proposed a greedy heuristic resource allocation (or load balancing) algorithm for achieving best computational performance in cloud computing environment with rigorous proof of the optimality




Resource Allocation in Distributed Systems




  • Developed and implemented a lattice algorithm, which highly outperformed the previous algorithms in terms of idea clarity, code readability and computational complexity, for solving problems of coupled difference equations

  • Successfully solved a tricky problem which is to analytically characterize the probability for the first-time consensus by creatively using conditioning method recursively


Cascading Failures in Power Grids

  • Built a novel continuous-time Markov chain model to understand and approximate cascading failures in power grids. The model embeds the details of physics in power but is still analytical and linear in complexity.

  • Successfully derived asymptotic analysis on the proposed model by having carefully observed some of the imperceptible features when performing matrix operation


EXPERIENCE

Invited Researcher Qatar University, Doha, Qatar May 2013 - Sep. 2013

  • Be in charge of the Qatar National Research Fund (QNRF) project about Cloud Computing

  • Arranged routine Skype meetings between PIs from US universities and Qatar University

  • Helped in writing 6-month technical reports


Research Group Websites Creator and Maintainer University of New Mexico

  • Created and maintained the following two research-group websites

  • http://www.ece.unm.edu/lb/

  • http://powergrid.ece.unm.edu


Teaching and Teaching Assistant University of New Mexico

  • Giving lectures for courses: ECE340 Probabilistic Methods in Engineering; ECE541 Probability and Stochastic Processes (Graduate course)

  • TA for ECE131 Programing Fundamentals (i.e., Programming in C)



SELECTED PUBLICATIONS

1. Z. Wang, M. M. Hayat, Nasir Ghani and Khaled B. Shaban,“A probabilistic multi-tenant model for virtual machine mapping in cloud systems,” in Proc. of The Third IEEE International Conference on Cloud Networking to be held in Luxemburg, October 8-10, 2014.



2. Z. Wang, M. M. Hayat, M. Rahnamay-Naeini, Y. Mostofi, and J. E. Pezoa, Consensus-based Estimation Protocol for Decentralized Dynamic Load Balancing over Partially Connected Networks,” in Proc. of The 50th IEEE Conference on Decision and Control and European Control Conference (IEEE CDC-ECC 2011) in Orlando, Florida, December 12-15, 2011.

3. M. Rahnamay-Naeini, Z. Wang, N. Ghani, A. Mammoli, and M. M. Hayat, “Stochastic Analysis of Cascading Failure Dynamics in Power Grids”, IEEE Transactions on Power Systems, vol.29, no.4, pp.1767-1779, July 2014.

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