YUANFENG (PHIL) WANG [abnnva@r.postjobfree.com] 949-***-****

OBJECTIVE

A full-time engineer or scientist position in data analytics and predictive modeling

EDUCATION

Ph. D. in Physics, University of California, Irvine (expected) 02/2013

M. S. in Physics, University of California, Irvine (GPA: 3.75/4)

01/2009

B. S. in Applied Physics, Tsinghua University, Beijing, China 06/2002

SKILLS

Hand-on experience of predictive modeling, machine learning with large data sets

Proficient in Python, R and MATLAB, familiar with C/C++, Java, Mathematica

Familiar with statistical analysis/test, data mining tools (Hadoop environment, Amazon EC2, MySQL) and

algorithms

Familiar with Linux, shell scripting, subversion/GIT and cluster computing environment

EXPERIENCE

Research Analytics Intern, ATL, Adobe Systems Inc. 06/2012 – 09/2012

Digital marketing, Web analytics, Reinforcement learning, Markov decision process

Apply reinforcement learning framework to website Ads. content optimization, gave tutorial to product team

Tested different regression/classification method (SVM, gbm, Extra-trees) for modeling customer behavior

09/2006 – Present

Graduate Student Researcher, Department of Computer Science

Machine learning, MCMC/sampling methods, Data analysis, Systems biology

Developed novel parameter inference algorithms for stochastic dynamic systems with discrete states, applied

it to gene regulatory network modeling (implemented in Matlab, C)

Developed an efficient algorithm to do model selection in Gaussian graphical model with latent variables

based on matrix decomposition, applied to gene expression data sets and stock return data (implemented in Matlab)

Proposed a graphical model for analyzing histone modification pattern using high-throughput ChIP-Seq data

and developed a variational inference algorithm for model optimization (implemented in Python)

Teaching Assistant, Department of Physics & Astronomy 03/2008 – 12/2011

Gave lectures, led discussions, and tutored students in more than eight undergraduate physics courses and one

graduate physics course

PROJECTS

CS273A Machine Learning final project, ranked 1st in class, 6th overall (KDD cup 04 Quantum physics problem:

two-class classification problem, methods used including AdaBoost, SVM and logistic regression)

GRADUATE COURSES

(Physics, Math Dept.) Numerical Methods, Math Physics, Statistical Physics, Classical Mechanics, Quantum

Mechanics, Electro-magnetism Theory, Stochastic Differential equation

(CS, Engineering Dept.) Computational Systems Biology, Convex Optimization, Engineering Math I/II, Intro. to

Algorithm (audited), Machine Learning etc.

SELECTED PUBLICATIONS

Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent, Wang

Y, Christley S, Mjolsness E, Xie X. BMC Systems Biology 2010, 4:99

Efficient Latent Variable Graphical Model Selection via Split Bregman Method, Ye G, Wang Y, Chen Y., Xie X.

arXiv:1110.3076

Discovering and Mapping Chromatin States Using a Tree Hidden Markov Model, Biesinger J, Wang Y (co-first

author), Xie X. submitted

SOCIAL ACTIVITIES

President of Chinese Student & Scholar Association at UCI 2007 – 2008

Member of Sino-American Biomedical & Pharmaceutical Professionals Association

*Working Authorization: currently on F1 visa, will need H1B sponsorship

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