Brian Hu is seeking a full-time position in data science/analytics leveraging his skills in mathematics, statistics, and programming. He has a B.S. in Mathematics from Carnegie Mellon University with experience in statistical analysis using R, mathematical topics, and programming languages including Python, C++, and Java. His past internship projects involved deploying a scalable cloud computing environment using Python scripts and analyzing CPU cache performance. Brian also developed an Android app to track solar panel testing and led a high school experiment aboard the International Space Station.

1. Brian Hu
510-668-1350 (home) 510-299-8592 (mobile) brianjameshu@gmail.com
OBJECTIVE
Full-time position in data science/data analytics leveraging my unique combination of mathematics,
statistical analysis, and programming skills.
EDUCATION
B.S. Mathematics with Computational and Applied Math concentration, Carnegie Mellon University,
Pittsburgh, PA,May 2016
SKILLS
 Statistical analysis: R,R Markdown, statistical inference and probability
 Mathematical topics:Theoretical/Pure Math,Numerical Analysis, Operations Research,
Matrices/Algebraic/Linear Transformations
 Programming languages: C/C++,Python, Java,PBASIC,Android Development
 Math Software: LaTeX, Mathematica, Maple, MATLAB
EXPERIENCE
Cavium, Inc, Engineering Internship, CPU Architecture Group Summer 2015
Completed two projects:
 Deployed a scalable and cost-effective cloud-based environment to add compute
capacity on demand. Created Python-based Linux scripts and used MIT’s
StarCluster, an open source tool for managing virtual machines, to allow architects to
easily submit computational intensive simulations jobs to Amazon Web Services.
Analyzed the cost structure of using internal machines vs. cloud-based resources
factoring in Amazon’s QoS-based pricing. Presented findings which will be used for
future deployment considerations.
 Completed a study of the effectiveness of address hashing algorithms for mapping
physical memory addresses to CPU cache lines. Simulated memory access patterns
and stride sizes of various workloads over multiple datasets and hash algorithms.
Developed Python scripts to analyze the address spread characteristics into sets and
ways of the cache, and identified hot spots of line contention. Presented the findings
to the CPU Architecture Group for future design/architectural considerations.
Renewable Energy Testing Center (RETC) Summer Internship Summer 2014 and 2011
Developed a tablet-based Android app to track solar panel test flows. App reads and
synchronizes the panel’s barcode serial numbers and stores its testing results in a MySQL
database server. Used Eclipse to write the app in Java and XML, while the backend was coded in
PHP.
International Space Station Science Experiment. August 2011 to May
2012
As Payload Technical Leader of a 9 member student-led team in high school, I led the research to design,
build, and place an electro-plating experiment aboard the ISS in 2012. As Electro-plating has the potential
to be purer in micro-gravity, this experiment may lead to better maintenance solutions for the ISS.
Brian James Hu

2. 510-668-1350 (home) 510-299-8592 (mobile) brianjameshu@gmail.com
CLASS PROJECTS
.
 Data Analysis Projects that take given datasets (such as realestate sales,economic mobility, and
bicycle rental patterns) and applied linear regression models of the data, histograms, normality plots
and residual plots using R markdown. Presented the data science finding reports with relationship
inferences of the dataset made from analysis of said regressions to fellow classmates.
 Implemented a Peg-Solitaire solver that analyzes the initial board configuration and determines a
winning solution (if one exists) using a DFS algorithm. The program inputs moves until an
unsolvable configuration is reached and then backtracks to a point where a new move can be made.
My implementation was optimized by storing previous configurations in a hash table to improve
speed and efficiency
COURSEWORK
 Data Analysis and Applied Math:
 Advanced Data Analysis: A follow-on class to Modern Regression, advanced theory of
Linear Regression and its applications. Completed 10 data analysis reports of various topics.
 Modern Regression: An in depth course on building statistical models with an emphasis on
Linear Regression and R programming.
 Symbolic Programming Methods: An advanced course on Symbolic Computing, taught in
Wolfram Mathematica.
 Computer Science:
 Principles ofImperative Computing: Basic data structures and how to construct
algorithms. Labs completed include constructing a text-editor, a Peg-Solitaire solver, and a
Virtual Machine. Taught in C.
 Great Theoretical Ideas in Computer Science: Abstract concepts of Computer Science.
Notable topics are Graph Theory, Turing Machines, and the P=NP problem.
 Fundamentals ofProgramming: Introduction to basic programming topics. Taught in
Python.
 Core Math Classes:
 Differential Equations and Partial Differential Equations
 Principle of RealAnalysis I and II
 Introduction to Probability Theory and Statistical Inference
 Operation Research
 Numerical Methods
 Linear Algebra, Matrices and Linear Transformation, Algebraic structures
 Multi-dimensional Calculus
 Concepts of Mathematics
 Putnam Seminar Math Competition: (2012-2015)
ACHIEVEMENTS/AWARDS
 National AP Scholar Award
 Santa Clara Valley Math Association
 2012 Senior Outstanding Senior Student
 Valley Christian High School Quest Math Award