Computer Science Data

Brian Hu

510-***-**** (home) 510-***-**** (mobile) ************@*****.***

OBJECTIVE

Full time position in data science/data analytics leveraging my unique combination of mathematics, statistical analysis, and programming skills

EDUCATION

BS Mathematics with emphasis on Computational and Applied Math, 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

Engaged in two projects during my internship:

Deployed a cloud-based environment for Cavium to supplement scarce internal computation capacity. 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 as needed. Conducted and presented a cost and performance (QoS) analysis of using cloud-based vs. procuring additional internal compute resources.

Developed Python scripts to analyze the effectiveness of address hashing algorithms for mapping physical memory addresses to CPU cache lines. . Simulated workload in the form of memory access patterns and stride sizes. Simulated multiple datasets and hash algorithms, analyzed the spreading characteristics to sets and ways of the cache, and identified hot spots of line contention. Presented the findings to CPU architects 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. Synchronized barcode serial numbers on the panels with details on each panel 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

510-***-**** (home) 510-***-**** (mobile) ************@*****.***

CLASS PROJECTS

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Data Analysis Projects that take given datasets (such as real estate sales, economic mobility, and bicycle rental patterns), 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.

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:

oAdvanced 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

oModern Regression: An in depth course on building statistical models with an emphasis on Linear Regression and R programming.

oSymbolic Programming Methods: An advanced course on Symbolic Computing, taught in Wolfram Mathematica.

Computer Science:

oPrinciples of Imperative 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.

oGreat Theoretical Ideas in Computer Science: Abstract concepts of Computer Science. Notable topics are Graph Theory, Turing Machines, and the P=NP problem.

oFundamentals of Programming: Introduction to basic programming topics. Taught in Python.

Core Math Classes:

oDifferential Equations and Partial Differential Equations

oPrinciple of Real Analysis I and II

oIntroduction to Probability Theory and Statistical Inference

oOperation Research

oNumerical Methods

oLinear Algebra, Matrices and Linear Transformation, Algebraic structures

oMulti-dimensional Calculus

oConcepts of Mathematics

Putnam Seminar Math Competition: (2012-2015)

ACHIEVEMENTS/AWARDS

National AP Scholar Award

Santa Clara Valley Math Association

o2012 Senior Outstanding Senior Student

Valley Christian High School Quest Math Award

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