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Software Engineer with experience in ML and HPC
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Georgia Institute of Technology
Ph.D, Computer Science
January 1, 2007 – January 1, 2013
Georgia Institute of Technology
B.S, Discrete Mathematics
January 1, 2003 – January 1, 2007
Zoox
Software Engineer
April 1, 2024 – Present
Foster City, California, United States · Hybrid
Staff Software Engineer
September 1, 2022 – October 1, 2023
Mountain View, California, United States
Apple
Machine Learning Engineer
October 1, 2016 – July 1, 2022
Cupertino, CA
University of Texas
ICES Postdoctoral Fellow
September 1, 2013 – August 1, 2016
Georgia Institute of Technology
Graduate Research Assistant
August 1, 2007 – August 1, 2013
Atlanta, GA
Georgia Institute of Technology
NSF Research Experience for Undergraduates
June 1, 2006 – August 1, 2006
Georgia Institute of Technology
Teaching Assistant
January 1, 2005 – May 1, 2007
ASKIT -- Approximate Skeletonization Kernel Independent Treecode
August 1, 2013 – August 1, 2016
ASKIT is a novel algorithm for training kernelized (non-linear) models for machine learning. ASKIT scales with both the number of data points and the number of features, and has successfully scaled to tens-of-thousands of compute cores. The open source implementation is available at padas.ices.utexas.edu/libaskit .
Npoint -- scalable algorithms for spatial statistics
June 1, 2010 – Present
N-point correlation functions are a key statistic for analyzing large-scale astronomical data. This open-source software library implements a novel, scalable algorithm I developed to efficiently compute this statistic using one or more compute nodes.
MLPACK
June 1, 2007 – Present
MLPACK is an efficient, open-source machine learning library written in C++. In addition to the development of core routines and testing and maintenance, I invented and implemented a new, efficient algorithm for Euclidean minimum spanning tree construction.
Cultural Fit Analysis
The candidate's background is heavily skewed towards advanced research, algorithm development, and machine learning engineering, with a strong emphasis on C++ and high-performance computing. While these skills are valuable, the target role is 'Data Analyst'. There is a potential mismatch in the depth of technical focus; the candidate's experience appears to be at a much higher, more theoretical and engineering-intensive level than a typical data analyst role. Their projects demonstrate a strong inclination towards building foundational algorithms rather than applying existing tools for data analysis, which might indicate a different career trajectory or preference. The lack of explicit data analysis tools (e.g., Python, R, SQL, specific BI tools) in their listed skills or project descriptions further suggests a potential gap in direct alignment with a standard Data Analyst role.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong capacity for independent research, problem-solving, and developing complex, scalable solutions. Their academic and professional history suggests a high degree of intellectual curiosity and a drive for innovation. However, without psychometric test results or interview data, specific soft skills like teamwork, communication style, or stress handling cannot be definitively assessed.