Apple Vision Pro | Deep Learning & Computer Vision
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Apple Vision Pro, Semi-supervised learning, representation learning, augmented reality, 3D reconstruction https://mriaz.github.io
UC San Diego
Master’s Degree, Computer Science
January 1, 2014 – January 1, 2016
Lahore University of Management Sciences
BS, Electrical and Electronics Engineering
January 1, 2008 – January 1, 2012
Apple
Deep Learning Engineer
October 1, 2016 – Present
Cupertino, California
University of California, San Diego
Graduate Student Researcher
April 1, 2015 – September 1, 2016
La Jolla, California
Lahore University of Management Sciences
Research Assistant
June 1, 2012 – August 1, 2014
Lahore, Pakistan
Shadow-Aware Single View Face Reconstruction
March 1, 2015 – September 1, 2016
Implemented a fully automatic, state of the art single view reconstruction system for human faces. The reconstruction is achieved by decomposing the image into its intrinsic components namely diffuse, specular, albedo and lighting. A realistic shading model is used in the process which includes effects like subsurface scattering and specular highlights . We were able to get a high quality reconstruction and capture features like small wrinkles and pimples.
Video Stabilization
January 1, 2014 – June 1, 2014
Designed a complete video stabilization system, removing jitter from hand held camera videos while preserving the smooth camera motion. The stabilization was achieved using global flow to compute affine transforms between frames and low-pass filtered to allow smooth camera motion. Experimented with sparse vs dense flow, and affine vs perspective transformation.
Shape from Angle Regularity
June 1, 2012 – August 1, 2014
Urban architecture is characterized by a profusion of straight lines that meet orthogonally. This simple observation leads to a flexible definition of the man-made world and the distortion in these regular angles serves as a new shape-from-X cue. This project formulates the idea and explores its applications in various scenarios in detail.
Cultural Fit Analysis
The candidate's experience is heavily focused on Deep Learning and Computer Vision, which is a strong technical area. However, the target role is 'Data Analyst'. While there's a foundation in data processing through their technical work, the direct alignment with typical data analyst responsibilities (e.g., SQL, BI tools, statistical modeling for business insights) is not explicitly demonstrated. The projects are primarily research-oriented and lack explicit business or product impact metrics often associated with data analyst roles. This suggests a potential mismatch in the day-to-day responsibilities and focus compared to a traditional Data Analyst role.
Soft Skills & Operational Fit
The provided data does not contain sufficient information to assess soft skills or operational fit beyond the technical descriptions of projects and roles. No psychometric test results are available.