Aamer Zaheer
Computer Vision Architect at Apple
Computer Vision Architect at Apple
Apple
https://www.linkedin.com/company/apple
Sunnyvale, California, United States
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Computer Vision Architect
KeepTruckin Inc.
Over the past 2.5 years, my teams have been responsible for building all critical event detection products for safety monitoring at KeepTruckin. This includes Driver Performance events like Hard Acceleration / Braking / Cornering, Crash Detection, Tailgating, Unsafe Lane Change, Driver Distraction etc.

I built 3 teams from scratch including:
- Vision: 8 people team responsible for the manual video annotation product as well as state of the art ML/Vision algorithms for understanding road-facing and driver-facing videos.
- Motion: 7 people team responsible for detecting unsafe behaviors on our ELD using motion sensors from the vehicle, GPS and IMU. Built the product to deliver high 90s Recall.
- Annotation: A 40+ people manual annotation team responsible for real-time labeling video data for product as well as ML algorithms.
San Francisco Bay Area
Head of Research and Development (Computer Vision, Machine Learning, Robotics, Data Science)
University of Central Punjab
Nearly half the BS students drop out or change majors by the end of their freshmen year in even the technologically advanced countries. The ratio was nearing 60% at UCP. A major culprit, as described by students and evident by their grades in Calculus 1.

I lead a team of Mathematics and Computer Science teachers in a complete overhaul and redesign of the course. To my utter disbelief, we were able to bring freshmen dropout rates from 60% down to 14% in one year while only one, out of 461, students dropped out during/after Calculus.

We achieved this by redesigning the pedagogy, initially focusing on unlearning bad attitudes towards maths, motivating students with cutting edge problems in Computer Science, prioritizing conceptual learning over mathematical rigor & memorization, using technology to help in class through function visualizations and google search for learning new concepts, using technology to keep track of each student's progress every week, offering cutting edge projects that relied on modeling a real world problem through calculus and verifying the model via programming, and most importantly, providing one-on-one counseling through regular viva sessions.

Most of the credit goes to Dr. Ashraf Iqbal for instigating, guiding, inspiring and, at times, providing unreasonable support above and beyond you would expect at a University. Equally important was the role of my team especially Sarfraz Raza who helped us design, grade & viva the programming projects - it was his method of one-on-one help that eventually worked in achieving learning objectives for over 80% of the students. Moreover Saba Naeem and Amanullah Kariapar were instrumental with the sheer amount of time they spent working on this. All of my team made the time at UCP unbelievably effective and worthwhile.
Lahore, Pakistan
Lecturer
Ingrain.io
Ingrain.io is a revolutionary platform for real-time placement of products and Native Advertising into online video using our proprietary SceneSense technology. Unlike other digital product placement companies that basically "photoshop" an ad into a video, we deliver ads that are dynamically targeted to individual viewers in real-time.

At Ingrain.io, although I'm officially the CTO but practically the Getting-Things-Done officer. In addition to charting out the overall technology plan as well as processes, I have worked on the initial product-market feasibility, customer development, fund-raising and business development among other things. These days I'm mainly responsible for Product Management and Computer Vision algorithms.
Palo Alto, CA
CTO & Co-founder
Groopic Inc
https://www.linkedin.com/company/eyedeus-labs
At groopic I was leading the technology & algorithm development, prototyping new technologies, responsible for product ideas & their technical as well as market feasibility. As a first time entrepreneur, I got to learn a lot of new things and wear all the possible hats from Fund-raising and managing Social Media to Hardcore Technology and User Experience design.

The biggest reward of working at groopic was helping nearly a Million user with a unique solution that simply came from our wild imagination.
Palo Alto, CA
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CTO & Co-Founder
Lahore University of Management Sciences
https://www.linkedin.com/company/lahore-university-of-management-sciences
Thesis titled "Shape from Geometric Regularities" deals with 3D reconstruction from a single image and motion capture using regularities in the 3D structure.

I was a full time PhD student from 2006-2013. Did the thesis defense after a break for the startup - in 2018.
Lahore, Pakistan
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PhD (Computer Vision)
CVLab LUMS
Lahore University of Management Sciences, Lahore
Research Assistant
PhD Computer Science
Computer Vision
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Lahore University of Management Sciences
https://www.linkedin.com/school/lahore-university-of-management-sciences/
Master of Science (MS)
Computer Science
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Lahore University of Management Sciences
https://www.linkedin.com/school/lahore-university-of-management-sciences/
Bachelor of Science (BS)
Computer Science
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International Islamic University
https://www.linkedin.com/school/international-islamic-university/
This paper proposes Automatic Metric Rectification of projectively distorted 3D structures for man-made scenes using Angle Regularity. Man-made scenes, such as buildings, are characterized by a profusion of mutually orthogonal planes and lines. Assuming the availability of planar segmentation, we search for the rectifying 3D homography which maximizes the number of orthogonal plane-pairs in the structure. We formulate the orthogonality constraints in terms of the Absolute Dual Quadric (ADQ). Using RANSAC, we first estimate the ADQ which maximizes the number of planes meeting at right angles. A rectifying homography recovered from the ADQ is then used as an initial guess for nonlinear refinement. Quantitative experiments show that the method is highly robust to the amount of projective distortion, the number of outliers (i.e. non-orthogonal planes) and noise in structure recovery. Unlike previous literature, this method does not rely on any knowledge of the cameras or images, and no global model, such as Manhattan World, is imposed.
3D Metric Rectification using Angle Regularity
WACV
In this paper we show that typical nonrigid structure can
often be approximated well as locally rigid sub-structures
in time and space. Specifically, we assume that: 1) the
structure can be approximated as rigid in a short local time
window and 2) some point- pairs stay relatively rigid in
space, maintaining a fixed distance between them during
the sequence. First, we use the triangulation constraints
in rigid SfM over a sliding time window to get an initial
estimate of the nonrigid 3D structure. Then we automat-
ically identify relatively rigid point-pairs in this structure,
and use their length-constancy simultaneously with trian-
gulation constraints to refine the structure estimate. Lo-
cal factorization inherently handles small camera motion,
short sequences and significant natural occlusions grace-
fully, performing better than nonrigid factorization meth-
ods. We show more stable and accurate results as compared
to the state-of-the art on even short sequences starting from
15 frames only, containing camera rotations as small as 2◦
and up to 50% contiguous missing data.
NRSfM using Local Rigidity
WACV
Research Project done at Lahore University of Management Sciences.

Abstract:
Most nonrigid objects exhibit temporal regularities in
their deformations. Recently it was proposed that these regularities
can be parameterized by assuming that the nonrigid
structure lies in a small dimensional trajectory space.
In this paper, we propose a factorization approach for 3D
reconstruction from multiple static cameras under the compact
trajectory subspace representation. Proposed factorization
is analogous to rank-3 factorization of rigid structure
from motion problem, in transfromed space. The benefit
of our approach is that the 3D trajectory basis can be
directly learned from the image observations. This also allows
us to impute missing observations and denoise tracking
errors without explicit estimation of the 3D structure.
In contrast to standard triangulation based methods which
require points to be visible in at least two cameras, our approach
can reconstruct points, which remain occluded even
in all the cameras for quite a long time. This makes our solution
especially suitable for occlusion handling in motion
capture systems. We demonstrate robustness of our method
on challenging real and synthetic scenarios.
Multiview Structure from Motion in Trajectory Space
IEEE ICCV
http://cvlab.lums.edu.pk/MultiviewNRSFM/index.html
This paper deals with automatic Single View Reconstruction (SVR) of multi-planar scenes characterized by a profusion of straight lines and mutually orthogonal line-pairs. We provide a new shape-from-X constraint based on this regularity of angles between line-pairs in man-made scenes. Unlike earlier literature, our approach does not make restrictive assumptions about the orientation of the planes or the camera and works for both indoor and outdoor scenes.
Shape From Angle Regularity
European Conference on Computer Vision, 2012
http://link.springer.com/chapter/10.1007/978-3-642-33783-3_1
For further details, see my paper 

Zaheer et al, "Multiview Structure from Motion in Trajectory Space", ICCV 2011

Rehan et al, "NRSfM using Local Rigidity", WACV 2014
Motion Capture
http://cvlab.lums.edu.pk/MultiviewNRSFM
For further details, see my papers:

Zaheer, Rashid and Khan, "Shape from Angle Regularity",  ECCV 2012
Zaheer and Khan, "3D Metric Rectification using Angle Regularity", WACV 2014
Shape from Angle Regularity
http://cvlab.lums.edu.pk/SfAR
Research Project  / Publication . An investigation into exploiting trajectory based constraints for 3D reconstruction in motion capture systems with fewer number of cameras and better noise and missing data handling.
Multiview Structure From Motion in Trajectory Space
http://cvlab.lums.edu.pk/MultiviewNRSFM/index.html
https://il.linkedin.com/in/omri-yosub-5361913a
Israel
Omri Yosub
Algorithm Engineer (Computer Vision and Deep Learning ) at Mobileye
https://il.linkedin.com/in/adam-kelder-572b5b8b
Southern, Israel
Adam Kelder
Computer Vision Researcher
https://pk.linkedin.com/in/abdul-rafey-b7142849
Pakistan
Abdul Rafey
Research Engineer at KeepTruckin
https://www.linkedin.com/in/muhammad-ahmed-riaz-08a41237
San Francisco Bay Area
Muhammad Ahmed Riaz
Deep Learning & Computer Vision at Apple
https://pk.linkedin.com/in/mohsen-ali-5666394
Pakistan
Mohsen Ali
Assistant Professor at Information Technology University
https://sa.linkedin.com/in/drsohaibkhan
Saudi Arabia
Sohaib Khan
CEO at Hazen.ai
https://pk.linkedin.com/in/afsheen-rafaqat-ali-028b47124
Lahore District
Afsheen Rafaqat Ali
Senior Research Engineer
https://pk.linkedin.com/in/alihassan12
Lahore District
Ali Hassan
Engineering Manager – Computer Vision and AI at KeepTruckin
https://www.linkedin.com/in/mohammad-haris-baig-a9358637
San Jose, CA
Mohammad Haris Baig
Deep Learning Engineer at Apple
https://www.linkedin.com/in/abdul-rafay-khalid-a9505854
Greater Seattle Area
Abdul Rafay Khalid
Machine Learning Engineer at Apple
https://www.linkedin.com/in/alirehans
San Francisco Bay Area
Ali Rehan
Engineering Manager, AI/Vision Products at KeepTruckin
https://www.linkedin.com/in/fareeha-irfan-81599a111
Seattle, WA
Fareeha Irfan
Machine Learning Engineer @ Apple | Google Lime Scholar | Google Grace Hopper Scholar | Inclusion & Diversity @ Apple
https://il.linkedin.com/in/elad-talbi
Haifa, Israel
Elad Talbi
Computer Vision & Deep Learning Researcher at Elbit Systems
https://pk.linkedin.com/in/mohsin-ali-sheikh
Pakistan
Mohsin Ali
PhD student at Lahore University of Management Sciences
https://www.linkedin.com/in/nainaraisinghani
San Francisco, CA
Naina Raisinghani
Product Manager at Google
https://www.linkedin.com/in/anumsarmadmalik
Greater Seattle Area
Anum Malik
Software Engineer | Apple Siri
https://www.linkedin.com/in/danish-shahzad
Cupertino, CA
Danish Shahzad
Hardware Engineer at Apple
https://www.linkedin.com/in/andy-xiaolong-wang-0292aa27
Mountain View, CA
Andy (Xiaolong) Wang
Head of Deep Learning R&D Team
https://www.linkedin.com/in/liangjiehong
Sunnyvale, CA
Liangjie Hong
Director of Engineering, AI at LinkedIn
https://www.linkedin.com/in/parmidab
San Francisco, CA
Parmida Beigi
Machine Learning @ Alexa AI | EdTech, Explainable AI, Self Supervised ML
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United Arab Emirates
aamer zaheer
employee at government
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Pakistan
Aamer Muhammad Zaheer
--
LUMS Alumni in U.S.A.
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Computer Vision Technologies
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