Azwad Sabik
Data AnalystCV/ML @ Matterport
Matterport
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
CV/ML @ Matterport
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Learning about learning.
The Johns Hopkins University
Master’s Degree, Robotics
January 1, 2015 – January 1, 2016
The Johns Hopkins University
Bachelor of Science (BS), Biomedical/Medical Engineering
January 1, 2011 – January 1, 2015
Matterport
Engineering Manager, Computer Vision & Machine Learning
October 1, 2025 – Present
Matterport
Senior Staff Deep Learning Engineer
January 1, 2025 – October 1, 2025
Matterport
Staff Deep Learning Engineer
June 1, 2021 – January 1, 2025
Matterport
Senior Deep Learning Engineer
June 1, 2020 – June 1, 2021
Matterport
Senior Deep Learning Engineer
December 1, 2018 – April 1, 2020
San Francisco Bay Area
Huawei Technologies
Software Engineer - Artificial Intelligence
October 1, 2016 – December 1, 2018
San Francisco Bay Area
Clear Guide Medical
Software Engineering Intern
June 1, 2015 – August 1, 2015
Baltimore, Maryland
Tsinghua-Johns Hopkins Joint Center for Biomedical Engineering Research
Research Intern
June 1, 2014 – August 1, 2014
Tsinghua University in Beijing, China
NASA Ames Research Center - Advanced Controls and Displays Laboratory
Apprentice - National Space and Biomedical Research Institute
June 1, 2013 – August 1, 2013
Mountain View, California
Loma Linda University Medical Center
Research Intern - Radiation Medicine
June 1, 2012 – August 1, 2012
Loma Linda, California
Deep Learning for Image Understanding
May 1, 2016 – Present
Fine-tuned and validated modified version of AlexNet in Caffe framework to perform 1000-class classification of pill images, achieving 56.1% test accuracy. Utilized a dataset of 2000 "reference" quality images plus 5000 "consumer" quality images and performed fine-tuning on multi-GPU system. Project idea drawn from NIH Pill Recognition challenge.
Learning Theory
May 1, 2016 – Present
Simulated human gaze as coordinated motion of eyes and head in one dimension driven by optimal-feedback control. Performed motion planning through iterative computation of optimal inputs (as gains via Bellman equation) and state estimates (as Kalman gains).
Robot Systems Programming
April 1, 2016 – May 1, 2016
Implemented a ROS package enabling autonomous navigation and landing of quadrotor at target location within drone cage fitted with grid of AR tags. Performed sensor fusion between readings from onboard cameras, IMU, and ultrasonic rangefinder (altimter) to achieve odometry and state estimation. Utilized ROS packages for efficient kinematic transformations and AR tag detection; developed system in Gazebo simulation prior to testing.
Robot Devices, Kinematics, Dynamics and Control
December 1, 2015 – Present
Developed system to reproduce GUI-input line-art on whiteboard using joint level control of low-cost UR5 robot arm. Two modes of operation were developed: one utilizing inverse kinematics to map workspace positions to joint-space states, and another utilizing differential kinematics to map workspace errors to joint-space velocities. System was developed in MATLAB.
Representation Learning
October 1, 2015 – December 1, 2015
Developed pipeline to generate user-specific recommendations for character selection and gameplay behaviours for distinct temporal segments of MOBA matches. Utilized techniques of unsupervised learning (automatic feature extraction and dimensionality reduction) to allow effective identification of distinct playstyle clusters in professional League of Legends (LoL) matches. Principal components analysis (PCA), kernel PCA, locally linear embeddings, and clustering via affinity propagation were explored for the purpose of learning playstyles. Partial least squares regression was utilized to generate recommendations.
Foundations of Computational Biology and Bioinformatics
March 1, 2015 – May 1, 2015
The goal of this project was functional characterization of disease-associated risk SNPs. Researched features of SNPs (single nucleotide polymorphisms) statistically associated with Crohn's Disease, Type I Diabetes, and Type II Diabetes. Focused on features potentially associating SNP with four particular functional changes: alteration in protein activity and stability; change in gene expression level; altered splicing; altered microRNA binding. Utilized UCSC genome browser and other on-line genetic databases to study features of risk SNPs. Developed knowledge-based classifier for predicting functional change given SNP, incorporating automatic lookup of the SNP's feature values.
Mechatronics
February 1, 2015 – May 1, 2015
Built 3-DOF autonomous vehicle to perform scavenging and delivery task while adhering to competition's vehicle cost and size constraints. Implemented controllers for embedded sensing (CMUcam5, IMU) and actuation (DC/servo motors) as well as high-level control by Arduino Mega. Won first place in course competition having developed vehicle most efficiently able to perform task.
Computer Integrated Surgery II
January 1, 2015 – May 1, 2015
Explored feasibility of assessing bloodflow from smartphone-quality video as a low-cost alternative to laser doppler imaging for chronic wound patients. Developed, trained, and refined support vector machine classifiers utilizing handcrafted features derived through eulerian analysis/spatiotemporal processing and identified as potentially relevant to bloodflow. Evaluated potential for Eulerian Video Magnification to improve classification results. Won award for best project in course competition.
Principles of Design of BME Instrumentation
November 1, 2014 – December 1, 2014
Developed 5-DOF controller utilizing head- and leg-motion input. Implemented utilizing accelerometer and magnetometer data collected via Arduino and communicated to computer using Python-based serial communication library; data was subsequently processed and interpreted on computer as mouse-and-keyboard input used for control of character in Minecraft.
Cultural Fit Analysis
The candidate's project history shows a strong inclination towards research and development, often in academic or early-stage company settings, and a focus on complex technical challenges. While the professional experience is in a corporate setting, the target role of 'Data Analyst' might not fully leverage the candidate's deep expertise in Computer Vision and Machine Learning engineering. The projects are highly technical and diverse, indicating a curious and driven individual. However, the direct alignment with a typical 'Data Analyst' role, which often emphasizes business intelligence, reporting, and statistical analysis over deep learning model development, is not immediately apparent from the provided data. The candidate's background is more aligned with a Machine Learning Engineer or Research Scientist role.
Soft Skills & Operational Fit
The candidate's career progression to Engineering Manager at Matterport suggests strong leadership, project management, and team collaboration skills. The detailed project descriptions indicate good problem-solving abilities and a structured approach to complex technical challenges. However, without specific assessment data on soft skills, this remains an inference.