This is Mohamed Zahran CV AI | Machine Learning | Deep learning | Computer Vision | Autonomous Cars | Data Scientist | NLP

1. Mohamed Zahran
Machine Learning Research Engineer
Phone +201119986552
E-mail moh3th1@gmail.com
Google Scholar http://bit.ly/36wSnsq
LinkedIn www.linkedin.com/in/mzahran001/
GitHub https://github.com/mzahran001
Experience
2019-09 - present Program Experience manager - Data & AI Schools
Udacity
Program Experience manager for Data and AI schools.
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Owned the two schools technically, leading a geographically dispersed team of mentors and reviewers.
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Used Data-Driven Decision-Making techniques, help scale and improve Udacity products.
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Collaborated with business and technical stakeholders through the end-to-end data science process and represented Udacity
technically in clients meetings
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2020-10 - present Senior Machine learning Engineer
Spirable
Co-operated with cross-functional teams of data scientists, user researchers, product managers, designers and engineers
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Designed and implemented machine learning and deep learning pipelines
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Performed analysis on large sets of data to extract impactful insights that will help drive video creative decisions
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Mentored junior engineers and data scientist
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2019-12 - present Independent Researcher
KAUST: King Abdullah University of Science and Technology
Responsibilities:
Achievement:
Worked under the supervision of Dr. Mohamed Elhoseiny and Dr. Li Erran Li to solve future prediction using Variational
autoencoders and graph neural networks.
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Conducted state of the art research in machine learning and deep learning application to autonomous driving.
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Writes/reviews new publications.
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Innovated solutions for motion forecasting in self-driving cars
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Used Reinforcement learning to solve real world problems
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One paper accepted at ICLR 2021
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Two papers under review at NeurIPS 2021
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2020-02 - present Yonohub Advocates Team Lead
Avelabs
Responsibilities:
Achievement:
Lead a team of five engineers to innovate and implement new use cases for Yonohub.
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Help Automotive, machine learning, and robotics developers to be successful in Yonohub.
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Be customer zero for our products, provide internal and external feedback from the field to our engineering teams to influence
product direction.
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Manage time-sensitive projects and programs from conception to execution.
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Built and managed many use cases of Yonohub in the robotics and the automotive industry using ROS, Autoware, simulators
(Carla, CarMaker, Gazebo, AirSim) and machine learning.
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2018-12 - present Senior Machine learning Research Engineer
WebVille
Responsibilities:
Achievements:
Using machine learning and computer vision to build the next generation of video and image studios, automatically generating 2D
and 3D content.
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Simultaneously managed content and stakeholders for multiple content generation and computer vision projects.
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Researched, designed, and implemented a versatile production-ready content generation toolkit using GANs.
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Successfully tested the product across different verticals like security, real estate planning, and e-commerce.
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2017-06 - 2019-09 Deep Learning Researcher
Valeo
Responsibilities:
Achievements:
Writes/reviews new publications/patents.
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Conducted state of the art research in machine learning and deep learning application to autonomous driving.
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Implemented and prototyped research ideas on real hardware and embedded systems.
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Innovated solutions for self driving cars problems.
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Published research papers/ in the area of deep learning and autonomous driving.
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Published seven papers in top tier conferences focusing on 3D object detection from LiDar point cloud and semantic
segmentation.
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Implemented, Improved and integrated Automatic Annotation pipeline into Valeo platforms.
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Researched and Implemented deep learning models for Generative adversarial networks (GANs), Automatic Annotation, Active
learning, depth prediction, object detection, semantic segmentation, tracking, and sensor fusion on the available data from Valeo
sensors.
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Deployment of deep learning models to embedded devices.
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2016-09 - 2017-05 Co-Founder
Deep Learning startup

2. Responsibilities:
Achievements:
Co-Founded the first deep learning startup in Egypt for democratizing deep learning in the industry.
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Lead the negotiation and planning with industry partners to implement tailored deep learning solutions.
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Integrated deep learning solutions into customers' machines.
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Customized object detection and classification models for the food processing industry.
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Increased Sorting machines accuracy by 40%.
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Slashed operational cost of our customer by 10 Million Egyptian pounds.
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Performed exploratory data analysis to collect and prepare customer data.
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Education
2018-09 - present M.Sc. in Communication and Information Technology, School of Information Technology and
Computer Science, Nile University
Coursework: Big data, Scientific computing
Projects: - Numerical library: Developed a C++ library that implements a collection of numerical techniques for scientific computing
like Gauss-Seidel, cubic spline interpolation and etc...
2010-09 - 2016-07 B.Sc. in Communication and Electronics Engineering, Faculty of Engineering, Tanta University
Coursework: Communication systems, Electronics, Waves, Satellite communication, Mobile Communication, Networks and
Security.
Graduation Project: Human Follower Robot: Designed and implemented a fully Autonomous Robot prototype that follows a human
and avoid obstacles using wearable device.
Skills
- Programming Languages: Python, C++, and Matlab
- Python libraries: Numpy, Scipy, Pandas, and OpenCV
- Machine learning Frameworks: TensorFlow, Pytorch, Keras and Scikit-learn
- Deep Learning, Machine Learning and Computer vision Algorithms: Generative adversarial networks (GANs), Long Short
term Memory (LSTM), Reinforcement learning, Regression Analysis, GMDH, Automatic Annotation, Active learning, depth
prediction, 3D reconstruction, Classification, object detection (2d and 3D), semantic segmentation (2d and 3D), tracking, and
sensor fusion, Reinforcement learning
- Big Data: Hadoop, Hive and Spark
- Software engineering practices and tools: Version control (Git, DVC), Jira, ZenDesk, CI/CD, Agile development, AWS EC2,
S3, Docker
- Embedded systems (AVR & ARM).
Publications
2021-03 HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents (ICLR 2021)
2019-10 Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation (ML4AD workshop NeurIPS 2019)
2019-06 End-to-end sensor modeling for LiDAR Point Cloud (ITSC 2019)
2019-05 LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving (AI4AD workshop ICML 2019)
2018-12 YOLO4D: A Spatio-temporal Approach for Real-time Multi-object Detection and Classification from LiDAR Point Clouds
(MLITS workshop NeurIPS 2018)
2018-06 YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud (ECCV workshop
2018).
2018-05 MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving (ITSC 2018)
2017-12 Motion and Appearance Based Multi-Task Learning Network for Autonomous Driving (MLITS workshop NeurIPS 2017).
Patents
Training local artificial neural networks
Filed Mar 2, 2020 de 10 2020 105 485.8
Multi-Sensor fusion for Radar Cocoon object detection
Filed Oct 10, 2019. de 10 2019 127 283.1
3D Object Bounding Box Detection from Lidar pointcloud
Filed Oct 10, 2019. de 10 2019 127 306.4
System and method for analyzing a three-dimensional environment represented by a point cloud through deep learning
Filed Nov 14, 2018 . de 10 2018 128 531.0
Courses and Trainings
- Certified Scrum Product Owner (Scrum Alliance)
- Algorithms and data structure Nanodegree (Udacity)
- Convolutional Neural Networks for Visual Recognition, CS231n (Stanford University via official website)
- Deep Learning Specialization (Deeplearning.ai via Coursera)
- Deep Learning with TensorFlow (Google via Udacity)
- Machine learning, Andrew Ng

3. - Big Data 101 ( IBM via Cognitive Class)
- Programming for Everybody (University of Michigan via Coursera)
- Emerging Technologies: From Smartphones to IoT to Big Data Specialization (Yonsei University via Coursera)
- Design Thinking Process from Hasso Plattner Institution (HPI)
Additional Experience
2020-06 - 2020-09 Maxis Communications, Universiti Teknologi Malaysia (UTM), Aerodyne Group
Part Time Deep learning Engineer
A collaboration project between Maxis Communications, Universiti Teknologi Malaysia (UTM), and Aerodyne Group to build a
predictive maintenance system for telecommunication towers in Malaysia using 3D object detection and segmentation using
Multiple camera and Lidar.
2020-05 - 2020-07 InfraLytiks
Part Time Deep learning Engineer
A collaboration project between InfraLytiks and the USA government to detect and record the intersections between the trees and
the powerlines to help the government officials in preventing the firestorms.
2019-10 - 2019-12 NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving
Program Committee
As a member of the workshop program committee, I had to review some of the submitted papers (ml4ad.github.io).
2019-02 - 2019-06 Phoenix LiDAR Systems
Part Time Deep learning Engineer
Designed and built Power Lines Segmentation from Aerial LiDAR scans.
2018 - 2019 Smart Iot Systems
Part Time Deep learning Engineer
- Hajj Medical Health platform: Lead and contributed the development of medical health monitoring system to track the health
status of the Hajj when visiting Mecca. We build a prediction and classification model applied to the ECG and EMG signals from low
cost sensors helping the government to take fast actions. The system consist of the sensors wired to embedded microcontroller
which perform basic machine learning functions in real time giving the Hajj a live status about his/her health. The data is
synchronized with cloud at periodic intervals which performs in-depth analysis for the signals.
- Real Estate Chatbot: Analyzed and build a customized chatbot for Egyptian real estate industry. The chatbot is connected with
basic functions in the house like light and air conditioner.
- Mobile Application for children with special needs: Contributed to the development of a specialized android application for the
children with special needs by developing a deep learning model that can recognize and identify a doodle drawn by the child and
pronounce it. The goal of the app is to help the doctors understand the psychological status of the child using these drawings.
- Diabetic Retinopathy Classification: Build deep learning system to help ophthalmologist recognize and identify Diabetic
Retinopathy using cheap cameras.
- Adaptive personalized e-Learning System: Contributed to the development of an adaptive learning system that's learns the
student learning style using reinforcement learning models.
2017-08 - 2018-01 Adam.ai
Part Time Deep learning Engineer
- Researched and designed a toolkit for Intelligent meeting management using machine learning
- Meetings Robot: Contributed to the development of a meeting robot that mentors any meeting, counts people, identify them and
finally takes "Meeting Minutes"
Awards
2017 - Cairo Innovates: Won third place in International Cairo Exhibition of Innovation based on our proposal for solving the okra
infection problem.
2016 - Yomken innovation challenge Award: Won (10,000$) reward for designing and implementing okra sorting machine software
prototype.
Languages
- Arabic - Native
- English - Fluent