Mohamed Ahmed Eldemardash Zahran

• As part of Genius Sports acquisition to Spirable August 2021, I become a senior machine learning engineer at Genius Sports.
• Co-operated with cross-functional teams of data scientists, user researchers, product managers, designers, and engineers.
• Designed and implemented machine learning and deep learning pipelines
• Performed analysis on large sets of data to extract impactful insights that will help drive creative video decisions
• Mentored junior engineers and data scientist

• Owned the two schools technically, leading a geographically dispersed team of mentors and reviewers
• Used Data-Driven Decision-Making techniques, help scale and improve Udacity products
• Collaborated with business and technical stakeholders through the end-to-end data science process and represented Udacity technically in clients meetings
• Acted as a solution architect for Udacity products related to Governments by analyzing, creating, reviewing the solution developed

Responsibilities:

• Worked under the supervision of Dr. Mohamed Elhoseiny and Dr. Li Erran Li to solve future prediction using Variational autoencoders and graph neural networks
• Conducted state of the art research in machine learning and deep learning application to autonomous driving.
• Writes/reviews new publications.
• Innovated solutions for motion forecasting in self-driving cars
• Used Reinforcement learning to solve real world problems

Achievement:

• One paper accepted at ICLR 2021
• One paper accepted at CoRL 2021

Responsibilities:

• Using machine learning and computer vision to build the next generation of video and image studios, automatically generating 2D and 3D content.
• Collected and Identified business and technical requirements
• Developed and defined high-level product specifications and all aspects of development.
• Communicated all concepts and guidelines to development team
• Conducted code and architecture review
• Mentored junior engineers and data scientist
• Achievements:
• Simultaneously managed content and stakeholders for multiple content generation and computer vision projects.
• Researched, designed, and implemented a versatile production-ready content generation toolkit using GANs.
• Successfully tested the product across different verticals like security, real estate planning, and e-commerce.

Responsibilities:

• Lead a team of five engineers to innovate and implement new use cases for Yonohub.
• Help Automotive, machine learning, and robotics developers to be successful in Yonohub.
• Be customer zero for our products, provide internal and external feedback from the field to our engineering teams to influence product direction.
• Manage time-sensitive projects and programs from conception to execution.
• Acted as application architect owning requirements and architectural review.

Achievement:

• 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.

Responsibilities:

• Writes/reviews new publications/patents.
• Conducted state of the art research in machine learning and deep learning application to autonomous driving.
• Implemented and prototyped research ideas on real hardware and embedded systems.
• Innovated solutions for self driving cars problems.
• Published research papers/ in the area of deep learning and autonomous driving.

Achievements:

• Published seven papers in top tier conferences focusing on 3D object detection from LiDAR point cloud and semantic segmentation.
• Implemented, Improved and integrated Automatic Annotation pipeline into Valeo platforms.
• 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.
• Deployment of deep learning models to embedded devices.

Responsibilities:

• Co-Founded the first deep learning startup in Egypt for democratizing deep learning in the industry.

Achievements:

• Lead the negotiation and planning with industry partners to implement tailored deep learning solutions.
• Integrated deep learning solutions into customers' machines.
• Customized object detection and classification models for the food processing industry.
• Increased Sorting machines accuracy by 40%.
• Slashed operational cost of our customer by 10 Million Egyptian pounds.
• Performed exploratory data analysis to collect and prepare customer data.