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Hi, I'm Muhammad Talha! ๐Ÿ‘‹

Robotics, Electronics, Software, and Data; not a jack of all trades but as an all-rounder, I have come a long way. I have earned my Master's in Robotics from Columbus State University, Georgia, US, and completed my undergraduate degree in Mechatronics at KIET.

๐Ÿš€ About Me

  • Summer Intern at Georgia Institute of Technology.
  • Robotics Research student at Columbus State University.
  • Graduated with a Bachelors degree in Mechatronics Engineering.
  • Loves building and development of robust software systems
  • Interests - Software Developer | Machine learning | Computer Vision | Autonomous systems .
  • Contributing Writer, Medium: https://medium.com/@talha.ej10
  • How to reach me [email protected] or [email protected] ๐Ÿ“ซ
  • USA | Pakistan

Education

  • Completed a Masters of Science in Robotics Engineering at Columbus State University with a commendable CGPA of 3.8/4.0.
  • Relevant coursework includes subjects such as Computer Vision, Artificial Intelligence, Kinematics, Evolutionary Computation, and Robotics System Design.
  • Earned a Bachelor of Engineering in Mechatronics from PAF-Karachi Institute Of Economic Technology with a CGPA of 3.1/4.0.
  • Undergraduate coursework covered a broad range of topics, including Mechatronics System Design, Industrial Control Automation, Linear Control Systems, Introduction to Robotics and Computer Programming, and more.

Employment History:

  • As a Summer Research Intern at Georgia Institute of Technology, I collaborated on web and app development, focusing on system integration and Azure-based web applications with GIS functionality.
  • As a Graduate Teaching Assistant at Columbus State University, I provided academic support, directed undergraduate students, and assisted in hardware and software concepts.
  • As a Artificial Intelligence Engineer at Motiventive worked on Mood Analytics project.
  • As a Trainee Engineer at Yunus Textile Mills Ltd., you were involved in project management, cost control, and weekly reporting.

Research Publications:

Projects:

Skills:

  • Proficient in programming languages such as Python, C/C++, SQL, Java, and LATEX.
  • Familiar with various IDEs, including IntelliJ, Jupyter, Spyder, PyCharm, and more.
  • Skilled in using tools like ROS, Gazebo, Rviz, Mission Planner, and Git.
  • Deep learning expertise includes Tensorflow, Pytorch, Keras, and more.
  • Experienced with operating systems like Windows, Ubuntu, Linux, Raspbian, Mac.

Awards and Achievements:

  • Received a fully funded merit-based scholarship for a master's program.
  • Served as a Robot Inspector at the FIRST Robotics Competition.
  • Selected for an online AI Joint Lab Program in Autonomous Driving led by Koรง University, Turkey in 2020.

Professional Training:

  • Completed various professional courses, including Visual Perception for Self-Driving Cars, AI for Medical Diagnosis, and Apache Spark With Scala Hands On with Big Data.

๐Ÿ”— Links

portfolio linkedin twitter youtube instagram

Coding

talhaejazh

Connect with me:

talha-ejaz-hussain muhammad talha ejaz talhaejazhu talhaejazh

Languages and Tools:

android aws blender c flask gcp illustrator matlab mysql opencv pandas python pytorch scikit_learn seaborn tensorflow

talhaejazh

ย talhaejazh

talhaejazh

Muhammad Talha Ejaz's Projects

indoor-localization-using-6-axis-imu-sensor-python icon indoor-localization-using-6-axis-imu-sensor-python

In this repository, we developed an IMU-based indoor localization system using the GY-521 module, which has both a gyroscope and an accelerometer. The first phase of the project involved capturing raw data, and the second phase involved plotting and implementing three filters: Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter

mlforecast icon mlforecast

Scalable machine ๐Ÿค– learning for time series forecasting.

self-driving-car-using-deep-learning-quanser-qcar- icon self-driving-car-using-deep-learning-quanser-qcar-

A small prototype of the self-driving car using a Convolutional Neural Network. This is my course project to implement an end-to-end method for training convolutional neural networks for the autonomous navigation of a mobile robot. The proposed navigation system shows object detection. The vehicle it is used can directly output the linear velocity of the robot from an input image in a single step. The trained model gives wheel velocities for navigation as outputs in real-time making it possible to be implanted on mobile robots such as robotic vacuum cleaners. The experimental results show an average linear velocity with a maximum turning angle is 30 degrees. The proposed model built in python 3.0 and experimental tests based on small scale car Quanser latest self-driving car (QCar) state-of-the-art product equipped with Jetson TX2 has been conducted to verify the effectiveness of the proposed network.

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