ESP Biography



AKHILESH BALASINGAM, ESP Teacher




Major: EE/CS

College/Employer: Stanford

Year of Graduation: 2025

Picture of Akhilesh Balasingam

Brief Biographical Sketch:

Not Available.



Past Classes

  (Clicking a class title will bring you to the course's section of the corresponding course catalog)

C7964: Introduction to Computer Vision in Splash Spring 2024 (May. 18 - 19, 2024)
Computer vision is an exciting field that is changing the world we live in. It has numerous applications, from self-driving cars to facial recognition systems. In this course, we will explore non-machine learning forms of computer vision that are still widely used today. We will start by introducing the basics of image processing, including image transforms, image enhancements, and template matching. We will then move on to non-machine learning approaches to computer vision, such as edge detection using the Canny algorithm, and image filtering using techniques like Gaussian blur and median filtering. We will also cover image segmentation techniques, including thresholding and region growing. By the end of the course, students will have a solid understanding of non-machine learning forms of computer vision and will have developed the skills to apply these techniques to real-world problems. This course is ideal for students who have some programming experience, particularly in Python, but no prior knowledge of computer vision is required. Students should be comfortable with basic linear algebra and calculus concepts.


C7825: Introduction to Computer Vision in Splash Spring 2023 (May. 20 - 21, 2023)
Computer vision is an exciting field that is changing the world we live in. It has numerous applications, from self-driving cars to facial recognition systems. In this course, we will explore non-machine learning forms of computer vision that are still widely used today. We will start by introducing the basics of image processing, including image transforms, image enhancements, and template matching. We will then move on to non-machine learning approaches to computer vision, such as edge detection using the Canny algorithm, and image filtering using techniques like Gaussian blur and median filtering. We will also cover image segmentation techniques, including thresholding and region growing. By the end of the course, students will have a solid understanding of non-machine learning forms of computer vision and will have developed the skills to apply these techniques to real-world problems. This course is ideal for students who have some programming experience, particularly in Python, but no prior knowledge of computer vision is required. Students should be comfortable with basic linear algebra and calculus concepts.