Google AI Research Week Scholar 2022 (Computer Vision Track)

Mastering LLMs: A Conference For Developers & Data Scientists

Bachelor of Technology (B.Tech.)

Master of Technology - MTech

Software Engineer - Machine Learning

◦ Camera calibration of stereo camera setup.
◦ Developed 3D maps of Anterior Segment of the eye.

Winter Research Internship

◦ Objective: 3D mapping of the indoor environment.
◦ Mainly Worked on the 3D mapping of an indoor environment using Microsoft Kinect in ROS.
◦ I also designed a 6-DOF Robotic Arm using SolidWorks.

Summer Research Internship

• Developed the new algorithm for camera calibration in omnistereo cameras end to end.
• We considered each camera as virtual camera which is present at a tangent to the imaginary circle of the field of view.
• This algorithm can be adapted to any field of view of the camera.

Applied Computer Vision Intern

◦ Worked on pedestrian tracking using mean shift tracking and background subtraction algorithms.
◦ Object detection using HOG features and Random forest.
◦ Complete the literature survey of object detection using non-deep learning methods.

• Architected the whole problem from data collection to model training.
• Developed a new small-size dataset of malnourished and healthy children using images from the internet with help of doctors.
• Developed deep learning-based algorithm to work on small size dataset for malnourishment detection.
• Designed a 3D printing model of cradle to collect image dataset of newborn babies at the hospital.

Graduate Student Researcher

Face Detection
• Architected face detection on edge devices, end to end.
• Enabled face detection with mask
• Improved mAP score from 54% to 76%.
• Deployed model on Qualcomm SNPE, which is serving on 20K trucks/cars worldwide.
Head Pose Estimation
• Developed pose estimation for faces with mask.
• Use of contrastive loss brought down the MAE from 15.7 to 9.4.
• Architected the uncertainty-based pose estimation algorithm to reduce the falses.
• Uncertainty-based pose estimation reduced the falses by 15%.
Other Responsibilities
• Collaborating with IIIT Delhi for federated learning project on Edge Devices.
• Took interviews of more than 20 candidates in the machine learning domain.

Senior Software Engineer - Machine Learning

Staff Machine Learning Engineer

Experienced ML Engineer

Know about Ashish Sethi's work experience, education, bootcamps, certifications, and much more. Ashish Sethi