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education

Highest Academic Performance Award(2018), High Honor(2015,2016,2017,2018)

MS Thesis: Pose and Camera Invariant Person Reidentification and Attribute Recognition

Topic: Active Learning for Self-Driving Cars: An Intelligent Learning Strategy for Data-Efficient Development of Autonomous Driving

Researched image processing algorithms for registration and segmentation to get familiarized with medical image processing concepts and tools used for building projects.
Created a user-interface plugin for MITK using C++ for medical image processing algorithms designed for use of health-care professionals working on healthcare data in detection of deformations.
Deployed the applications in Docker containers to eliminate environment dependency problem.

Designed a new person re-identification model to improve performance of the model in-use by +16% by proposing a part-based generator network to generate more realistic looking pedestrian images.
Organized experiments in PyTorch to compare performance and image quality that resulted in reduction in complexity of the backbone network which doubled the speed at test time.
Participated in entire R&D cycle: coding the network, data loading, training in a Nvidia-Docker container, conversion to a deployable TensorRT Caffe model, integrating with surveillance system in C++.

Researched Active Learning methods for data-efficient model development. Researched method helped 2D Object Detection deep learning model achieve peak performance with 30% less training data.
Constructed an Active Learning based pipeline consisting of data selection, training dataset creation and model training that helped engineers spend less time on data preparation processes and more on model development.
Worked on training and implementation of multiple autonomous driving perception models including: 2D Object Detection, Lane Detection, Point Cloud Segmentation, Perception Fusion and Motion Prediction.
Deployed various neural network models using TensorRT and C++ 11 for faster inference running on NVIDIA DRIVE AGX and ROS.
Participated in design and decision making process of full perception architecture for a self-driving car.

Conducted research at BMW while also working towards a Doctoral Degree from Technical University Munich
Developed Active Learning methods for Autonomous Driving tasks to increase efficiency of data selection process
Collaborated with other PhD students in the team and supervise master thesis students