Krishnanjana Reddy

• Developed, trained and tested deep learning models for autonomous vehicle tasks with camera and lidar
data using Pytorch.
• Worked with object detection, panoptic segmentation, monocular depth estimation and traffic light
classification tasks.
• Implemented mid-fusion techniques for camera-LIDAR data fusion in BEV space and late-fusion methods lever￾aging objects geometric and semantic consistencies using neural networks.
• Developed tools to automate both the data collection and in-house annotation processes, facilitating the efficient
generation of datasets for training deep learning models.
• Developed custom Convolutional Neural Networks (CNNs) from the initial design phase through to
deployment, incorporating in-house data to meet specific project requirements.
• Worked with training pipelines in SageMaker for training new batches of data at scale and monitoring perfor￾mance.
• Optimised deep learning models for performance using TensorRT, proficient in converting advanced ar￾chitectures, implemented custom operators, and wrote CUDA modules for sensor data processing.
• Participated in multiple autonomous vehicle tests aimed at identifying the limitations of the current stack within a
specific operational design domain (ODD) and collaborated with other teams to develop safer solutions.
• Conducted thorough research and testing of state-of-the-art sensing systems (cameras and lidars) and
perception algorithms for the next-generation autonomous vehicle stack, creating detailed comparison reports.
• Showcased autonomous vehicle operations to clients from both industry and academia.
• Played a key role in upgrading the autonomous vehicle stack from ROS 1 to ROS 2.
• Customized the sensor drivers at a lower level to ensure that they provide sensor data and diagnostics in
accordance with our AV stack.

Developed, trained and tested deep learning models for autonomous vehicle tasks with camera and lidar data using Pytorch.
Worked with object detection, panoptic segmentation, monocular depth estimation and traffic light classification tasks.
Implemented mid-fusion techniques for camera-LIDAR data fusion in BEV space and late-fusion methods leveraging objects geometric and semantic consistencies using neural networks.
Developed tools to automate both the data collection and in-house annotation processes, facilitating the efficient generation of datasets for training deep learning models.
Developed custom Convolutional Neural Networks (CNNs) from the initial design phase through to deployment, incorporating in-house data to meet specific project requirements.
Worked with training pipelines in SageMaker for training new batches of data at scale and monitoring performance.
Optimised deep learning models for performance using TensorRT, proficient in converting advanced architectures, implemented custom operators, and wrote CUDA modules for sensor data processing.
Participated in multiple autonomous vehicle tests aimed at identifying the limitations of the current stack within a specific operational design domain (ODD) and collaborated with other teams to develop safer solutions.
Conducted thorough research and testing of state-of-the-art sensing systems (cameras and lidars) and perception algorithms for the next-generation autonomous vehicle stack, creating detailed comparison reports.
Showcased autonomous vehicle operations to clients from both industry and academia.
Played a key role in upgrading the autonomous vehicle stack from ROS 1 to ROS 2.
Customized the sensor drivers at a lower level to ensure that they provide sensor data and diagnostics in accordance with our AV stack.
Utilizing various open-source perception algorithms, we enhanced their speed and accuracy by tailoring them to specific task requirements and incorporating the latest advances in computer vision and machine learning.
An end-to-end scalable pipeline that facilitates nearly continuous data collection, annotation, training, and testing of deep learning models.
This experience comprises a two-year remote job based in India. My performance was so impressive that I was offered the opportunity to work closely with an Autonomous Vehicle team in the UK, allowing me to contribute more effectively.

Conducted in-depth analysis of AI technical literature, including patents and research papers.
Derived valuable insights on AI’s impact on client industries.
Provided clients with competitive research directions and facilitated IP management for strategic advantage.
During his tenure with us, we found him to be hardworking and very productive.
We found him to be a self-starter who is motivated, duty bound and a highly committed team player with strong conceptual knowledge.