Tech lead for computer vision and multimodal research

Designing and optimizing generative models to enhance product image quality through advanced image synthesis and restoration techniques.

Developing multimodal large-scale models to accurately predict product attributes and identify mismatches and policy non-compliance in listings.

Created ETL and staging pipeline for measuring the performance of ML models at scale (Kubeflow, Prefect, Kubernetes, EKS, AWS Batch, Lambda, and Terraform)

Developed a real-time visual search engine dashboard and API service for exploring millions of ultrasound images using KNN and vector database (Amazon OpenSearch, Beanstalk, Plotly)

Setting up a workflow for model experiment tracking and model unit-testing using Data Version Control (DVC), Weights&Biases, and Kolena platforms

Scripted unit tests, checked the module performance on simulation, bench, and field vehicle, and finally deployed the module for the release

Utilized Google Cloud Platform (GCP) for model training and prediction

Simulated a virtual vineyard in Blender and generated synthetic data for pre-training and

Applied supervised transfer learning and retrained Mask-RCNN, U-NET, and MMRotate for

Filtered and manipulated pointclouds in Open3D, registered pointclouds from multiple

Evaluated the accuracy of stereo depth estimation from OpenCV block matching, HITNet,

Simulated robot’s manipulation in ROS and benchmarked +10 trajectory planners from

Integrated, calibrated, and tested ZED2i, RealSense D435i, Analog Device ToF, Occipital, and

Developed React-based teleoperation dashboard and utilized roslibjs and WebRTC to

Developed a real-time ROS node for license plate recognition

Tested Facebook’s action recognition deep network (SlowFast) for suspicious behavior recognition on the collected data from the field

Implemented speech-to-text feature to detect suspicious keywords inside the conversations

Applied HDL Graph SLAM and created 3D map and localized robot using 3D lidar, wheel odometry

Tuned global and local ROS planners for autonomous navigation of the robot

Experimented and evaluated Autoware’s vector map for autonomous navigation inside the parking lots

Distributed computation between edge devices and running real-time object detection (Yolo), license plate recognition network, and navigation stack in parallel

Set up Webviz for remote monitoring of the robot