Currently heading the Face AI Research team building face-based identity verification & fraud detection algorithms, Fair Face Recognition algorithms, 2D & 3D face synthesis models, and efficient deep learning architectures for FR.

Built the best face recognition algorithms in the nation and some of the best in the world, ranking among the top 5 globally on the prestigious US NIST's Face Recognition Vendor Test (FRVT) 1:1 and 1:N leaderboards.

Leading the new AI research initiatives and go-to-market in the organization like deepfake detection, visual speech recognition, and face-based remote health assessment.

Worked on approximate nearest neighbor (ANN) algorithms for efficient billion-scale face search and de-duplication systems. Worked extensively with ANN libraries like Milvus and Faiss.

Built highly accurate rotation invariant face detector and deployed it in production, which more than 100 clients in different countries use, detecting millions of faces every month.

Developed efficient distributed deep learning training pipelines for multi-node and multi-GPU training workflows with non-trivial deep learning architectures.

Took deep learning optimization and deployment tools like TensorRT, Triton Inference Server, ONNX and OpenVINO mainstream throughout the organization.

Responsible for setting up all the QC pipelines and documentation for the face recognition pod. Conducted numerous POCs with potential clients to assess value addition and understand new product directions.

Responsible for automating POC and digitization pipelines which decreased the manual efforts from days to a few hours.

• Managed and mentored more than 10 interns and ML Engineers to help them successfully achieve their goals.

Worked closely with the engineering team to take our models to production. Also worked with product and business teams for product market research, ideation, and understanding client requirements.