Experience

Amazon

Jan 2025 – Present San Jose, CA

Design and implement advanced function-calling and tool-use capabilities for Amazon Q, enabling multi-step agentic reasoning and orchestration across enterprise knowledge sources.
Develop post-training pipelines including instruction tuning, DPO-based preference optimization, and targeted fine-tuning strategies that improve response quality, factual grounding, and task completion in complex enterprise workflows.
Research and deploy privacy-preserving inference techniques and alignment methods — including constitutional AI guardrails, PII-aware generation, and safety classifiers — for trusted enterprise LLM deployment at scale.
Build comprehensive evaluation frameworks for agentic LLM behaviors, incorporating multi-turn reasoning benchmarks, function-calling accuracy metrics, and automated red-teaming to drive rapid iteration cycles.

May 2022 – Dec 2024 Redmond, WA

Designed and shipped multimodal foundation model architectures for AR/VR, achieving 0.96 mIoU on semantic segmentation through novel SWIN-based Vision Transformer pipelines with knowledge distillation and quantization-aware training.
Built cross-modal knowledge transfer systems using CLIP and cross-attention mechanisms for VQA and image captioning, enabling zero-shot generalization across unseen domains.
Developed large-scale deep sequence recommendation models serving billions of daily predictions, incorporating Transformer-XL architectures with meta-learning for cold-start optimization.
Drove research on self-supervised pre-training, contrastive learning, and domain-adaptive strategies adopted across multiple product surfaces.

Huma AI

Jun 2020 – May 2022

Architected end-to-end cancer detection pipelines combining hierarchical CNNs, attention mechanisms, and self-supervised pre-training on pathology images, deployed on AWS SageMaker for clinical-scale inference.
Invented differentially private synthetic data generation frameworks using convolutional GANs and diffusion models, enabling HIPAA-compliant model training (published in Information Sciences, 140+ citations).
Designed adversarial debiasing and causal inference frameworks (Double ML) for disease prediction, reducing diagnostic bias while maintaining clinical accuracy.

Virginia Tech / West Virginia University

Jan 2016 – Aug 2020 Blacksburg, VA

Published 15+ peer-reviewed papers in top venues (IEEE Access, Information Sciences, arXiv) spanning NLP, computer vision, generative models, and audio-visual learning — accumulating 1,000+ citations.
Pioneered 3D CNN architectures for audio-visual speaker recognition and lip reading, establishing new benchmarks (150+ citations, IEEE Access 2017).
Authored a comprehensive NLP deep learning survey that became a widely referenced resource in the field (380+ citations).
Created high-impact open-source projects (TensorFlow-World, lip-reading-deeplearning, speechpy) with 8,000+ combined GitHub stars, adopted by researchers and practitioners worldwide.

Stealth Company

Sep 2012 – Dec 2014

Designed and optimized real-time digital signal processing algorithms for embedded systems, including adaptive filtering and spectral analysis modules.
Built low-latency audio processing pipelines for production hardware with strict throughput and memory constraints.

Work Experience