CV

Lead Machine Learning Engineer - Tech Lead

Nike

Directing the development of advanced machine learning and generative AI systems to revolutionize personalized consumer experiences and design process. Focused on integrating AI into Nike's design, testing, and consumer engagement initiatives to drive innovation and optimize operational efficiency.

• Leveraged generative AI for enhanced image search, enabling precise and visually-driven product discovery.
• Designed a recommendation system integrating vision models and Large Language Models (LLMs) to deliver highly personalized design suggestions.
• Implemented collaborative filtering techniques in recommendation systems, improving accuracy and relevance for user-specific product recommendations.
• Applied generative AI to enhance semantic segmentation and feature learning, boosting model performance in understanding complex visual data.

Machine Learning Research Scientist

Meta (Facebook)

Led cutting-edge AI research and development, focusing on the integration of NLP, Computer Vision, and Generative AI to drive advancements in consumer engagement and content personalization. Directed end-to-end initiatives from conceptualization through deployment, consistently enhancing computational efficiency and accuracy across Meta's digital platforms.

• Enhanced semantic segmentation accuracy by 7% and boosted user engagement by 15% using advanced Vision Transformer (ViT) and SWIN Transformer models.
• Deployed Retrieval-Augmented Generation (RAG) systems with Large Language Models (LLMs) to improve query relevance and user satisfaction.
• Tripled computational efficiency with mixed-precision training and model quantization, reducing costs and increasing revenue by 33%.
• Developed cross-modal AI systems integrating NLP and Computer Vision, advancing capabilities in Visual Question Answering (VQA) and Image Captioning.
• Led deployment of generative AI models for personalized content, using diffusion models and transformers to elevate user experience on Meta platforms.
• Optimized model deployment with TorchScript and ONNX for low-latency, real-time performance across diverse hardware.
• Built large-scale data pipelines with SQL and BigQuery, improving data-driven decision-making and model training efficiency.

Senior Data Scientist

Huma AI

Specialized in developing and optimizing automated Q&A systems powered by large language models (LLMs) to drive revenue growth and operational efficiency. Led initiatives that leveraged cutting-edge NLP techniques to deliver accurate, responsive answers and streamline workflows, contributing to substantial cost savings and enhanced user experience.

• Boosted answer accuracy by 20% and reduced response latency by 40% using zero-shot learning and clustering, enhancing system responsiveness and accuracy.
• Optimized end-to-end data flow for Q&A, cutting manual labeling costs by 70% with automation, leading to significant cost savings.
• Integrated AWS services, including SageMaker, for scalable Q&A model deployment, ensuring high performance and reliability in production.
• Generated over $1M in annual revenue by improving model accuracy and system reliability, making the Q&A system a key revenue asset.
• Built robust NLP pipelines using frameworks like Hugging Face and LangChain, enhancing model training efficiency and system responsiveness.

Graduate Research Assistant

Virginia Tech

Conducted research on privacy-preserving techniques for deep learning, focusing on federated learning in NLP and Computer Vision.

• Developed privacy enhancement algorithms for large language models.

Graduate Research Assistant

West Virginia University

Researched the integration of computer vision and speech technologies for multimodal interaction systems.

• Engineered a lip-reading system to enhance communication aids for the hearing-impaired.

PhD

Virginia Polytechnic Institute and State University (Virginia Tech)

Computer Science

• Thesis: Synthetic data generation for privacy-preserving deep learning in medical applications.

Best Ph.D. Thesis Award

Virginia Tech

Awarded for significant contributions to generative AI-driven synthetic data generation for healthcare.

Open Source Projects on GitHub

A collection of machine learning, computer vision, and NLP projects.

• Applications in Generative AI
• Multi-modal AI
• Optimization for real-time systems