Sean Wade

• Led cross-functional team in developing, shipping and maintaining core on-device ML algorithms to users:
  • Meal logging tool with food image classification and LLM nutrition feedback
  • Personalized workout recommender system using a multi objective loss function and online learning
  • Unsupervised walk detection algorithm from steps data
  • Contextual notifications engine using routine identification (commute to work, typical workout time, etc)
• Evaluated performance of ML models with focus on safety, robustness and privacy and efficacy communicated tradeoffs to partners
• Architected data schema and privacy preserving data collection framework for launch of multiple health related programs and research studies (Lumihealth, Heartline, Attain)
• Created application for labeling health data to create datasets and provide human feedback for models
• Built data pipelines to process high volumes of medical claims data and health sensor data
• Designed research studies and experiments to study impact of digital interventions and models on hypertension, diabetes, asthma, MCI and atrial fibrillation

• Created tools to improve deploying, evaluating, and retraining ML models on Azure
• Extended Azure SDK to support MLflow

• Developed recommender system for rides/attractions in Disney World Park app
• Created realtime pipelines and dashboards to monitor performance

• Used drone imaging and photogametry to construct 3D models of buildings
• Built convolutional neural network to identify and classify roof damage

Researched synthetic cancer cell image generation using conditional adversarial neural networks and wrote a python library to distribute it. Created novel methods for representing medical claims history with vector embeddings.