AI/ML Solutions for Business Optimization

• Developed AI/ML solutions to help businesses optimize processes and improve efficiency.
• Implemented fraud detection systems to predict anomalies in transactions.
• Developed demand forecasting models to optimize inventory and supply chains.
• Created customer churn prediction models to analyze data and retain users.
• Built recommendation systems to personalize content and products.
• Conducted sentiment analysis using NLP to detect sentiments.

As a computer vision expert, I was consulted to address the challenge of facial pore recognition for a beauty application. The problem involved accurately identifying and measuring individual pores on various skin types and tones. To tackle this, I designed an algorithm that could adaptively capture high-resolution images and extract relevant features. Leveraging machine learning, I trained a model on a comprehensive dataset that I helped gather, comprising different face images under various lighting conditions. The resulting algorithm could recognize and measure individual pores with high precision, providing personalized skincare recommendations to users, and thereby enhancing the app's user experience.

• Collected and preprocessed data to build a time prediction model that improved the accuracy of rendering job estimates.

• Designed and implemented a visual predictive model using synthetic data for internal research.

• Built a sentiment analysis tool to assess user feedback and created a recommendation system based on user behaviour patterns.

• Engineered a SOTA multi-document summarization model for news aggregation, achieving an 87% human acceptance rate with less than 1% critical errors.
• Designed automated news timelines for evolving events, resulting in a 2.8% increase in content depth and a 1.4% boost in user time spent.
• Optimized ranking algorithms with a CTR prediction model, increasing daily active users by 4.3% and user engagement by 2.1%.
• Created a novel similarity scoring formula, improving F1-score from 0.91 to 0.95 for news clustering.

Over the last two years, I've been dedicated to promoting the integration of AI among scientists at the globally acclaimed marine research institution, Plymouth Marine Laboratory (PML). PML's core research program is focused on critical areas such as climate change, marine pollution, and sustainability. In a recent research study published in July 2023, I developed Machine Learning approaches to ascertain the primary factors influencing the phytoplankton carbon-to-chlorophyll ratio in the Atlantic Basin.These methods provided valuable insights into the environmental factors affecting phytoplankton carbon and chlorophyll levels in the corresponding figures.