Automated Incident Detection Models

Over a three-year period, we worked closely with one of Russia's largest banks to successfully establish a robust AI monitoring system. This system operates with precision to swiftly address system failures by pinpointing their root causes and making well-informed decisions. The model expanded the scope of forecasted indicators to a total of 50,000, achieving an 85% reduction in false positive alerts. It also delivered a 20% improvement in precision compared to previous methodologies.

Led a team to develop an AI monitoring system that was capable of detecting anomalies in various business indicators. By closely working with one of Russia's largest banks over a span of 3 years, we designed and implemented an advanced backend system dedicated to the detection of anomalies across a wide spectrum of critical business metrics. This system significantly decreased the time and effort previously invested in manual incident detection and enhanced operational efficiency.

I coordinated a team in the development of two distinct automated incident detection models, which yielded substantial improvements in incident detection times, reducing them by an impressive 5 to 15 minutes. These models played a pivotal role in streamlining processes and optimizing the bank's operations.

As part of my role at Time Research & Innovation, I Ied creation of World Health Aid (WHA), a multilingual real-time health advice platform designed to support disadvantaged individuals. WHA is a comprehensive solution that facilitates telemedicine services, bridging the gap between healthcare providers and underserved populations. It combines live patient and clinician-facing decision support systems, offering a range of paid and free services, including self-diagnosis screening through video and voice chat, multilingual instant translation, voice-command symptom search, real-time messaging, and secure record keeping.

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.