EEG labeling

This application streamlines the neurological review process by automatically detecting and labeling potential epileptic activity in long-term EEG recordings. By filtering out non-event data, the tool enables specialists to shift their focus from searching for anomalies to performing high-value clinical analysis. See demo here.

Currently foccusing on (January 2026):

• System Performance Evaluation: Researching and establishing standardized benchmarking metrics to quantitatively evaluate system performance against state-of-the-art solutions.
• Multiclass Anomaly Detection: Expanding the Machine Learning model from binary detection to the specific classification of different epileptic event types.
• System Design Refinement: Optimizing the data pipeline to ensure the classification engine maintains low latency during high-volume recording analysis.

• Machine Learning
• System Design

IsHas

Fire Safety Digitalization

IsHas is developed specifically for fire safety inspectors, this application replaces time-consuming manual notes with a streamlined digital workflow that allows for instant, one-click report generation. By centralizing records and implementing role-based access, the platform significantly boosts operational efficiency and ensures full audit readiness.

Currently foccusing on (January 2026):

• Offline Synchronization: Engineering a robust "offline-first" mode for the mobile app to ensure inspectors can record data in buildings with poor connectivity.
• Asset Support Expansion: Extending the system architecture to include comprehensive inspection modules for hydrants, moving beyond the initial fire extinguisher focus.
• Workflow Step Refinement: Optimizing the step-by-step sequence within the app for different usecases of the app.
• Process Intuition: Iterating on the user interface to make the transition between different inspection stages more seamless and user-friendly.

• Web App
• Mobile App