Creation of AI generated image detector

I used vision transformers, data augmention,face detector and deployed the model on AWS SagaMaker

I used webscrapped data to create similarity search model

In the corporate sector, text data flows swiftly, emanating from diverse sources in various formats, destined for multiple endpoints and actions. This continuous stream of data necessitates management through structuring, making it easy to interpret and analyse, and ultimately directing it to the appropriate user or applying the insights obtained. I utilised several large language models (LLMs) streamline and enhance this process:

• A question-answering LLM, enabling users to inquire about the content of each document within the flow.
• A summarization LLM, offering a concise overview of each document.
• An NLP model for sentiment analysis, determining the sentiment of each document.
• An NLP model that calculates an absolute similarity score among all documents.
• Clustering, which groups similar documents together and simultaneously identifies outlier documents.

• Led development of face and license plate analytics across 4 engineering teams.

• Built ML models for driver identification using sensor data (RF, MLP, DT, KNN).

• Researched video-based action recognition (publication under review).

• Created a facial recognition API using FastAPI and OpenVINO Model Server.

• Supported academic research with practical deployments and experiments

In the dynamic realm of banking, ensuring effective anomaly detection is paramount. Utilising my expertise in state-of-the-art ML algorithms and advanced Deep Learning techniques, I tackled this imperative at Deutsche Bank, a globally recognized leader in financial services. The solution involved several critical stages:

• Identifying relevant data from various sources.
• Researching and testing optimal Machine Learning algorithms for tabular data analysis.
• Exploring and validating appropriate Deep Learning models for extracting insights from textual data (Natural Language Processing).
• Integrating the resulting models into the rigorous banking environment, complete with all necessary software engineering components: Python, Object-Oriented Programming, Algorithm Design and Complexity, Deployment, and more.