Led an 8–10 member R&D; team to develop an autonomous drone-delivery vision system, achieving a 30% reduction in detection latency through algorithm optimisation. Recovered a project with a 5-month delay risk, delivering on time without scope reduction and improving cross-functional collaboration. Mentored engineers and coordinated with hardware, software, and QA teams to meet regulatory and safety requirements. Reported directly to CEO and COO.

Applied state-of-the-art Deep Learning, from prototype to optimization and deployment. Developed algorithms for classification, regression, and segmentation (2D and 3D) in CT scans. Conducted model evaluation, selection, and validation, ensuring robustness and reliability of deployed models. Communicated findings effectively through reports and presentations to both technical and non-technical audiences.

Developed AI pipelines for 3D product generation from text using diffusion models and neural radiance fields (NeRFs). Implemented multi-view neural rendering to create photorealistic 3D environments and improved pipeline efficiency.

Reviewed and developed Python and C++ code generated by a Language Model (LLM) to ensure accuracy and adherence to best practices. Provided expert feedback and mentored junior developers, enhancing their coding skills and knowledge. Improved LLM performance by refining its knowledge base through thorough code evaluations and feedback loops. Implemented quality control processes to ensure the correctness of code and feedback.

Description: Guided 10 students to develop Deep Learning projects from data collection, training, testing, and reporting.

Description: Teaching assistant of the Intelligent Systems in Medical Imaging course for Master students. Supervised a Master student during her graduation project.

Proposed solution for Google acceptance and sanity tests: 80% faster and 200% more precise than previous approaches. Created the Android Automation team and led 5 Software developers. Developed a Macbeth ColorChecker algorithm for the Computer Vision team of Motorola Mobility & Lenovo.

Description: Reduced the false positive rate of OrkaPod’s face recognition algorithm using OpenCV.

Engineered a real-time video surveillance prototype to detect and track individuals, using Computer Vision.

Assistant professor and researcher in Machine Learning and Computer Vision.

Developed feature extraction algorithms using computer vision, the outcome was published in 3 scientific papers.

Thesis on Deep Learning for Localization and Segmentation in Thorax Abdomen CT. Applied Deep Learning for localization and segmentation of organs and abnormalities in CT scans. This research contributed to scientific journal publications.

Dissertation on Supervised Feature Selection by Ranking to Process Similarity Queries in Medical Imaging. Published scientific papers in conferences.