Currently enrolled in a pre-PhD position as a Deep Learning Research Engineer with IDEMIA to address the challenge of "Handling Occlusion for Multi-Object-Tracking"

• Carried out a 5‑month research internship at the AudioVisual Communications Laboratory (LCAV), focusing on developing a state‑of‑the‑art reconstruction algorithm for lensless cameras using generative models, contributing to the compact and lightweight imaging systems.• Utilized a generative model to address the unique inverse problem of lensless imaging, balancing luminosity and reconstruction complexity trade‑offs. Leveraging a pre‑trained generator on the FFHQ dataset for faces, the approach demonstrated promising simulation results compared to traditional optimization techniques leading to a PSNR increase from 0.8 to 9.8, depending on the mask choice.• Significantly improved the efficiency of internal libraries, including LenslessPiCam, waveprop, and slm‑controller. Enhanced mask creation speed by reducing computation time from 5.57 s to 35.1 ms, and introduced a separable mask option, accelerating computations by a factor of 10 through essential updates and optimizations.

• Implemented adaptive teaching methodologies in an undergraduate electrical engineering course, mentoring students with practical experiments and engineering projects, thereby fostering hands‑on learning and problem‑solving skills.

• Led the development of an automatic aircraft anchoring system during a six‑month research internship in an R&D department, achieving a reduction in anchoring time from 3 minutes manually to just 30 seconds automatically.• Applied deep AI and machine learning knowledge to adapt anchoring methodologies for specialized use‑cases. Gained proficiency in camera model and calibration theory to optimize system performance. Further extended the model capabilities to the Hololens platform using WinML and the ONNX converter in C#

• Undertook a two-month research internship focusing on the development of a self-supervised method for depth map estimation and model-based refinement.• Acquired comprehensive practical insights into machine learning and computer vision through immersion in a dynamic research center environment, thereby refining my problem-solving skills and expanding my field knowledge.