I am an undergraduate student in Biological Sciences at Carnegie Mellon University in Qatar and a research fellow with the Optima Research Group. My academic interests are centered on interdisciplinary research at the convergence of biology, optics, and computation, with a particular emphasis on developing computational frameworks that enhance experimental sensing and analytical platforms.
My research motivation lies in the translation of biological and physical systems into digital and computational models that enable efficient data acquisition, interpretation, and decision-making. I am especially interested in the application of machine learning and neural networks to optical and spectroscopic data, where data-driven approaches can complement and extend traditional analytical methods.
Within the Optima Research Group, I contribute as a research intern with a focus on computational and software-based support for ongoing research activities. This includes participation in the development and maintenance of the group’s digital research infrastructure, facilitating clear dissemination of research objectives, methodologies, and outcomes. Through this role, I aim to support collaborative research while gaining deeper exposure to experimental design, optical instrumentation, and data analysis workflows.
Through my work at Optima, I seek to further develop my interdisciplinary research skills and contribute to data-driven innovations in biological and optical sciences.
Computational representation and analysis of biological systems, emphasizing abstraction, simulation, and algorithmic interpretation of complex biological processes.
Application of supervised and unsupervised learning methods to spectroscopic, imaging, and biosensing data for feature extraction, classification, and predictive modeling.
Design and analysis of optical systems for sensing and measurement, including light–matter interaction, signal optimization, and system-level performance evaluation.
Use of optical spectroscopy and imaging techniques for qualitative and quantitative analysis in biological, chemical, and environmental contexts.
Development of optical biosensors and photonic techniques for biomedical diagnostics, health monitoring, and biological signal detection.
Integration of computational methods with optical system design to improve reconstruction, denoising, calibration, and interpretation of optical signals.
Research into compact, low-cost, and mobile sensing systems that combine photonics, electronics, and computation for real-world deployment.
A smartphone-based near ultraviolet (UV) colorimeter is demonstrated for the first time using an external UV LED source (Δλ ≈ 365 − 415 nm) and the inbuilt CMOS camera of a smartphone.