Mona Sharifi

  • Country of Origin: Iran
  • Home University: Shahid Beheshti University
  • Host University & Country: City College of New York - USA
  • PhD Title: AI, Algorithm development, Image processing in Single Molecule Localization Microscopy (SMLM)
  • Year of Completion: 2029
Mona Sharifi

PhD Overview

My research focuses on developing advanced machine learning algorithms for Single-Molecule Localization Microscopy (SMLM), a super-resolution imaging technique that resolves cellular structures beyond the optical diffraction limit.
I design and implement novel neural network architectures based on Kolmogorov-Arnold Networks (KANs), which use learnable spline-based activation functions to model complex nonlinear relationships with greater interpretability than conventional deep learning models.
My work includes physics-based simulation of microscopy data, signal processing, and the development of multi-stage detection and localization algorithms for high-density, overlapping emitter scenarios.
I also perform statistical analysis and benchmark algorithm performance against theoretical estimation limits such as the Cramér-Rao lower bound, while studying how different basis functions affect model accuracy and efficiency.
Overall, this research advances computational imaging and data analysis methods that enable more precise, efficient, and transparent quantitative analysis of biological data.

Sustainability Goals

  • Good Health and Well-being
  • Industry, Innovation and Infrastructure

About Me

I am a PhD candidate in Electrical Engineering at the CUNY City College of New York, working under the supervision of Professor Yi Sun. My work brings together artificial intelligence, data analysis, algorithm development, and biomedical imaging. I am especially interested in building algorithms that are both powerful and interpretable, so scientists can understand and trust the tools they use. My research applies Kolmogorov-Arnold Networks, an emerging class of neural networks, to super-resolution microscopy, an area where they have barely been explored. I enjoy working at the intersection of engineering and biology, turning complex physical and imaging problems into practical computational solutions.

My goal is to contribute to research that helps scientists better understand living systems at the molecular level and supports progress in health and medicine. I am truly grateful for the opportunity Schlumberger Foundation provided for me as a woman in research community and engineering. It has given me the time, resources, and help to focus fully on my research and pursue my research. Beyond the financial support, it means alot to me to know that the institute believes in my work and my potential as a researcher. This support has helped me grow both technically and professionally, and it motivates me to make the most of this opportunity.