Research Phase Postdoctoral Fellows
Kwok Lung (Jason) Fan, PhD
2025-2029
Dr. Fan obtained a BSc in Physics and Decision Analytics from the University of Hong Kong in 2019 and a PhD in Physics from the University of Maryland in 2024. During his doctoral studies, Dr. Fan researched particle and multi-messenger astrophysics with the HAWC gamma-ray and IceCube neutrino experiments. His work involved developing machine learning reconstruction algorithms to distinguish gamma-ray signals and likelihood-based joint analyses for astrophysical objects, specifically focusing on galactic PeVatrons—extreme accelerators in the galaxy powered by exotic physical events.
Following his PhD, Dr. Fan transitioned into the field of medical physics, first joining the US Food and Drug Administration as an ORISE postdoctoral fellow. There, he focused on the evaluation of medical imaging AI and the quantification of performance uncertainty in diagnostic imaging. He subsequently joined the University of Pennsylvania as a postdoctoral researcher, where he specialized in the analysis and explainability of AI for CT denoising.
In 2025, Dr. Fan joined the hybrid residency program at Memorial Sloan Kettering Cancer Center. He is currently working on markerless prostate tracking using Cone Beam Computed Tomography (CBCT) with Dr. Tianfang Li, with a focus on limited-angle reconstruction utilizing diffusion models. By improving the accuracy of real-time motion tracking during radiotherapy, his work aims to ensure more precise dose delivery and better treatment outcomes for patients.
Nasim Adnani, PhD
2025-2029
Dr. Adnani earned both a Master’s and a PhD in Electrical Engineering from Auburn University, where her graduate research focused on quantitative magnetic resonance imaging (MRI) of the human central nervous system.
In 2025, she joined Memorial Sloan Kettering Cancer Center (MSKCC) as a Postdoctoral Research Fellow and will continue her training within a CAMPEP-accredited therapeutic medical physics residency. Her current work centers on the development of artificial intelligence–based MRI reconstruction methods for diffusion-weighted imaging (DWI) and non-Cartesian spiral acquisition for MSK’s MR-Linac system. These approaches aim to improve signal-to-noise ratio (SNR), enhance image quality, and reduce scan time. Her research interests include quantitative MRI, with a current focus on diffusion, MRI image acquisition and reconstruction, and translational MRI technologies for radiation oncology and cancer treatment.
Susanne O’Hare, MS
2025-2027
Susanne O’Hare earned a First-Class Honors degree in Theoretical Physics from Trinity College Dublin and a Master of Science in Medical Physics from Columbia University. Combining strong academic foundations with hands-on clinical expertise, Susanne has experience across leading healthcare institutions including NYU Langone, Mount Sinai, and Petrone Associates, where she has conducted quality assurance for advanced radiotherapy systems, supported regulatory compliance, and contributed to patient safety initiatives. Susanne joined the MSK residency program in December 2025.
Bohong Huang, PhD
2024-2028
Bohong Huang’s research focuses on developing deep learning–based solutions for markerless tumor motion monitoring in radiation. Her work centers on a target decomposition technique that uses patient-specific deep learning models to enhance tumor visibility on KV projection images, enabling real-time markerless lung tumor tracking during SBRT. She has validated this framework on a dynamic chest motion phantom, achieving sub-millimeter tracking accuracy with clinically feasible latency, and applied it to quantify internal tumor stability during DIBH lung SBRT, revealing that external surrogates alone may be insufficient for high-precision motion management. In parallel, she is developing diffusion model–based 4D CBCT reconstruction from sparse-view acquisitions to extend markerless tracking capability to additional treatment sites. By integrating AI-driven imaging techniques into radiotherapy workflows, her work aims to improve treatment precision and patient outcomes. Dr. Huang is scheduled to start clinical residency in 2026.
Clinical Phase Residents
Abbas Jinia, PhD
2023-2027
Dr. Jinia obtained a Master’s degree in Nuclear Engineering from Purdue University and a PhD in Nuclear Engineering and Radiological Sciences from the University of Michigan. During graduate school, Dr. Jinia researched the application of machine learning algorithms for digital processing of scintillation pulses.
In 2023, Dr. Jinia joined Memorial Sloan Kettering Cancer Center as a postdoctoral researcher working on developing an Artificial Intelligence-based Incident Learning System. This system analyzes safety- and quality-related events reported by clinicians in the Department of Medical Physics using large language models. By automating the analysis, this system aims to identify hard-to-discern data trends in large datasets, with the ultimate goal of improving safety and quality in radiation treatment. Dr. Jinia began his clinical residency in 2025.
Xinan Chen, PhD
2023 - 2027
Xinan Chen earned her BS degree in Mathematics from University of Science and Technology of China in 2017. Advised by Dr. Allen Tannenbaum and Dr. Helene Benveniste, Xinan received her PhD degree in Applied Mathematics from Stony Brook University in 2022, where she applied the theory of optimal mass transport (OMT) into the dynamic contrast-enhance MRI (DCE-MRI) data of rat brains to quantitatively measure and visualize brain fluid dynamics.
Since joining Memorial Sloan Kettering in late 2022, Xinan worked with Dr. Joseph Deasy on developing and applying new OMT-based mathematical models into DCE-MRI of breast cancer and glioblastoma to quantify the microenvironment of tumors. Dr. Chan began her clinical residency in 2025.
Sudharsan Madhavan, PhD
2023 - 2027
Sudharsan Madhavan is a therapeutic medical physics resident at Memorial Sloan Kettering Cancer Center, where his research focuses on developing deep learning methods for MRI-guided prostate radiotherapy, including deformable registration, dose accumulation, and outcomes modeling.
Previously as a postdoc, he applied voxel-based machine learning to identify anatomic subregions predictive of radiation toxicity in head and neck cancer patients. His interdisciplinary training spans medical physics, biomedical engineering, and cardiovascular biomechanics. He aspires to advance AI-driven personalized radiotherapy as an independent investigator someday.
Paulo Mejia Quintero, PhD
2023–2027
Paulo holds a Bachelor’s degree in Engineering Physics and a Master’s degree in Medical Physics from the Universidad Nacional de Colombia, along with a Master’s in Advanced Physical Methods in Radiotherapy from the Heidelberg University in Germany and a PhD in Physics from the University of Hull in England. Besides his eight years in clinical radiotherapy, his early research focused on radiobiology, evaluating the survival and hypoxia effects in irradiated tumoral cells. Later, he designed and validated a 3D-printed phantom for patient-specific quality assurance (PSQA) in radiosurgery, integrating simultaneous dosimetric tools such as pinpoint ionization chambers, microDiamond detectors, and Gafchromic films.
During his PhD, Paulo explored the modulation complexity metrics of single and dual-layer multi-leaf collimators (MLC), employing machine learning techniques to correlate these metrics with treatment quality metrics like gamma passing rates.
Paulo’s post-doctoral work at MSK (2023-2025) focused on AI-based methods for generating synthetic 3D and 4D MRI oriented to improve motion management strategies in radiotherapy. With a diverse background, Paulo is committed to advancing the field of medical physics and contributing to the future of radiotherapy.