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Hire Sobhan M.

Germany

USD 95 /hr

PhD | Senior Data Scientist | Science Manager and PhD Coach | Turning Multimodal Clinical Data into Actionable Insight

Profile Summary

Solid 10+ years of Experience in AI/ML/Data-Driven Research and Development for Clinical Decision Support (CDS) and Software as Medical Device (SaMD) | Highly Experienced in Science Management, Proposal Writing, Mentoring PhD Researchers

Subject Matter Expertise

• ☆ Computational Biology
• ☆ Biostatistics
• ☆ Neuroscience
• ☆ Computational Neuroscience
• ☆ Computer Science
• ☆ Data Analysis
• ☆ Data Management
• ☆ Decision Science
• ☆ Computer Science Education
• ☆ Excel
• ☆ Tensorflow
• ☆ LaTex
• ☆ R
• ☆ Scikit-learn
• ☆ Markup Languages
• ☆ Medical Devices
• ☆ Medical Device Development
• ☆ Medical Device Validation
• ☆ Medical Device Prototyping
• ☆ Medical Device Regulatory Consulting
• ☆ European Medical Device Regulation (MDR)
• ☆ ISO 13485
• ☆ Clinical Trials
• ☆ Clinical Trial Design
• ☆ Clinical Data Management
• ☆ Clinical Trials Data Analysis
• ☆ Precision Medicine
• ☆ Health Technology Assessment
• ☆ E-Health & Telemedicine
• ☆ Health Informatics

Services

• Writing

Writing Copywriting, Creative Writing, General Proofreading & Editing

Work Experience

Senior Postdoc Researcher

Helmholtz Centre for Infection Research

June 2025 - Present

Senior Postdoctoral Researcher

Fraunhofer SCAI

October 2023 - May 2025

Postdoctoral Research Associate

Digital Health Lab Düsseldorf

February 2022 - September 2023

Education

PhD in Computer Science

Univerity of Bonn

April 2018 - March 2022

MSc in Media Informatics

RWTH Aachen

October 2011 - March 2015

Certifications

• Certification details not provided.

Publications

JOURNAL ARTICLE

Holger Fröhlich, Anne Funck Hansen, Mika Hilvo, Gunther Jansen, Sumit Madan, Sobhan Moazemi, Sanziana Negreanu, Venkata Satagopam, Phil Gribbon, Christian Muehlendyck (2025). Reality Check: The Aspirations of the European Health Data Space Amidst Challenges in Decentralized Data Analysis . Journal of Medical Internet Research.

Lisa Kühnel, Julian Schneider, Ines Perrar, Tim Adams, Sobhan Moazemi, Fabian Prasser, Ute Nöthlings, Holger Fröhlich, Juliane Fluck (2024). Synthetic data generation for a longitudinal cohort study – evaluation, method extension and reproduction of published data analysis results . Scientific Reports.

1. Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano LJV, Dewitz B, et al. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review. Frontiers in Medicine [Internet]. 2023;10. Available from: https://www.frontiersin.org/articles/10.3389/fmed.2023.1109411 .

Steven Kessler, Dennis Schroeder, Sergej Korlakov, Vincent Hettlich, Sebastian Kalkhoff, Sobhan Moazemi, Artur Lichtenberg, Falko Schmid, Hug Aubin(2023). Predicting readmission to the cardiovascular intensive care unit using recurrent neural networks . DIGITAL HEALTH. 9. p. 205520762211495. {SAGE} Publications

Kessler, Steven, Schroeder, Dennis, Korlakov, Sergej, Hettlich, Vincent, Kalkhoff, Sebastian, Moazemi, Sobhan, Lichtenberg, Artur, Schmid, Falko, Aubin, Hug (2023). Predicting readmission to the cardiovascular intensive care unit using recurrent neural networks . Digital Health.

Moazemi, Sobhan, Vahdati, Sahar, Li, Jason, Kalkhoff, Sebastian, Castano, Luis J. V., Dewitz, Bastian, Bibo, Roman, Sabouniaghdam, Parisa, Tootooni, Mohammad S., Bundschuh, Ralph A., et al. (2023). Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review . Frontiers in Medicine.

Sobhan Moazemi(2022). Real-time 3D scans of cardiac surgery using a single optical-see-through head-mounted display in a mobile setup . Frontiers in Virtual Reality. 3. Frontiers Media {SA}

Sobhan Moazemi, Sebastian Kalkhoff, Steven Kessler, Zeynep Boztoprak, Vincent Hettlich, Artur Liebrecht, Roman Bibo, Bastian Dewitz, Artur Lichtenberg, Hug Aubin, et al. (2022). Evaluating a Recurrent Neural Network Model for Predicting Readmission to Cardiovascular ICUs Based on Clinical Time Series Data . Engineering Proceedings.

Dewitz, Bastian, Bibo, Roman, Moazemi, Sobhan, Kalkhoff, Sebastian, Recker, Stephan, Liebrecht, Artur, Lichtenberg, Artur, Geiger, Christian, Steinicke, Frank, Aubin, Hug, et al. (2022). Real-time 3D scans of cardiac surgery using a single optical-see-through head-mounted display in a mobile setup . Frontiers in Virtual Reality.

Annette Erle, Sobhan Moazemi, Susanne Lütje, Markus Essler, Thomas Schultz, Ralph A. Bundschuh (2021). Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans . Tomography.

Annette Erle, Sobhan Moazemi, Susanne Lütje, Markus Essler, Thomas Schultz, Ralph A. Bundschuh (2021). Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans . Tomography.

Sobhan Moazemi and Annette Erle and Zain Khurshid and Susanne Lütje and Michael Muders and Markus Essler and Thomas Schultz and Ralph A. Bundschuh(2021). Decision-support for treatment with 177Lu-PSMA: machine learning predicts response with high accuracy based on PSMA-PET/CT and clinical parameters . Annals of Translational Medicine. 9. (9). p. 818--818. {AME} Publishing Company

Sobhan Moazemi, Annette Erle, Susanne Lütje, Florian C. Gaertner, Markus Essler, Ralph A. Bundschuh(2021). Estimating the Potential of Radiomics Features and Radiomics Signature from Pretherapeutic PSMA-PET-CT Scans and Clinical Data for Prediction of Overall Survival When Treated with 177Lu-PSMA . Diagnostics. 11. (2). p. 186. {MDPI} {AG}

Sobhan Moazemi, Annette Erle, Susanne Lütje, Florian C. Gaertner, Markus Essler, Ralph A. Bundschuh (2021). Estimating the Potential of Radiomics Features and Radiomics Signature from Pretherapeutic PSMA-PET-CT Scans and Clinical Data for Prediction of Overall Survival When Treated with ¹⁷⁷Lu-PSMA . Diagnostics.

Moazemi, Sobhan, Erle, Annette, Luetje, Susanne, Gaertner, Florian C., Essler, Markus, Bundschuh, Ralph A. (2021). Estimating the Potential of Radiomics Features and Radiomics Signature from Pretherapeutic PSMA-PET-CT Scans and Clinical Data for Prediction of Overall Survival When Treated with Lu-177-PSMA . Diagnostics.

Moazemi, Sobhan, Essler, Markus, Schultz, Thomas, Bundschuh, Ralph A. (2021). Predicting Treatment Response in Prostate Cancer Patients Based on Multimodal PET/CT for Clinical Decision Support . Lecture Notes in Computer Science.

Sobhan Moazemi, Zain Khurshid, Annette Erle, Susanne Lütje, Markus Essler, Thomas Schultz, Ralph A. Bundschuh(2020). Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy . Diagnostics. 10. (9). p. 622. {MDPI} {AG}

Sobhan Moazemi, Zain Khurshid, Annette Erle, Susanne Lütje, Markus Essler, Thomas Schultz, Ralph A. Bundschuh (2020). Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy . Diagnostics.

Moazemi, Sobhan, Khurshid, Zain, Erle, Annette, Luetje, Susanne, Essler, Markus, Schultz, Thomas, Bundschuh, Ralph A. (2020). Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy . Diagnostics.

BOOK CHAPTER

Sobhan Moazemi, Tim Adams, Hwei Geok Ng, Lisa Kühnel, Julian Schneider, Anatol-Fiete Näher, Juliane Fluck, Holger Fröhlich (2024). NFDI4Health Workflow and Service for Synthetic Data Generation, Assessment and Risk Management . Studies in Health Technology and Informatics.

Sahar Vahdati, Deepankan Bharathi Nagaraj, Maximilian Bryan, Sobhan Moazemi, Sabine Gründer-Fahrer, Michael Martin (2023). Utilizing Transformers on OCT Imagery and Metadata for Treatment Response Prediction in Macular Edema Patients .

Sobhan Moazemi, Markus Essler, Thomas Schultz, Ralph A. Bundschuh(2021). Predicting Treatment Response in Prostate Cancer Patients Based on Multimodal PET/CT for Clinical Decision Support . 22--35Springer International Publishing

CONFERENCE PAPER

Bastian Dewitz, Sobhan Moazemi, Sebastian Kalkhoff, Steven Kessler, Christian Geiger, Frank Steinicke, Hug Aubin, Falko Schmid (2023). Enacted Selves in Technological Activities – Framework and Case Study in Immersive Telementoring .

Sobhan Moazemi(2022). ARMAGNI: Augmented Reality Enhanced Surgical Magnifying Glasses . Linköping Electronic Conference Proceedings. Linköping University Electronic Press

Sobhan Moazemi, Sebastian Kalkhoff, Steven Kessler, Zeynep Boztoprak, Vincent Hettlich, Artur Liebrecht, Roman Bibo, Bastian Dewitz, Artur Lichtenberg, Hug Aubin, et al.(2022). Evaluating a Recurrent Neural Network Model for Predicting Readmission to Cardiovascular ICUs Based on Clinical Time Series Data . {MDPI}

DISSERTATION THESIS

Computer Assisted Diagnosis in PET/CT : Machine Learning for Prognosis in Oncological Patients @phdthesis{handle:20.500.11811/9708, urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-66096, author = {{Mohammadsobhan Moazemi Goodarzi}}, title = {Computer Assisted Diagnosis in PET/CT : Machine Learning for Prognosis in Oncological Patients}, school = {Rheinische Friedrich-Wilhelms-Universität Bonn}, year = 2022, month = mar, note = {Artificial intelligence (AI) has revolutionised problem solving in a wide range of industrial as well as research domains. Particularly, computer-aided diagnosis (CAD) and clinical decision support systems (CDSSs) as sub-domains of AI, have gained critical importance in many biomedical and clinical domains such as virology, computational neuroscience, and oncology. As making accurate decisions in a timely manner is an inevitable part of daily routines in the medical and clinical domains, machine learning (ML) and deep learning methods are widely applied in CAD and CDSSs to provide diagnostic and prognostic assistance for the researchers and physicians as the domain experts. Focusing on advanced prostate cancer (PCa) disease as an example, the procedure of disease staging and patient screening using established CAD tools is considered time consuming and attention intensive. In many clinical practices, this procedure includes examining patients’ prostate-specific membrane antigen-positron emission tomography/computed tomography (PSMA-PET/CT) scans and analyzing patient-specific clinical factors in a daily routine. Thus, as the main motivation behind this PhD thesis project, AI and ML based methods are utilized to automate the corresponding diagnostic and prognostic pipelines. Accordingly, providing an automated CDSS which facilitates: 1) visualization and annotation of medical scans, 2) automated segmentation of pathological uptake, 3) prediction of treatment outcome taking advantage of radiomics features extracted from Gallium[68]-(68Ga)-PSMA-PET/CT scans in PCa patients was the main objective of this thesis. To this end, we introduce AutoPyPetCt, an automated pipeline developed in Python which takes multimodal whole-body baseline 68Ga-PSMA-PET/CT scans and patient-specific clinical parameters as input and applies state-of-the-art statistical, ML, and deep learning techniques to automatically identify and segment pathological uptake all over the body, to anticipate responders to Lutetium[177]-(177Lu)-PSMA therapy, and to predict overall survival of the PCa patients. To achieve this, on the one hand, multimodal PET/CT scans integrate functional as well as anatomical aids to locate malignancies as volumes and regions of interest (VoIs and and RoIs respectively). On the other hand, a variety of conventional parameters (such as standardized uptake value (SUV)) as well as radiomics features (such as textural heterogeneity features) extracted for the VoIs/RoIs together with patient-specific clinical factors (such as age and prostate-specific antigen (PSA) level) form the basis for statistical and ML-based analyses towards prognostic hypotheses realizing the prediction of patient level outcomes such as treatment response and overall survival. The main contribution of the methods is to provide automated decision support tools to manage patients with advanced PCa in shorter times and with limited annotation effort. To investigate the relevance and to quantify the performance of the methods, multiple retrospective quantitative as well as qualitative clinical studies have been conducted which resulted in several preliminary conference abstracts, four journal papers, and one conference paper. The studies had been carried out along the whole project’s life-cycle, starting by a proof of concept and finalizing with the evaluations of the integrated solution pipeline. The findings from the clinical studies confirmed the overall relevance of the methods and their potential to replace parts of current clinical routine procedures in the future. Most interestingly, the provided automated segmentation tools achieved high performance in true delineation of pathological uptake which outperformed a standard established thresholding based approach. However, the results of the treatment response prediction studies, regardless of different segmentation methods, identified rooms for improvement. To conclude, the provided automated decision support system has shown its potential to serve as an assistant for the management of patients diagnosed with advanced prostate cancer disease. However, to further assess the generalizability of the findings and to improve the decision making certainty, studies including multicentric data should be considered as future work.}, url = {https://hdl.handle.net/20.500.11811/9708} } .