The KEMAI Team
Doctoral Researchers
Meet the doctoral researchers of the KEMAI project team.
13 researchers
Computer Science, Philosophy & Ethics
9
Maximilian Otte, M.Sc.
Department of Computer Science
Neuro-Symbolic Integration with Information Constraints
Alexander Lodemann, M.Sc.
Institute of Artificial Intelligence
Planning the Unplannable: Leveraging Automated Planning to Guide Medical Procedures
AK
Atif Khurshid, M.Sc.
Institute of Neural Information Processing
Learning Search and Decision Mechanisms in Medical Diagnoses
Nahla Taha, M.Sc. Ing.
Institute of Medical Systems Biology
Integrating Semantic Domain Knowledge In Machine Learning For Medical Diagnostics.
NP
Nina Parchmann, M.A.
Institute of the History, Philosophy and Ethics of Medicine
Accountability of AI-based Medical Diagnoses
Uliana Vedenina, M.A.
Department of Computer Science
Multimodal Representation Learning for Reliable Medical AI
Yiheng Xiong, M.Sc.
Section of Experimental Radiology, University Hospital of Ulm
Robust Deep Learning for Medical Imaging under Domain Shift and Data Scarcity
Maximilian Otte, M.Sc.
Department of Computer Science
Neuro-Symbolic Integration with Information Constraints
→Alexander Lodemann, M.Sc.
Institute of Artificial Intelligence
Planning the Unplannable: Leveraging Automated Planning to Guide Medical Procedures
→AK
Atif Khurshid, M.Sc.
Institute of Neural Information Processing
Learning Search and Decision Mechanisms in Medical Diagnoses
→Michael Glöckler M. Sc.
Institute of Media Informatics
Explainable 3D Deep Learning for medical data
→Nahla Taha, M.Sc. Ing.
Institute of Medical Systems Biology
Integrating Semantic Domain Knowledge In Machine Learning For Medical Diagnostics.
→NP
Nina Parchmann, M.A.
Institute of the History, Philosophy and Ethics of Medicine
Accountability of AI-based Medical Diagnoses
→Uliana Vedenina, M.A.
Department of Computer Science
Multimodal Representation Learning for Reliable Medical AI
→Yiheng Xiong, M.Sc.
Section of Experimental Radiology, University Hospital of Ulm
Robust Deep Learning for Medical Imaging under Domain Shift and Data Scarcity
→Medicine
4CZ
HC
Jonas Plathow
Clinic for Diagnostic and Interventional Radiology
Künstliche Intelligenz zur Vorhersage des Verlaufes und der Hospitalisierung bei COVID-19-Pneumonien - Eine Experimentelle Studie als Modell zur zukünftigen Pneumonieprädiktion auf dem Boden von bildgebenden Biomarkern und klinischen Parametern
CZ
Christina Zellner
Ulm University Medical Center, Nuclear Medicine
Kombinierte bildgebungsbasierte und klinische Multi-Omics-Charakterisierung der intraindividuellen Tumorheterogenität beim metastasierten NSCLC - Analyse von Primärtumor und Fernmetastasen
Hanna Krekler
Ulm University Medical Center, Nuclear Medicine
Frühes Therapieansprechen beim NSCLC - Vorhersage mittels Delta-Radiomics, Machine Learning und integrativer Multi-Omics-Analyse
HC
Henriette Czech
Ulm University Medical Center, Nuclear Medicine
Multimodale, Radiomics- und KI-gestützte Charakterisierung der Vitalität und Heterogenität von Echinococcus-Läsionen mit besonderem Fokus auf prädiktive Marker des Therapieansprechens
Jonas Plathow
Clinic for Diagnostic and Interventional Radiology
Künstliche Intelligenz zur Vorhersage des Verlaufes und der Hospitalisierung bei COVID-19-Pneumonien - Eine Experimentelle Studie als Modell zur zukünftigen Pneumonieprädiktion auf dem Boden von bildgebenden Biomarkern und klinischen Parametern