I'm a Postdoctoral Scholar at Stanford University in the Computational Neuroimage Science Laboratory (CNS Lab) with Prof. Kilian M. Pohl. My research focuses on machine learning models that can improve the understanding, diagnosis, and treatment of neuropsychiatric disorders.
Previously I completed my PhD at the Chair for Computer Aided Medical Procedures at the Technical University of Munich under the supervision of Prof. Nassir Navab. I am passionate about designing trustworthy deep learning methods for challenging applications.
Honors & Awards
Graduate Student Travel Award, Medical Image Computing and Computer Assisted Interventions (MICCAI) (October 2019)
Best Poster Award, International Conference on Information Processing in Medical Imaging (IPMI) (June 2019)
Boards, Advisory Committees, Professional Organizations
Public Relations Officer, MICCAI Student Board (2017 - 2020)
PhD, Technical University of Munich, Learning Robust Representations for Medical Diagnosis (2021)
M.Sc., Technical University of Munich, Informatics (2017)
B.Sc., Aristotle University of Thessaloniki, Informatics (2015)
Kilian Pohl, Postdoctoral Faculty Sponsor
Detecting negative valence symptoms in adolescents based on longitudinal self-reports and behavioral assessments.
Journal of affective disorders
BACKGROUND: Given the high prevalence of depressive symptoms reported by adolescents and associated risk of experiencing psychiatric disorders as adults, differentiating the trajectories of the symptoms related to negative valence at an individual level could be crucial in gaining a better understanding of their effects later in life.METHODS: A longitudinal deep learning framework is presented, identifying self-reported and behavioral measurements that detect the depressive symptoms associated with the Negative Valence System domain of the NIMH Research Domain Criteria (RDoC).RESULTS: Applied to the annual records of 621 participants (age range: 12 to 17 years) of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), the deep learning framework identifies predictors of negative valence symptoms, which include lower extraversion, poorer sleep quality, impaired executive control function and factors related to substance use.LIMITATIONS: The results rely mainly on self-reported measures and do not provide information about the underlying neural correlates. Also, a larger sample is required to understand the role of sex and other demographics related to the risk of experiencing symptoms of negative valence.CONCLUSIONS: These results provide new information about predictors of negative valence symptoms in individuals during adolescence that could be critical in understanding the development of depression and identifying targets for intervention. Importantly, findings can inform preventive and treatment approaches for depression in adolescents, focusing on a unique predictor set of modifiable modulators to include factors such as sleep hygiene training, cognitive-emotional therapy enhancing coping and controllability experience and/or substance use interventions.
View details for DOI 10.1016/j.jad.2022.06.002
View details for PubMedID 35688394
- OperA: Attention-Regularized Transformers for Surgical Phase Recognition SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 604-614
- Rethinking Ultrasound Augmentation: A Physics-Inspired Approach SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 690-700
- Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 273-282
- Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning IEEE. 2020: 5534-5541
SIGNAL CLUSTERING WITH CLASS-INDEPENDENT SEGMENTATION
IEEE. 2020: 3982-3986
View details for Web of Science ID 000615970404046
- Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 517-529
- 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 438-446
- Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 493-501