My Research

Current position (June 2023-)

I am currently working as a postdoc at M&CS, TU Eindhoven, as a part of ITEA4 DAIsy project. I am co-leading WP4 of this project, which focuses on development of AI solutions for Major depressive disorder and Eating disorder. I am currently working on developing causal ML methods for clinical decision support.

Postdoc position (Sep 2021-May 2023)

I worked jointly at the Institute of Risk Assessment Science (IRAS) in Utrecht University and at UMC Utrecht. I am learned about exposome and cardiovascular risks, and used interpretable ML models to find urban exposomes which increased risks of mortality.

PhD research

During my PhD I worked on interpretable supervised machine learning approaches to deal with real world medical data, with the final goal of having computer aided clinical decision support system for medical professionals , and knowledge extraction from provided datasets.

Here you can find a summary of my academic journey till now.

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Publications

All in the Name of Artificial Intelligence: A Commentary on Linardon (2025) – an invited commentary for International Journal of Eating Disorder – Pia Burger and Sreejita Ghosh
Interpretable modelling and visualization of biomedical data – Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte
A prototype-based model for set classification –Mohammad Mohammadi and Sreejita Ghosh
Review of Machine Learning solutions for Eating Disorders –Sreejita Ghosh, Pia Burger, Mladena Simeunovic Ostojic, Joyce Maas, Milan Petkovic
Stacking multiple prediction models to optimise performance in local settings: exemplars in cardiometabolic disease (Preprint) –Sreejita Ghosh, Jasmine Gratton, Roel C.H Vermeulen, Folkert W Asselbergs, Jelle J. Vlaanderen, Amand Floriaan Schmidt
Intrinsically Interpretable Machine Learning In Computer Aided Diagnosis (PhD thesis)
Visualisation and knowledge discovery from interpretable models –Sreejita Ghosh, Peter Tino, Kerstin Bunte
Positive and negative parenting in conduct disorder with high versus low levels of callous–unemotional traits -Ruth Pauli, Peter Tino, Jack C. Rogers, Rosalind Baker, Roberta Clanton, Philippa Birch, Abigail Brown, Gemma Daniel, Lisandra Ferreira, Liam Grisley, Gregor Kohls, Sarah Baumann, Anka Bernhard, Anne Martinelli, Katharina Ackermann, Helen Lazaratou, Foteini Tsiakoulia, Panagiota Bali, Helena Oldenhof, Lucres Jansen, Areti Smaragdi, Karen Gonzalez-Madruga, Miguel Angel Gonzalez-Torres, Maider Maider Gonzalez de Artaza-Lavesa, Martin Steppan, Noortje Vriends, Aitana Bigorra, Reka Siklosi, Sreejita Ghosh, Kerstin Bunte, Roberta Dochnal, Amaia Hervás, Christina Stadler, Aranzazu Fernandez-Rivas, Graeme Fairchild, Arne Popma, Dimitris Dikeos, Kerstin Konrad, Beate Herpertz-Dahlmann, Christine M. Freitag, Pia Rotshtein, Stephane A De Brito
Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders –Sreejita Ghosh, Elizabeth S. Baranowski, Rick van Veen, Gert-Jan de Vries, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte

Publications

All in the Name of Artificial Intelligence: A Commentary on Linardon (2025) – an invited commentary for International Journal of Eating Disorder – Pia Burger and Sreejita Ghosh

Interpretable modelling and visualization of biomedical data – Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte

A prototype-based model for set classification –Mohammad Mohammadi and Sreejita Ghosh

Review of Machine Learning solutions for Eating Disorders –Sreejita Ghosh, Pia Burger, Mladena Simeunovic Ostojic, Joyce Maas, Milan Petkovic

Stacking multiple prediction models to optimise performance in local settings: exemplars in cardiometabolic disease (Preprint) –Sreejita Ghosh, Jasmine Gratton, Roel C.H Vermeulen, Folkert W Asselbergs, Jelle J. Vlaanderen, Amand Floriaan Schmidt

Intrinsically Interpretable Machine Learning In Computer Aided Diagnosis (PhD thesis)

Visualisation and knowledge discovery from interpretable models –Sreejita Ghosh, Peter Tino, Kerstin Bunte

Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders –Sreejita Ghosh, Elizabeth S. Baranowski, Rick van Veen, Gert-Jan de Vries, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte