Surabhi S. Nath

Surabhi S. Nath

Doctoral Candidate
Research interests: • computational/probabilistic modelling of cognitive processes • emotions in decision-making • affective neuroscience • learning and improvisation • deep learning, reinforcement learning


Academic education

since 2020   Doctoral candidate, Max Planck School of Cognition, Leipzig
                       Doctoral research performed at Max Planck Institute for Biological Cybernetics, Tübingen
                       Supervisor: Peter Dayan

since 2020   Master of Science, Berlin School of Mind & Brain, Humboldt-Universität zu Berlin

2016–2020  Bachelor of Technology in Computer Science and Engineering with minor in Computational Biology, Indraprastha Institute of Information Technology (IIIT), Delhi, India



2021  Mentor, Delhi Women in Machine Learning & Data Science, India
2019  Introduction to Quantitative Biology, IIIT Delhi, India


Academic awards and scholarships

2019              IUSSTF Viterbi Scholarship, University of Southern California, CA, USA
2019              Innovation R&D Award, IIIT Delhi, India
2016–2019  Part of Dean's Merit List, IIIT Delhi, India


Poster presentations and talks

Nath, S. S., & Pachur, T. (2021). The affect gap in risky choice with positive outcomes. Poster presented at "Subjective Probability Utility & Decision Making", Warwick, UK, 22-24 August 2021.

Nath, S. S., Udandarao, V., & Shukla, J. (2021). It’s LeVAsa not LevioSA! Latent encodings for valence-arousal structure alignment. In 8th ACM IKDD CODS and 26th COMAD (pp. 238–242).

Nath, S. S. (2020). Hear her fear: Data sonification for sensitizing society on crime against women in India. In IndiaHCI'20: Proceedings of the 11th Indian Conference on Human-Computer Interaction (pp. 86–91).

Nath, S. S., Jolly, B. L. K., Aggrawal, P., Gupta, V., Grover, M. S., & Shah, R. R. (2019). Universal EEG encoder for learning diverse intelligent tasks. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM, pp. 213–218). IEEE.

Nath, S. S., Mukhopadhyay, D., & Miyapuram, K. P. (2019). Emotive stimuli-triggered participant-based clustering using a novel split-and-merge algorithm. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data (pp. 277–280).


Jin, H., Nath, S. S., Schneider, S., Junghaenel, D., Wu, S., & Kaplan, C. (2021). An Informatics Approach to Examine Decision-Making Impairments in the Daily Life of Individuals with Depression. Journal of Biomedical Informatics, 122:103913. doi:10.1016/j.jbi.2021.103913


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Photo: Anja Schneider

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