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
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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