Surabhi S. Nath

Doctoral Candidate

Surabhi.S.Nath@maxplanckschools.de

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, Indraprastha Institute of Information Technology (IIIT) Delhi, India

Teaching

2019 Introduction to Quantitative Biology, IIIT Delhi, India

Poster presentations and talks

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).