Surabhi S. Nath
Doctoral Candidate
Academic education
since 2020 Doctoral candidate, Max Planck School of Cognition, Leipzig, Germany
Doctoral research performed at Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Supervisor: Peter Dayan
Lab rotations in the orientation phase: Peter Dayan, John-Dylan Haynes, and Ralph Hertwig
since 2020 Master of Science, Berlin School of Mind & Brain, Humboldt-Universität zu Berlin, Germany
2016–2020 Bachelor of Technology in Computer Science and Engineering with minor in Computational Biology, Indraprastha Institute of Information Technology (IIIT), Delhi, India
Teaching
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
Publication
Jin, H., Nath, S. S., Schneier, 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. https://doi.org/10.1016/j.jbi.2021.103913
Poster presentations and talks
Nath, S. S., & Pachur, T. (2021, August). The affect gap in risky choice with positive outcomes [Poster]. "Subjective Probability Utility & Decision Making", Warwick.
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).
Photo: Anja Schneider