Surabhi S. Nath
Doktorandin (Fast-Track-Kandidatin an der Berlin School of Mind and Brain)
Akademischer Werdegang
| seit 2020 |
Doktorandin, Max Planck School of Cognition, Leipzig Promotionsarbeit am Max-Planck-Institut für Biologische Kybernetik, Tübingen, Betreuer: Peter Dayan Lab-Rotationen in der Orientierungsphase: Peter Dayan, John-Dylan Haynes, and Ralph Hertwig |
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| seit 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 |
Lehre
| 2021 | Mentor, Delhi Women in Machine Learning & Data Science, India |
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| 2019 | Introduction to Quantitative Biology, IIIT Delhi, India |
Akademische Preise und Stipendien
| 2019 | IUSSTF Viterbi Scholarship, University of Southern California, CA, USA |
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| 2019 | Innovation R&D Award, IIIT Delhi, Indien |
| 2016–2019 | Part of Dean's Merit List, IIIT Delhi, Indien |
Publikation
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
Posterpräsentationen und Vorträge
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).
Foto: Anja Schneider