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
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
2019 Introduction to Quantitative Biology, IIIT Delhi, India
Akademische Preise und Stipendien
2019 IUSSTF Viterbi Scholarship, University of Southern California, CA, USA
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