Satya Borgohain



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Personal profile


Satya Borgohain is a Research Fellow with SoDa Laboratories (Social Science Insights from Alternative Data) within the Department of Business and Economics where he works in the capacity of both as a Data Science researcher and DevOps engineer. He completed his Masters of Data Science from Monash University and conducted research on bio-plausible, neural network hierarchies along with meta-learning, primarily taking inspiration from the mammalian brain, as part of his minor thesis.

In general, he has a keen interest towards both applied machine learning as well as fundamental deep learning research. His prior experiences include working as a full-stack software developer for two years in the industry where he implemented and deployed predictive models for an in-house analytics platform.

His core areas of interest are:

  • Statistical/Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Audio Deep learning
  • Data engineering

Education/Academic qualification

Data Science, Masters, MONASH UNIVERSITY

Feb 2018Dec 2019

Award Date: 31 Dec 2019

Mechanical Engineering, Bachelors

Research area keywords

  • Machine Learning
  • Deep learning
  • Natural Language Processing
  • Computer Vision
  • Bayesian Neural Networks


Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
  • Self-organising neural network hierarchy

    Borgohain, S., Kowadlo, G., Rawlinson, D., Bergmeir, C., Loo, K., Rangarajan, H. & Kuhlmann, L., 2020, AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Proceedings. Gallagher, M., Moustafa, N. & Lakshika, E. (eds.). 1st ed. Cham Switzerland: Springer, p. 359-370 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12576 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review