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Portrait of Radha Manisha Kopparti

Contact

Postal address

City, University of London
Northampton Square
London
EC1V 0HB
United Kingdom

About

Overview

I'm a PhD Candidate at the Research Centre for Machine Learning at City University of London working in the domain of Neuro-symbolic AI focusing on sequential modeling and abstract relational learning. My research supervisors are Dr. Tillman Weyde and Dr. Artur Garcez.

My research interests span across abstract pattern learning, music information retrieval, systematicity and compositionality of deep neural networks.

Prior to joining here, I finished my Bachelors and Masters in Computer Science from IIIT-Hyderabad, India.

Qualifications

  1. Bachelors and Masters in Computer Science, International Institute of Information Technology, Hyderabad, India, Aug 2011 – Aug 2017

Postgraduate training

  1. Research Intern, Academia Sinica, Taipei, Taiwan, May – Jul 2016
  2. Visiting Undergraduate Researcher, Hong Kong University of Science and Technology, Hong Kong, Hong Kong, Jun – Jul 2014

Employment

  1. Machine Learning Researcher, Rolls Royce Corporation, Aug 2019 – Jan 2020
  2. Graduate Teaching Assistant, City University of London, Sep 2018 – present
  3. Data Scientist, Constellation AI, Jan – Oct 2018

Publications

Conference papers and proceedings (5)

  1. Kopparti, R.M. and Weyde, T. (2019). Weight Priors for Learning Identity Relations. KR2ML, NeurIPS (Neural Information Processing Systems) 8-15 December, Vancouver, Canada.
  2. Kopparti, R.M. (2019). Abstract Rule Based Pattern Learning with Neural Networks. Presented at Machine Intelligence Conference 5-6 August, Boston, Massachusetts.
  3. Kopparti, R. and Weyde, T. (2019). Factors for the Generalisation of Identity Relations by Neural Networks. Understanding and Improving Generalization in Deep Learning, 36th International Conference on Machine Learning (ICML), Long Beach, California, 2019 9-15 June, California, USA.
  4. Kopparti, R.M. and Weyde, T. (2019). Modeling Interval Relations for Neural Language models. Machine Learning for Music Discovery, 36th International Conference on Machine Learning (ICML) 9-15 June, Long Beach, California, USA.
  5. Weyde, T. and Kopparti, R.M. (2018). Feed-Forward Neural Networks Need Inductive Bias to Learn Equality
    Relations.
    Relational and Representation Learning (R2L) @ 32nd Conference on Neural Information Processing Systems (NIPS 2018) 2-8 December, Montreal, Canada.

Journal articles (2)

  1. Weyde, T. and Kopparti, R.M. (2019). Modelling identity rules with neural networks. Journal of Applied Logics, 6(4), pp. 745–769.
  2. Asano, R., Bornus, P., Craft, J.T., Dolscheid, S., Faber, S.E.M., Haase, V. … Vogeley, K. (2018). Spring School on Language, Music, and Cognition. Music & Science, 1, pp. 205920431879883–205920431879883. doi:10.1177/2059204318798831.

Professional activities

Collaboration (industrial)

  1. Researcher of Deep Learning for Semantic Data Mining project (Aug 2019 – Jan 2020)
    Sponsored by Rolls Royce Corporation, United Kingdom