Research work

Also see my Google Scholar and Semantic Scholar profiles:

• Multi hash embeddings in spaCy
  L. J. V. Miranda, Á. Kádár, A. Boyd, S. Van Landeghem, A. Søgaard, and M. Honnibal, “Multi hash embeddings in spaCy”, arXiv:2212.09255 [cs.CL]. 2022.
• Feature Extraction using a Mutually-Competitive Autoencoder for Protein Function Prediction
  L. J. V. Miranda and J. Hu, “Feature Extraction using a Mutually-Competitive Autoencoder for Protein Function Prediction,” IEEE International Conference on System, Man, and Cybernetics, IEEE, October 2018.
• A Deep Learning Approach based on Stacked Denoising Autoencoders for Protein Function Prediction
  L. J. V. Miranda and J. Hu, “A Deep Learning Approach based on Stacked Denoising Autoencoders for Protein Function Prediction,” 42nd IEEE Computer Society Signature Conference on Computers, Software, and Applications, IEEE, July 2018.
• PySwarms, a research-toolkit for Particle Swarm Optimization in Python
  L. J. V. Miranda, “PySwarms, a research-toolkit for Particle Swarm Optimization in Python,” Journal of Open Source Software, vol. 3, no. 433, 2018. doi: 10.21105/joss.00433.
• Appliance Recognition using Hall-Effect Sensors and k-Nearest Neighbors for Power Management Systems
  L. J. V. Miranda, M. J. Gutierrez, S. M. Dumlao, and R. Reyes, “Appliance Recognition using Hall-Effect Sensors and k-Nearest Neighbors for Power Management Systems,” in Proceedings of the 2016 IEEE Region 10 Conference 2016, IEEE, November 2016.
• Expulsion from Eden: the saga of the Calauit Safari Island Park
  L. J. V. Miranda, “Expulsion from Eden: the saga of the Calauit Safari Island Park,” APEIRON Student Journal of Philosophy, no. 8, pp. 201–219, 2016. ISBN: 1533659788.

Masters Thesis

My Masters thesis involves the prediction of protein functions using autoencoder-based techniques. You can find an unpublished version of my manuscript below:

• Autoencoder-based Feature Extraction Techniques for Protein Function Prediction Masters Thesis, Waseda University, Unpublished, June 2018.

In addition, here are some selected works during my time at the Furuzuki Neurocomputing Systems Laboratory (NCLab) in Waseda University. Most of these are unpublished seminar presentations and reports:

• Applying Reinforcement Learning to the Protein Folding Problem Laboratory Seminar Presentation, April 2018.
• Selective Feature Extraction via a Mutually-Competitive Autoencoder for Protein Function Prediction Midterm Defense Presentation, April 2018.
• Feature Extraction using a Stacked Denoising Autoencoder for Protein Function Prediction 11th International Collaboration Symposium on Information, Production, and Systems, November 2017.