Assistant Lecturer

Luiz Henrique Barbosa Mormille

Profile

Faculty of Science and Engineering Department of Information Systems Engineering

Specialized Field	Artificial Intelligence (AI)
Research theme	Deep Learning
research content	We aim to develop and apply novel regularization techniques to introduce effective inductive biases that lead to better generalization in self-supervised learning. We explore this approach in both computer vision and natural language processing.
Subjects in charge	Programming Exercise I (Spring) Software Seminar B (Autumn) Network Experiment (Autumn)

Main career, work history, and academic background	Education: 1) Graduated from Universidade P. Mackenzie 2) Graduated from the University of University of Sao Paulo Paulo 3) Soka University – Graduate School Graduate School of Science and Engineering Doctoral Program Work History: 1) 2014 - 2017 - Serasa Experian (Sao Paulo, Brazil) 2) 2017 – 2018 - M2Sys Tecnologia e Serviços S/A (Sao Paulo, Brazil) 3) 2018 – 2023 - Editora Brasil Seikyo (Remote)
Affiliated academic societies and organizations	-Information Processing Society of Japan (IPSJ) -The Japanese Society for Artificial Intelligence
Main Papers and Publications	Academic Papers: L. H. Mormille, C. Broni-Bediako, and M. Atsumi, “Regularizing self-attention on vision transformers with　2D spatial distance loss,” Artificial Life and Robotics, vol. 27, pp. 586–593, Aug. 2022. Original paper available at: https://rdcu.be/cRWwe L. H. Mormille, C. Broni-Bediako, and M. Atsumi, “Introducing inductive bias on Vision Transformers　through Gram matrix similarity-based regularization,” Artificial Life and Robotics, vol. 28, pp. 106–116, Jan.　2023. Original paper available at: https://rdcu.be/c2Rvn C. Broni-Bediako, Y. Murata, L. H. B. Mormille and M. Atsumi, “Searching for CNN architectures for remote sensing scene classification”, IEEE Transactions on Geoscience and Remote Sensing, pp. 1–13, 2021. C. Broni-Bediako, Y. Murata, L. H. B. Mormille and M. Atsumi, “Evolutionary NAS for Aerial Image Segmentation with Gene Expression Programming of Cellular Encoding”, Neural Computing and Applications, 2021. Conference presentations: L. H. Mormille, C. Broni-Bediako, and M. Atsumi, “Introducing inductive bias on Vision Transformers through Gram matrix similarity based regularization,” AROB-ISBC-SWARM 2023, January 2023, Beppu, Japan. L. H. Mormille, C. Broni-Bediako, and M. Atsumi, “Regularizing self-attention on vision transformers with 2D spatial distance loss,” AROB-ISBC-SWARM 2022, January 2022, Beppu, Japan. LH Mormille, F, G. Cozman, “Learning Probabilistic Relational Models: A Novel Approach,” 81st Annual Conference of Information Processing Society of Japan, March 2019, Fukuoka, Japan. L. H. Mormille, F, G. Cozman, “Learning probabilistic relational models: A simplified framework, a case study, and a package,” KDMiLe 2017 – Symposium on Knowledge Discovery, Mining and Learning, October 2017, Uberlândia, Brazil. C. Broni-Bediako, Y. Murata, L. H. B. Mormille, and M. Atsumi, “Evolutionary NAS with gene expression programming of cellular encoding”. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). Canberra, Australia, December 1–4, 2020. In Proceedings of 2020 IEEE SSCI, pp. 2670–2676, 2020.

Main career, work history, and academic background

Education:
1) Graduated from Universidade P. Mackenzie
2) Graduated from the University of University of Sao Paulo Paulo
3) Soka University – Graduate School Graduate School of Science and Engineering Doctoral Program
Work History:
1) 2014 - 2017 - Serasa Experian (Sao Paulo, Brazil)
2) 2017 – 2018 - M2Sys Tecnologia e Serviços S/A (Sao Paulo, Brazil)
3) 2018 – 2023 - Editora Brasil Seikyo (Remote)

Affiliated academic societies and organizations

-Information Processing Society of Japan (IPSJ)
-The Japanese Society for Artificial Intelligence

Main Papers and Publications

Academic Papers:

L. H. Mormille, C. Broni-Bediako, and M. Atsumi, “Regularizing self-attention on vision transformers with　2D spatial distance loss,” Artificial Life and Robotics, vol. 27, pp. 586–593, Aug. 2022.
Original paper available at: https://rdcu.be/cRWwe
L. H. Mormille, C. Broni-Bediako, and M. Atsumi, “Introducing inductive bias on Vision Transformers　through Gram matrix similarity-based regularization,” Artificial Life and Robotics, vol. 28, pp. 106–116, Jan.　2023.
Original paper available at: https://rdcu.be/c2Rvn
C. Broni-Bediako, Y. Murata, L. H. B. Mormille and M. Atsumi, “Searching for CNN architectures for remote sensing scene classification”, IEEE Transactions on Geoscience and Remote Sensing, pp. 1–13, 2021.
C. Broni-Bediako, Y. Murata, L. H. B. Mormille and M. Atsumi, “Evolutionary NAS for Aerial Image Segmentation with Gene Expression Programming of Cellular Encoding”, Neural Computing and Applications, 2021.

Conference presentations:

L. H. Mormille, C. Broni-Bediako, and M. Atsumi, “Introducing inductive bias on Vision Transformers through Gram matrix similarity based regularization,” AROB-ISBC-SWARM 2023, January 2023, Beppu, Japan.
L. H. Mormille, C. Broni-Bediako, and M. Atsumi, “Regularizing self-attention on vision transformers with 2D spatial distance loss,” AROB-ISBC-SWARM 2022, January 2022, Beppu, Japan.
LH Mormille, F, G. Cozman, “Learning Probabilistic Relational Models: A Novel Approach,” 81st Annual Conference of Information Processing Society of Japan, March 2019, Fukuoka, Japan.
L. H. Mormille, F, G. Cozman, “Learning probabilistic relational models: A simplified framework, a case study, and a package,” KDMiLe 2017 – Symposium on Knowledge Discovery, Mining and Learning, October 2017, Uberlândia, Brazil.
C. Broni-Bediako, Y. Murata, L. H. B. Mormille, and M. Atsumi, “Evolutionary NAS with gene expression programming of cellular encoding”. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). Canberra, Australia, December 1–4, 2020. In Proceedings of 2020 IEEE SSCI, pp. 2670–2676, 2020.

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