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Shangce GAO

Professor, Ph.D., Senior Member of IEEE

Faculty of Engineering,
University of Toyama, Toyama-shi, 930-8555 Japan.

Email: gaosc circle_alpha eng.u-toyama.ac.jp

Research Interests

My main research lies in Artificial Intelligence, Computational Intelligence, and Neural Networks.

Evolutionary computation, particularly meta-heuristics, differential evolution, and evolutionary multi/many-objective optimization
Neural networks and learning systems, particularly deep learning, reinforcement learning, and dendritic neuron models
Evolutionary machine learning, ensemble learning, and transfer learning; Feature extraction, feature construction, and feature learning
Real-world Application, including Internet of vehicles, medical imaging problems, and other NP-hard combinatorial problems

Biography

I received my B.S. degree from Southeast University, Nanjing, China in 2005, and M.E. and D.E. degrees from University of Toyama, Toyama, Japan in 2008 and 2011, respectively. I worked with Tongji University from 2011 as an Associate Professor. From 2014, I am an Associate Professor with the Faculty of Engineering, University of Toyama, Japan, and get promoted to Professor in 2023. My current research interests include nature-inspired technologies, Internet of things, machine learning, and neural networks for real-world applications.

I am a Senior Member of IEEE, and serve as an Associate Editor for many international journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE/CAA Journal of Automatica Sinica, etc. I also serve as a Secretary for Hokuriku Section, IEICE and have served on the program committees for several international professional conferences, including AAAI, NeurIPS, IEEE CEC, etc.

Research Contents

Our laboratory primarily focuses on researching artificial intelligence (AI), with a particular emphasis on deep learning and soft computing technologies. Our pioneering research findings have garnered worldwide recognition. The exceptional information-processing capabilities of the human brain are achieved through intricate interactions among dendrites (synaptic connections) within nerve cells (neurons). By comprehending the principles governing brain operations and designing AI models grounded in cognitive science principles, we aim to foster the development of more efficient and intelligent AI systems.

Deep learning, a leading AI technology, is employed to tackle intricate real-world issues characterized by complex network structures. For example, the GPT-4 model boasts over 500 billion parameters. Solving such intricate real-world problems often hinges on the "big models + big data -> big problems" learning paradigm. Consequently, enhancing the energy efficiency of deep learning is a critical bottleneck impeding its rapid advancement.

Inspired by the learning mechanisms observed in highly intelligent organisms in nature, we have introduced 'evolutionary dendritic learning'. This approach can be fashioned to operate with minimal power consumption, compact models, and limited data. Moreover, we have conducted comprehensive evaluations of this model, encompassing factors like computational power, associative memory, learning efficiency, classification accuracy, and prediction precision. These evaluations are being applied to a multitude of real-world challenges, spanning image recognition, medical imaging (e.g., ultrasound), automatic building safety analysis, cancer classification, and stock market time series prediction.

Honor and Awards

2024-2019, "World Top 2% Scientists" Single-Year Impact Ranking (Versions 2-7), Elsevier BV, Stanford University
2024, "World Top 2% Scientists" Career-Long Impact Ranking (Version 7), Elsevier BV, Stanford University
2024-2023, Best Computer Science Scientists in Japan, Research.com
2024, Bronze Award (3rd place winner), IEEE Congress on Evolutionary Computation (IEEE CEC 2024) Single-Objective Numerical Optimization Competition
2023, Mentorship Award of "the 2022 Chinese Government Award for Outstanding Self-financed Students Abroad"
2023, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Outstanding Associate Editor Award
2023, "The TEACHER" Award, Department of Intellectual Information Engineering, University of Toyama
2023, Outstanding Faculty Award (2022.4-2023.3), University of Toyama
2022, Silver Award (2nd place winner), International Conference on Swarm Intelligence (ICSI) Optimization Competition 2022 (ICSI-OC'2022)
2022, Best Post Presentation Award, Visual Expression and Arts & Sciences Forum 2022
2022, Outstanding Faculty Award (2021.4-2022.3), University of Toyama
2021, Best Presentation Award, 184th Computer Graphics and Visual Informatics (CGVI) 2021
2021, Corporate Award (CyberAgent Award), Visual Computing + VC Communications (VC+VCC) 2021
2021, Best Post Paper Award, Visual Computing + VC Communications (VC+VCC) 2021
2021, Outstanding Associate Editor Award of IEEE/CAA Journal of Automatica Sinica
2020, Best Post Paper Award (2 papers), Visual Computing (VC) 2020
2019, Outstanding Paper Award, The 4th Domestic Conference of Computer Network and Cloud Computing in Jiangsu
2019, Top Peer Review Award, Web of Science Group
2017, Best Oral Paper Award, IEEE 2017 International Conference on Progress in Informatics and Computing
2016, IEEE Senior Memeber
2016, Best Paper Award, IEEE 2016 International Conference on Progress in Informatics and Computing
2015, Best Post Paper Award, IEEE 2015 International Conference on Progress in Informatics and Computing
2014, Shanghai Rising-Star Scientist award
2012, Chen-Guang Scholar of Shanghai award
2011, Outstanding Academic Achievement Award of Information Processing Society of Japan (IPSJ)
2009, Outstanding Self-Financed Students Abroad Award of Chinese Government
2008, Outstanding Academic Performance Award of IEICE