Bin Liu | Machine Learning in Physics | Best Researcher Award

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Prof. Bin Liu | Machine Learning in Physics | Best Researcher Award

E-Surfing Digital Life Co., Limited | China

Bin Liu is a renowned expert in the fields of AI, deep learning, Bayesian methods, reinforcement learning, and embodied intelligence. He holds a Ph.D. in Signal and Information Processing from the Chinese Academy of Sciences and has contributed significantly to the development of AI algorithms with cross-disciplinary applications spanning robotics, physics, brain-computer interfaces, and more. Currently, he serves as the Chief Robot Expert at E-Surfing Digital Life Technology Co. Ltd., China Telecom Group, and continues to lead impactful research in AI and robotics.

👨‍🎓 Profile

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Early Academic Pursuits 🎓

Bin Liu began his academic journey at Beijing University of Posts and Telecommunications, earning a Bachelor’s degree in Automation. His deep interest in signal processing led him to pursue a Ph.D. at the Chinese Academy of Sciences under the mentorship of Prof. Chaohuan Hou, an IEEE Fellow and Academician of the Chinese Academy of Sciences. His doctoral research laid the foundation for his future contributions to AI and computational methods.

Professional Endeavors 💼

Bin Liu has held various prestigious positions in leading tech and academic institutions. His roles include Senior Research Fellow in AI at Midea Group, Team Leader at Zhejiang Lab, and Senior Algorithm Expert at Alibaba Group. He has also served as Associate Professor at Nanjing University of Posts and Telecommunications (NUPT) and Visiting Faculty at institutions such as Carnegie Mellon University and Duke University. His leadership positions reflect his vast influence in shaping AI research and development.

Contributions and Research Focus 🔬

Bin Liu’s research has made remarkable strides in deep learning, Bayesian inference, reinforcement learning, and robotics. He is currently focused on the development of large pre-trained AI models and embodied robot systems. His work on reinforcement learning algorithms and dynamic multi-model ensembling has contributed to solving complex AI challenges, particularly in robotics and automation.

Academic Cites 📚

Bin Liu’s research has garnered widespread recognition in the academic community, with numerous citations on platforms like Google Scholar, ResearchGate, and DBLP. His work is frequently referenced in AI conferences such as ICML, ICLR, NeurIPS, and CVPR, solidifying his stature in the global AI research community.

Research Skills 🔧

Bin Liu possesses a broad skill set, including expertise in statistical modeling, Bayesian statistics, deep learning algorithms, and robotics. His multidisciplinary approach allows him to tackle complex problems by integrating knowledge from areas such as optimization, signal processing, and reinforcement learning. He has demonstrated the ability to transform theoretical models into practical, scalable solutions for real-world applications.

Teaching Experience 📖

Beyond research, Bin Liu is deeply committed to education. He holds Adjunct Professorships at prestigious institutions like Zhejiang University and the Institute of Software, Chinese Academy of Sciences. As a Guest PhD Advisor, he mentors aspiring researchers, guiding them through advanced topics in AI and machine learning. His teaching and mentoring contribute to the next generation of AI experts and robotics innovators.

Awards and Honors 🏆

Bin Liu’s contributions to science and technology have earned him numerous accolades, including:

  1. MIT Technology Review Intelligent Computing Annual Innovator in China (April 2024)
  2. CVPR’23 SoccerNet Challenge Runner-Up (June 2023)
  3. Best Paper Award, ICACI 2018
  4. Research Achievement Award by APSCIT (2017)
  5. High-level Talent in Hangzhou City (2020)

These awards highlight his exceptional innovation and leadership in AI and technology.

Legacy and Future Contributions 🌟

Bin Liu’s long-term contributions continue to shape the trajectory of AI research and robotics. His focus on large pre-trained models and embodied AI systems will likely lead to significant breakthroughs in automation, robot-human interaction, and AI-enabled industries. His ongoing work is poised to make lasting impacts on how we integrate AI into everyday life, making him a pioneer in the evolving field of AI and robotics.

  Publications Top Notes

Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations

  • Authors: Zhengqi Zhang, Jing Li, Bin Liu
    Journal: Journal of Computational Physics
    Year: 2025

FADS: Fourier-Augmentation Based Data-Shunting for Few-Shot Classification

  • Authors: Shuai Shao, Yan Wang, Bin Liu, Weifeng Liu, Yanjiang Wang, Baodi Liu
    Journal: IEEE Transactions on Circuits and Systems for Video Technology
    Year: 2024

Stochastic Weight Averaging Revisited

  • Authors: Hao Guo, Jiyong Jin, Bin Liu
    Journal: Applied Sciences
    Year: 2023

Robust Dynamic Multi-Modal Data Fusion: A Model Uncertainty Perspective

  • Authors: Bin Liu
    Journal: IEEE Signal Processing Letters
    Year: 2021

A Survey on Trust Modeling from a Bayesian Perspective

  • Authors: Bin Liu
    Journal: Wireless Personal Communications
    Year: 2020

 

 

Zhiqing Bai | Machine Learning in Physics | Best Researcher Award

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Ms. Zhiqing Bai | Machine Learning in Physics | Best Researcher Award

Suzhou Institute of Nano-Tech and Nano-Bionics,CAS | China

👨‍🎓 Profile

Early Academic Pursuits 🎓

Ms. Zhiqing Bai began her academic journey with a strong foundation in Textile Engineering at Donghua University, where she completed both her Master’s (2016–2018) and PhD (2018–2023) studies. Her interest in fiber sensing and wearable technology developed early on, which became the focus of her later research. Her expertise expanded as he pursued joint PhD studies in Electrical and Computer Engineering at the National University of Singapore from 2021 to 2022, broadening her understanding of smart materials and energy harvesting systems.

Professional Endeavors 🔬

Since October 2023, Bai has been serving as a Research Fellow at the Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences (CAS). Her work spans multiple innovative fields, including fiber sensing, functional iongels, tactile sensors, and the development of wearable intelligent perception systems. Bai’s research has earned recognition through various academic leadership roles, including leader positions for numerous prestigious national research projects, such as the China National Postdoctoral Program and the National Natural Science Foundation of China.

🔬 Contributions and Research Focus

Zhiqing Bai’s research is centered on advancing triboelectric nanogenerators and interactive sensing technologies. Her pioneering work includes:

  • Development of eco-friendly nanocomposite fabrics for energy harvesting.
  • Creation of polyionic ecological skins for robust self-powered sensing.
  • Exploring bionic e-skin for enhanced robotic perception.

Impact and Influence 🌟

Bai’s work has significantly advanced the fields of wearable electronics and energy harvesting, with a strong focus on improving user interaction and sensor capabilities. Her designs for biocomposite materials and eco-friendly wearable technologies are paving the way for the next generation of smart textiles. Bai’s research has already influenced both academia and industry, attracting numerous citations and establishing her as a leading innovator in functional textiles.

Academic Cites 📚

Her research has resulted in numerous high-impact papers, with many published in journals such as Nano Energy, Advanced Functional Materials, and ACS Applied Materials & Interfaces. Bai’s work has been widely cited in the fields of triboelectric nanogenerators and wearable electronics, cementing her influence in the scientific community. Her contributions to multi-directional droplet sliding sensing and bionic e-skin technology have set the foundation for future developments in robotic perception and wearable devices.

Technical Skills 🛠️

Bai’s technical expertise encompasses fiber sensing, triboelectric nanogenerators (TENGs), polymeric materials, wearable sensors, and sustainable materials. She has extensive experience in designing and fabricating stretchable electronics, transparent power sources, and eco-friendly nanocomposites. Her ability to integrate interdisciplinary knowledge, including electrical engineering, textile engineering, and material science, makes her a standout researcher in the field of smart textiles and wearable technologies.

Teaching Experience 📚

Throughout her academic career, Bai has gained significant teaching experience, particularly in her role as a Research Assistant at the Suzhou Institute of Nano-Tech and Nano-Bionics. In this capacity, she has mentored graduate students and contributed to academic seminars, sharing her expertise on energy harvesting and wearable sensor systems. Bai’s role as a leader in various national research projects also involves providing guidance to young researchers, helping them grow and succeed in cutting-edge fields.

Top Noted Publications

Constructing high-efficiency stretchable-breathable triboelectric fabric for biomechanical energy harvesting and intelligent sensing
  • Authors: Xu, Y.; Bai, Z.; Xu, G.
    Journal: Nano Energy
    Year: 2023
Constructing a versatile hybrid harvester for efficient power generation, detection and clean water collection
  • Authors: Xu, Y.; Bai, Z.; Xu, G.; Shen, H.
    Journal: Nano Energy
    Year: 2022
Constructing highly tribopositive elastic yarn through interfacial design and assembly for efficient energy harvesting and human-interactive sensing
  • Authors: Bai, Z.; He, T.; Zhang, Z.; Xu, Y.; Zhang, Z.; Shi, Q.; Yang, Y.; Zhou, B.; Zhu, M.; Guo, J. et al.
    Journal: Nano Energy
    Year: 2022
Elastic Textile Threads for Fog Harvesting
  • Authors: Nguyen, L.T.; Bai, Z.; Zhu, J.; Gao, C.; Zhang, B.; Guo, J.
    Journal: Langmuir
    Year: 2022
Enhancing Fog Harvest Efficiency by 3D Filament Tree and Elastic Space Fabric
  • Authors: Nguyen, L.T.; Bai, Z.; Zhu, J.; Gao, C.; Luu, H.; Zhang, B.; Guo, J.
    Journal: ACS Sustainable Chemistry and Engineering
    Year: 2022