Juanling Liang | Automated Machine Learning (AutoML) | Young Scientist Award

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Ms. Juanling Liang | Automated Machine Learning (AutoML) | Young Scientist Award

Student at Guangxi University of Science and Technology, China

Juanling Liang is a graduate student specializing in robotics engineering at Guangxi University of Science and Technology. Currently engaged in research focusing on robotic arm path planning and dynamic obstacle avoidance, Juanling has developed a strong foundation in algorithms such as RRT* and APF. The primary aim of the research is to optimize robotic arm movement in complex environments, with an emphasis on improving the operational efficiency of industrial tasks. Despite being early in his academic career, he has already contributed significantly to the field through his academic paper on robotic arm optimization.

Profile

Orcid

Education

Juanling Liang is pursuing a graduate degree in robotics engineering at Guangxi University of Science and Technology. His academic journey has been centered on understanding the intricate mechanisms of robotic motion and artificial intelligence, with a particular focus on dynamic obstacle avoidance and path planning for robotic arms. His educational background equips him with a solid grasp of both the theoretical and practical applications of robotics in real-world environments, positioning him well for future advancements in the field.

Experience

Although still a student, Juanling Liang has already demonstrated notable progress in the field of robotics. His primary research revolves around the optimization of algorithms such as RRT* and APF, which are essential for improving robotic arm navigation in environments with obstacles. This research not only strengthens his expertise but also shows his commitment to bridging the gap between theoretical models and practical applications, especially in the industrial sector.

Research Interest

Juanling’s research interests are primarily focused on path planning and dynamic obstacle avoidance for robotic arms. He aims to improve the performance of robotic arms in complex environments, where the efficient navigation of obstacles is crucial for productivity and safety. His work involves enhancing existing algorithms to optimize robotic movements, ensuring that robotic arms can operate more effectively in dynamic and cluttered spaces. The ultimate goal is to improve the efficiency of industrial tasks, such as assembly lines, where precision and speed are critical.

Award

Juanling Liang is a nominee for the prestigious Young Scientist Award, recognizing his outstanding contribution to robotics research. His work on optimizing robotic arm path planning has the potential to make significant strides in the efficiency of industrial processes. The award would serve as a recognition of his academic dedication and research contributions, highlighting his potential for future innovations in the field.

Publication

  1. Liang, J. (2024). “Optimization of the RRT* Algorithm for Robotic Arm Path Planning.” Journal of Robotics and Automation, Vol. 1, No. 1.
    Cited by: 12 articles

Conclusion

Juanling Liang is an emerging talent in the field of robotics engineering, with a strong focus on robotic arm path planning and dynamic obstacle avoidance. His work on optimizing algorithms such as RRT* and APF showcases his ability to address complex challenges in robotics, contributing to advancements that have significant real-world applications, especially in industrial settings. With his dedication to research and innovation, Juanling is poised to become a leading figure in robotics, making valuable contributions to the scientific community and the industries relying on robotics technology.

Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

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Assist. Prof. Dr. Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

Assistant Professor at Department of Electronic Engineering, Tsinghua University, China

Quanming Yao is a world-class researcher in the field of machine learning, holding the position of Assistant Professor in the Department of Electronic Engineering at Tsinghua University. With a strong academic background and extensive experience in deep learning, Yao’s research focuses on creating efficient and parsimonious solutions in machine learning, particularly in deep networks and graph learning. His work aims to enhance interpretability in AI models and has led to groundbreaking advancements, such as the development of EmerGNN, the first deep learning model that interprets drug-drug interaction predictions for new drugs. His contributions have significantly impacted both academia and industry, leading to the commercialization of his methods in the AI unicorn 4Paradigm.

Profile

Orcid

Education

Yao earned his Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology (HKUST) between 2013 and 2018. Prior to this, he completed his undergraduate studies at Huazhong University of Science and Technology, where he obtained a degree in Electronic and Information Engineering in 2013.

Experience

Before becoming an assistant professor at Tsinghua University in 2021, Yao worked as a researcher and senior scientist at 4Paradigm Inc. in Hong Kong, from June 2018 to May 2021. In his current academic role, he serves as a Ph.D. advisor, leading research in machine learning and AI, with a specific focus on making deep learning models more efficient and interpretable.

Research Interests

Yao’s research interests revolve around the concept of “parsimonious deep learning,” wherein he explores how simple solutions can lead to substantial improvements in machine learning models. His work is especially notable for its emphasis on automated graph learning methods, which has earned him first place in the Open Graph Benchmark, an equivalent to ImageNet in graph learning. He is also dedicated to the development of deep learning methods that provide interpretable results, particularly in domains like drug discovery, where his innovations have had a direct impact on creating a synthetic biology startup, Kongfoo Technology.

Awards

Yao’s exceptional contributions to the field of machine learning have earned him numerous prestigious awards. These include the Inaugural Intech Prize in 2024, the Aharon Katzir Young Investigator Award in 2023, Forbes 30 Under 30 in the Science & Healthcare Category (China) in 2020, and the Google Ph.D. Fellowship in 2016. He was also recognized as one of the World’s Top 2% Scientists in 2023, highlighting his influence in the global research community.

Publications

Yao has published over 100 papers in top-tier international journals and conferences, with a significant citation record (around 12,000 citations and an h-index of 36). His work includes several landmark papers, such as:

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network, Nature Computational Science, 2023.

AutoBLM: Bilinear Scoring Function Search for Knowledge Graph Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

Efficient Low-rank Tensor Learning with Nonconvex Regularization, Journal of Machine Learning Research (JMLR), 2022.

Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels, Advance in Neural Information Processing Systems (NeurIPS), 2018.

These papers showcase his innovative work in the areas of drug interaction prediction, knowledge graph learning, and robust training of deep neural networks, significantly impacting both theoretical and practical aspects of AI.

Conclusion

Quanming Yao stands out as a leader in machine learning, particularly in deep learning, graph learning, and AI applications in drug discovery. His exceptional academic journey, impactful research, and numerous awards reflect his profound influence in the field. Yao’s contributions to AI are reshaping industries, and his future work promises to continue pushing the boundaries of what is possible with machine learning.