Dr. Tsung-Hui (Alex) Huang is currently an Associate Professor in the Department of Mechanical Engineering (ME) at National Taiwan University (NTU), Taiwan. His research spans computational mechanics, with particular emphasis on physics-guided and data-driven machine learning, multiscale modeling, fracture and damage mechanics, and meshfree particle methods. In recent years, his work has expanded to computational methods for semiconductor manufacturing. His research is dedicated to developing innovative computational frameworks and algorithms to address a wide range of engineering challenges. His scholarly contributions have been recognized with several prestigious early-career awards, such as APACM Young Investigator Award, CSME Outstand Young Engineering Professor Award ,and many others. Dr. Huang received his Ph.D. in Structural Engineering from the University of California San Diego (UCSD) in 2020. From 2020 to 2025, he served as an Assistant Professor and later Associate Professor in the Department of Power Mechanical Engineering at National Tsing Hua University, Taiwan. In 2025, he joined National Taiwan University, where he currently holds his present position. For details, please visit https://huan0652.wixsite.com/thhuang, or the website of NTUME: https://www.me.ntu.edu.tw/home.jsp?lang=en

The Advanced Computational Mechanics and Engineering (ACME) Laboratory is dedicated to the research and development of computational methods, algorithms, and their underlying theoretical foundations for solving various engineering problems. The lab integrates three core disciplines: computational mechanics, numerical methods in engineering, and computer science, to develop interdisciplinary technologies that address current societal and industrial needs. In addition to conventional engineering modeling, the lab also focuses on the application and development of machine learning algorithms. Current research directions include: 1. Computational methods for semiconductor processing: Combining computational mechanics modeling with machine learning techniques to address mechanical challenges in semiconductor manufacturing, including process design, packaging, and equipment engineering. 2. Modeling and simulation of extreme engineering events: Developing and improving advanced simulation tools such as meshfree methods to analyze large-scale structural failure and engineering issues arising from extreme events (e.g., man-made or natural disasters), recently we focus more on the extreme modeling of manufacturing process (3D printing). 3. Physics-informed machine learning: Investigating how to incorporate physical laws and mechanical principles into machine learning algorithms by designing physics-guided loss functions or neural network architectures. The aim is to enhance training accuracy, stability, and efficiency while reducing the cost of transfer learning and inference. Application areas include tribology, biomedical materials, smart manufacturing, and 2D semiconductor materials.