Development Directions

Development Directions

From 2026 to 2030, the Department of Mathematics at National Taiwan Normal University will focus on three major directions: academic research, talent cultivation, and international collaboration. In research, the department will continue integrating pure and applied mathematics, with emphasis on optimization, machine learning, and scientific computing, thereby enhancing research capacity and responding to technological and societal needs. In talent cultivation, the department plans to deepen curriculum content, strengthen students’ mathematical foundations and interdisciplinary skills, and promote innovative teaching. Faculty and students will be encouraged to participate in research projects and international competitions, nurturing professionals with a global vision. Furthermore, the department will actively expand international exchanges through conferences and collaborative platforms, enhancing academic influence and fostering diverse cooperation. In celebration of its 80th anniversary in 2026, a series of events will unite alumni and faculty, showcasing the fusion of tradition and innovation. Overall, the five-year strategy balances academic depth with social engagement, aiming to advance research, teaching, and internationalization, and to solidify the department‘s position both domestically and globally. 

The department has outlined six key directions: 

  1. Core idea: Combine computational mathematics for algorithm stability, optimization theory for model training and generalization, and statistical inference for uncertainty analysis. 
  2. Research includes optimal transport geometric mapping for tumor imaging, multimodal fusion for Alzheimer’s diagnosis, and spatio-temporal statistical modeling for infectious diseases. 
  3. Strategies: Collaborate with medical centers, standardize data preprocessing, and promote open-source reproducibility. 
  1. Focus on structure-preserving algorithms, geometric computing, and AI-assisted simulators. 
  2. Applications include UAV control, medical image registration, and engineering design. 
  3. Strategies: phased resource acquisition, containerized deployment, open-source ecosystem, and industry collaboration. 
  1. Explore mathematical literacy in the AI era, AI-assisted teaching, and the philosophical context of mathematics in technology. 
  2. Develop interactive teaching platforms and AI-based educational systems. 
  3. Strategies: introduce AI modules in teacher training, build teaching robots, and establish experimental platforms. 
  1. Promote research in geometric analysis, variational methods, and PDEs. 
  2. Applications in medical imaging, fluid dynamics, and high-dimensional data. 
  3. Strategies: host international seminars, attract global graduate students, and foster interdisciplinary collaboration. 
  1. Strengthen algebraic and enumerative combinatorics, with applications in cryptography, coding theory, big data, and quantum computing. 
  2. Strategies: interdisciplinary courses, research groups, and symbolic computation tools (SageMath, GAP, Magma). 
  1. Focus on arithmetic geometry, p-adic algebraic geometry, and algebraic dynamical systems. 
  2. Expand into computational algebra and information security. 
  3. Strategies: recruit young scholars, modernize curricula, and establish a “Algebra and Applications” research cluster.