Find Academic Supervisors

• Machine Learning
• Domain Adaptation
• Transfer Learning
• +3 more

Dr. Sinno Jialin Pan is a leading researcher in machine learning and artificial intelligence at Nanyang Technological University, Singapore. His work focuses on domain adaptation, sentiment analysis, and efficient neural network optimization. Key research areas: Machine Learning, Domain Adaptation, Reinforcement Learning, Sentiment Analysis Recent publications demonstrate expertise in time-series classification (2025) using hierarchical domain adaptation, LLM efficiency (2025) through expert pruning, and graph generation (2024) via spectral diffusion. His work spans both theoretical advancements and practical applications in neural architecture optimization and adversarial learning. Scientific contributions include: 2025: Virtual-label hierarchical domain adaptation 2024: Spectral diffusion for graph generation 2024: Multilingual jailbreak analysis in LLMs Current trends show increasing focus on large language model optimization and robust neural architectures , with applications in fault diagnosis, recommender systems, and misinformation detection.

Bin HanView profile

Professor

• Computational Mathematics
• Numerical Partial Differential Equations (PDEs)
• Applied Harmonic Analysis
• +4 more

Bin Han is a Professor of Mathematics at the Department of Mathematical and Statistical Sciences , University of Alberta, Canada. He holds a PhD (1998), MSc (1994), and BSc (1991) in Mathematics from the University of Alberta, Chinese Academy of Sciences, and Fudan University, respectively. Research Interests: Computational Mathematics: High-order finite difference methods, numerical solutions of PDEs (Helmholtz, elliptic interface, Burgers' equations), and Fourier/wavelet-based algorithms. Applied Harmonic Analysis: Framelets/wavelets with applications in image processing, data sciences, and deep learning, focusing on directional and quasi-tight properties. Wavelet Theory: Construction of wavelets on bounded intervals for boundary value problems, Gibbs phenomenon analysis, and stability of refinable functions. Computer Aided Geometric Design (CAGD): Subdivision schemes, spline approximation, and isogemetric analysis. Article Trends: His recent work (2021-2022) emphasizes high-order finite difference methods for Helmholtz and interface problems, directional tensor product complex tight framelets for image processing, and quasi-tight framelets with balancing orders for robustness and sparsity. Scientific Awards: NSERC Postdoctoral Fellowship (1999-2000) Advising & Grants: He has supervised PhD students Qiwei Feng, Michelle Michelle, Ran Lu, and Chenzhe Diao. His research is supported by NSERC, Westgrid, Compute Canada, and MITACS.

Michelle MichelleView profile

Visiting Assistant Professor

• Numerical Methods for Wave Propagation
• Wavelet Methods for PDEs
• Scientific Machine Learning
• +3 more

Michelle Michelle is a Golomb Visiting Assistant Professor of Mathematics at Purdue University, mentored by Jianlin Xia and Haizhao Yang. She holds a PhD in Applied Mathematics from the University of Alberta (2022), advised by Bin Han and Yau Shu Wong. Her research focuses on numerical methods for wave propagation, elliptic interface problems, wavelet methods in PDEs and data science, and scientific machine learning. She has taught courses including MA 303 and MA 266 at Purdue and held teaching roles at the University of Alberta. Her research explores advanced numerical techniques for solving Helmholtz equations and electromagnetic scattering problems, with notable contributions to wavelet-based Galerkin methods and stable fast multipole algorithms. Recent work emphasizes high-order accuracy, stability analysis, and applications in computational physics. Michelle has presented at major conferences such as SIAM Applied Linear Algebra (2024) and SIAM Great Lakes Section (2022), focusing on numerical methods for wave equations and algorithmic stability. She has also contributed to workshops like the NSF Computational Mathematics PI Meeting (2024) and ICERM Modern Applied and Computational Analysis (2023). Her publications span journals like SIAM Journal on Numerical Analysis and Applied and Computational Harmonic Analysis.

Lixin ShenView profile

Professor

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +4 more

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.