جستجوی استاد راهنمای دانشگاهی

Marc Laforestمشاهده پروفایل

دانشیار

• Continuum mechanics
• Hyperbolic conservation laws
• Kinetic equations
• +۶ مورد دیگر

Marc Laforest is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a B.Sc. from UQAM and M.Sc. and Ph.D. degrees from SUNY at Stony Brook. His academic career at Polytechnique Montréal spans multiple decades, during which he has established himself as a researcher in computational mathematics and numerical analysis. Professor Laforest's research interests focus on continuum mechanics, hyperbolic conservation laws, kinetic equations, numerical analysis, multi-scale analysis, a posteriori error estimation, and mesh adaptivity . His work bridges theoretical mathematics with practical applications, particularly in wave propagation and gas mechanics. Recent publications demonstrate increasing integration of machine learning techniques with traditional numerical methods, reflecting evolving trends in computational science. Analysis of his 15 most recent publications reveals a strong trajectory in computational mathematics with growing emphasis on machine learning applications. His research spans pure mathematical theory to practical engineering applications, particularly in superconductivity modeling. The consistent publication rate (2 papers in 2025, 2 in 2024, 2 in 2023, and 5 in 2022) indicates active research programs and likely multiple ongoing grants. Professor Laforest has supervised 5 doctoral students to completion (most recent in 2024) and 9 master's students (most recent in 2023). His students' thesis topics reflect the breadth of his research interests, ranging from wave equation approximation to contact detection methods and superconductivity modeling. This extensive supervision record demonstrates his commitment to graduate education and mentorship. His teaching responsibilities include courses in differential equations, analysis, and numerical analysis , connecting his research expertise with classroom instruction. While specific grant information isn't provided in the available materials, his consistent publication record across multiple high-impact journals suggests successful grant acquisition and management of research funding.

Daniel Pimentel-Alarcónمشاهده پروفایل

استادیار

• Machine Learning
• Biostatistics
• Medical Informatics
• +۳ مورد دیگر

Daniel Pimentel-Alarcón is an Assistant Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison. His research focuses on machine learning and mathematical optimization for biomedical applications, including matrix completion, subspace clustering, and computer vision. He earned his PhD in Electrical and Computer Engineering from the University of Wisconsin–Madison. His work addresses challenges in handling incomplete data through innovative algorithms like Deep-Union Completion and Grassmannian-based visualization techniques. Recent projects explore contrastive learning, neural network architectures, and topological data analysis. For more details, visit his lab's website at https://danielpimentel.github.io .

• Computational Social Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr. Dachun Sun serves as an Assistant Teaching Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), with additional affiliations in the Data Science and Artificial Intelligence programs. Based in Unity Hall 452, he contributes to WPI's academic mission through both teaching innovation and computational social science research. His educational foundation was built entirely at the University of Illinois Urbana-Champaign: Ph.D. in Computer Science M.S. in Computer Science B.A. in Music Technology B.S. in Computer Science Dr. Sun's research sits at the critical intersection of artificial intelligence and societal impact , specializing in computational social science through graph neural networks , large language models , and retrieval-augmented generation . His work analyzes polarization in online communities to detect ideological positions and model responses to real-world events, with direct applications in influence campaign detection and crisis response systems. He simultaneously pioneers the integration of AI tools into computer science education while fostering critical awareness of their ethical implications. His 2023-2024 publications reveal a strong trajectory in temporal reasoning, polarized social graph analysis, and human activity recognition—appearing in top venues like ACM SIGIR and ICCCN. These works demonstrate consistent innovation in knowledge graph reasoning, multimodal representation, and practical AI applications for societal challenges. Dr. Sun actively mentors students through hands-on research opportunities in his computational social science group, emphasizing collaborative problem-solving for real-world issues. His funded projects directly translate research into tools for crisis response planning and misinformation mitigation. Though no formal lab name is specified, his research collective focuses on bridging AI methodology with social analysis, maintaining strong industry and academic connections as evidenced by his multi-institutional publications and upcoming CIKM 2024 tutorial on temporal graph learning.