جستجوی استاد راهنمای دانشگاهی

Helia Farhoodمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Dr. Helia Farhood is an Honorary Senior Research Fellow at the School of Computing, Macquarie University, specializing in Artificial Intelligence, Machine Learning, and Image Processing with applications in educational technology and object recognition. Her academic qualifications include a PhD in Computer Systems and Artificial Intelligence from the University of Technology Sydney (awarded November 2021) and a Master's degree in Computer-AI from Amirkabir University of Technology (Tehran Polytechnic, awarded September 2013). Dr. Farhood's research spans interdisciplinary AI applications, with significant contributions in student outcome prediction using generative adversarial networks, explainable AI through LIME heatmaps, and image-based storytelling systems. Her work integrates machine learning with educational data mining to enhance creativity assessment and learning analytics, while maintaining strong technical focus on 3D reconstruction and object recognition. Analysis of her 16 publications (2020-2025) reveals three dominant research trajectories: (1) AI-driven educational analytics for student performance prediction, (2) advanced image processing techniques for object recognition and 3D reconstruction, and (3) systematic reviews establishing methodological foundations in presentation attack detection and image-based storytelling. Her recent work increasingly emphasizes explainability and ethical considerations in AI deployment. Dr. Farhood has participated in externally funded research projects, including the 2022 project "Estimating the Number of Tyres in Stockpiles" (October-December 2022). No information is available regarding students she has advised. No information is available about specific research laboratories or teams led by Dr. Farhood.

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +۳ مورد دیگر

Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .

Jennyمشاهده پروفایل

استاد

• Turbulent Combustion Modeling
• Computational Fluid Dynamics
• Rarefied Gas Kinetics
• +۴ مورد دیگر

Professor Jenny leads the Jenny Research Group at ETH Zürich, specializing in turbulent reactive flows, rarefied gas kinetics, and biomedical fluid dynamics. Her work bridges fundamental research with industrial applications in energy systems and fluid mechanics. Develops advanced turbulence models (TDDM, hybrid LES/RANS) for multi-scale flows Pioneers data assimilation frameworks for RANS simulations using adjoint methods Advances particle-based stochastic algorithms for fractured porous media transport Her recent publications emphasize adaptive time integration techniques, probabilistic modeling of non-linear transport phenomena, and optimized simulation tools for hydrogen storage systems. The group's methodological innovations focus on reducing computational costs while maintaining physical accuracy through novel regularization strategies. Key applications include combustion device optimization, high-pressure tank filling analysis, and fractured reservoir simulations. Current projects integrate machine learning with traditional CFD methods to address challenges in droplet clustering, flame surface density propagation, and supersonic spray dynamics. The research framework spans from direct numerical simulations of fundamental flow physics to industrial-scale hybrid modeling implementations.

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.