جستجوی استاد راهنمای دانشگاهی

Jiaojiao Jiangمشاهده پروفایل

مدرس ارشد

• AI for Cybersecurity
• Misinformation Detection
• Network Analysis
• +۳ مورد دیگر

Dr. Jiaojiao Jiang is a Senior Lecturer in the School of Computer Science and Engineering at the University of New South Wales (UNSW). She holds a Ph.D. from Deakin University (Melbourne, Australia) and has published over 45 articles with 1,100+ citations. Her research focuses on AI-driven cybersecurity solutions, particularly misinformation detection and modeling information propagation dynamics. She is affiliated with UNSW's Sydney campus and can be contacted at jiaojiao.jiang@unsw.edu.au . Education: Ph.D., Deakin University, 2010s Research Interests: Artificial Intelligence applications in cybersecurity Misinformation detection and network analysis Machine learning for network security Data privacy in IoT systems Publications span topics like fake news detection via graph neural networks, multiplex network robustness, and cyber threat intelligence frameworks. Her work bridges theoretical network science with practical cybersecurity challenges.

• Machine Learning
• Signal Processing
• Biomedical Engineering
• +۷ مورد دیگر

Professor Danilo Mandic is a leading academic in Machine Intelligence and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering. He holds roles including President of the International Neural Network Society and Distinguished Lecturer for IEEE Computational Intelligence and Signal Processing Societies. His research spans Statistical Learning, Wearable Sensing (Hearables), Financial Signal Processing, and Tensor Networks for Big Data. Key contributions include pioneering in-ear physiological sensing and developing quaternion-based adaptive filters. He has authored over 600 publications, including seminal monographs on neural networks and complex-valued signal processing. Education: PhD in Nonlinear Adaptive Signal Processing from Imperial College (1999). Professional accolades include the 2019 Dennis Gabor Award and multiple IEEE Best Paper Awards. His labs include the Financial Signal Processing & Machine Learning Lab and collaborations with the Centre for Neurotechnology. He advises numerous students and leads projects on AI ethics, graph signal processing, and biomedical applications. His work emphasizes translating research into educational curricula via participatory sensor-based learning.

Taylor Johnsonمشاهده پروفایل

دانشیار

• Formal Verification
• Cyber-Physical Systems (CPS)
• Artificial Intelligence
• +۷ مورد دیگر

Taylor Johnson is an Associate Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and serves as a Senior Research Scientist in the Institute for Software Integrated Systems. Previously, he was an Assistant Professor at the University of Texas at Arlington from 2013 to 2016. His research focuses on formal verification techniques for cyber-physical systems (CPS), emphasizing safety, reliability, and security through hybrid systems, formal methods, and control theory. He has published extensively on neural network verification, earning best paper awards and recognition from IEEE, IFIP, and ACM. Education: Ph.D., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2013) M.Sc., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2010) B.S.E.E., Electrical and Computer Engineering (Rice University, 2008) Research Interests: Formal verification of neural networks and CPS, safety-critical systems, autonomous systems, and AI/ML security. His work bridges theoretical foundations (e.g., hybrid systems) with practical applications in aerospace, energy systems, and robotics. Key Contributions: Developed the NNV tool for neural network verification, led the Verification of Neural Networks Competition (VNN-COMP), and pioneered techniques for robust federated learning and malware detection. Awards: AFOSR YIP Award (2016), NSF CRII Award (2015), and multiple best paper honors. His research is funded by AFRL, NSF, Intel, NVIDIA, and industry partners. Labs & Collaborations: VeriVITAL Lab (Vanderbilt), collaborations with United Technologies Research Center, Boeing, and Toyota.

Florian Knollمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )

Mahdi Boloursaz Mashhadiمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Wireless Communications
• +۶ مورد دیگر

Mahdi Boloursaz Mashhadi is a Lecturer in Communications and AI at the Institute for Communication Systems (ICS), part of the School of Computer Science and Electronic Engineering at the University of Surrey, UK. He is a Surrey AI Fellow and an IEEE Senior Member, with a focus on advancing AI-driven wireless communication systems. He holds B.S., M.S., and Ph.D. degrees in mobile telecommunications from Sharif University of Technology, Tehran, Iran. Prior to joining Surrey, he served as a postdoctoral research associate at Imperial College London’s Intelligent Systems and Networks (ISN) Research Group (2019–2021). His research interests span AI/ML integration with wireless systems, including semantic communications, federated learning, generative AI for telecom, and beamforming optimization in massive MIMO architectures. He also explores edge computing, distributed deep learning frameworks, and energy-efficient communication designs using reconfigurable intelligent surfaces (RIS). Mahdi has been recognized with the IEEE EWDTS Best Paper Award and IEEE ComSoc Exemplary Reviewer Awards (2021–2022). He leads the UKTIN/DSIT 12M£ national project TUDOR and collaborates with industry on cutting-edge 5G/6G innovations. He has contributed to the ITU’s AI/ML in 5G challenge as a panel judge and serves as an editor for Springer’s Wireless Personal Communications Journal. His roles include advising on government/industry projects and advancing interdisciplinary research at the 5G/6G Innovation Centre. He emphasizes practical implementations of AI in telecommunications, aiming to bridge theoretical advancements with real-world applications.

Lena Funckeمشاهده پروفایل

استادیار

• Quantum Computing
• Theoretical Physics
• Lattice Field Theory
• +۳ مورد دیگر

Lena Funcke is an Assistant Professor of Theoretical Physics at Bonn University. Her research focuses on quantum computing, lattice field theory, and machine learning applications in physics. She explores topics such as topological phases, gauge theories, and quantum simulations. Her work bridges high-energy physics and computational methods, with a particular emphasis on overcoming noise challenges in quantum algorithms and leveraging machine learning for optimization tasks. Funcke’s research projects include C01 and C03, focusing on Hamiltonian lattice formulations and quantum computing methods for gauge theories. She investigates hybrid approaches combining Monte Carlo simulations with quantum computing to study quantum electrodynamics and topological systems. Her contributions highlight the interplay between theoretical physics and cutting-edge computational tools. Her publications span quantum algorithms for particle physics experiments, error mitigation strategies, and the application of normalizing flows to complex systems like the Hubbard model. She actively contributes to advancing the theoretical foundations of quantum computing and its practical implementation in solving fundamental physics problems.

Eugene Vinitskyمشاهده پروفایل

استادیار

• Reinforcement Learning
• Multi-agent Learning
• Autonomous Vehicles
• +۲ مورد دیگر

Eugene Vinitsky is an Assistant Professor at NYU Tandon School of Engineering, holding joint appointments in Civil and Urban Engineering and Computer Science. His research develops multi-agent reinforcement learning systems for autonomous vehicles and traffic control, with applications in robotics and intelligent infrastructure. He directs the Computational Transportation Systems Lab and leads projects like CIRCLES on congestion reduction. Research Focus: Designs algorithms enabling complex behaviors through unsupervised agent interactions, human-AI compatibility, and environment synthesis for autonomous systems. Awards & Leadership: NSF Graduate Fellow (2016), Eisenhower Fellow (2018, 2020), and PI on multiple grants including Amazon Research Awards. Mentored 14+ graduate students and organized international RL conferences.

Xuhui Fanمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Bayesian Methods
• +۵ مورد دیگر

Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.

Ahmed Hemaniمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• Hardware Acceleration
• +۵ مورد دیگر

Professor Ahmed Hemani is a faculty member at the Division of Electronics and Embedded Systems, KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty. He holds the role of PI for the project 'New Chip Architectures for Industrial Vision' and leads research in reconfigurable computing, memristor-based systems, and hardware acceleration for AI and edge computing. His work bridges theoretical computer science with practical VLSI design and embedded systems development. He actively contributes to cross-disciplinary initiatives at Digital Futures, a joint center with Stockholm University and RISE Research Institutes of Sweden focused on digital innovation. His research emphasizes scalable FPGA/HPC architectures, low-power neuromorphic systems, and optimization techniques for custom silicon solutions. Current projects include a Lego-inspired edge AI framework and memristor-driven MIMO acceleration. Teaching responsibilities span advanced courses in SOC design, digital system verification, and embedded systems. He supervises advanced-level degree projects across computer engineering and ICT innovation specializations, emphasizing hands-on hardware-software co-design methodologies. Recent publications highlight innovations in memristor applications, FPGA-based acceleration, and reconfigurable architectures for neural networks and bioinformatics. His work addresses challenges in dark silicon utilization, energy-efficient computation, and high-performance embedded systems.

Edriss S. Titiمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Applied Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Edriss S. Titi is a University Distinguished Professor and Arthur Owen Professor of Mathematics at Texas A&M University within the College of Arts & Sciences. His research focuses on nonlinear partial differential equations, applied mathematics, and geophysical fluid dynamics. He leads studies on fluid mechanics, atmospheric and oceanic dynamics, data assimilation, and control theory. His work often addresses mathematical rigor in modeling complex systems like climate dynamics and turbulent flows. Research Interests: Nonlinear PDEs and their applications Fluid dynamics and turbulence Data assimilation algorithms Climate and ocean modeling Infinite-dimensional dynamical systems Recent publications emphasize Navier-Stokes equations , primitive equations , and data assimilation in chaotic systems . His methodologies bridge theoretical analysis and computational modeling, with applications to weather prediction and geophysical flows. Collaborations include the Institute for Applied Mathematics and Computational Science (IAMCS) at Texas A&M. Notable contributions include rigorous analysis of global well-posedness for oceanic models and development of CDAnet, a physics-informed deep learning framework for fluid flow downscaling.

Xianyang Zhangمشاهده پروفایل

استاد

• High-dimensional statistics
• Functional data analysis
• Time series analysis
• +۸ مورد دیگر

Xianyang Zhang is a Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2013) and a B.S. from the University of Science & Technology of China (2008). His research focuses on high-dimensional statistics, functional data analysis, kernel methods, and genomics, supported by grants from NIH, NSF, and Texas A&M. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2013 B.S., Statistics, University of Science & Technology of China, 2008 Research Interests: Xianyang Zhang develops statistical theories and methodologies for complex data structures, including high-dimensional inference, kernel-based testing, change-point detection, and microbiome analysis. His work bridges computational and theoretical statistics, addressing challenges in genomics, omics-wide studies, and spatial statistics. Key Contributions: Developed KDist , a package for kernel and distance-based statistical inference Authored fastcpd for efficient change-point detection Advanced covariate-adaptive FDR control methods for omics studies Contributed to microbiome analysis tools like MicrobiomeStat and LinDA Advising & Grants: Advises multiple Ph.D. students in statistics and interdisciplinary projects Recipient of NIH and NSF grants for high-dimensional statistical research Collaborates with institutions like Mayo Clinic and Chinese University of Hong Kong Labs/Teams: Leads research groups focused on statistical methodology development, software implementation, and applications in computational biology and genomics.

Ji Shuiwangمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence for Science and Engineering
• Language Models and Agents
• +۳ مورد دیگر

Shuiwang Ji is a Professor and Truchard Family Endowed Chair in the Department of Computer Science & Engineering at Texas A&M University, where he also holds Presidential Impact Fellow and Chancellor EDGES Fellow titles. He specializes in machine learning, AI for science/engineering, and language models/agents. His research bridges theoretical advances and practical applications in materials science, quantum chemistry, and biomedical engineering. Education: Ph.D. in Computer Science from Arizona State University (2010). Research focuses on equivaraint neural networks for symmetry-aware learning, graph-based molecular modeling, and generative AI for scientific discovery. He develops algorithms that integrate physics principles with deep learning, addressing challenges in materials design, PDE solving, and biomolecular structure prediction. Publications emphasize symmetry-aware architectures (e.g., equivariant Fourier neural operators), efficient interatomic potential computations, and diffusion models for protein/DNA design. Recent work explores trustworthiness in LLMs and causal reasoning in graph neural networks. Awards include NSF CAREER Award (2014), IEEE Fellow (2023), and Texas A&M teaching excellence awards. His work has been recognized in top venues like NeurIPS, ICML, and ICLR. His research group collaborates on projects funded by NSF, NIH, and industry partners, advancing AI applications in healthcare, robotics, and environmental science.

Hazem U. Abdelhadyمشاهده پروفایل

پژوهشگر ارشد

• Coastal Processes
• Hydrodynamic Modeling
• Remote Sensing
• +۴ مورد دیگر

Hazem U. Abdelhady is a Postdoctoral Research Fellow at the University of Michigan's School for Environment and Sustainability (SEAS), jointly appointed with the Cooperative Institute for Great Lakes Research (CIGLR). He will join Texas A&M University's Department of Geography as an Assistant Professor in Fall 2025. His research focuses on coastal and hydrodynamic processes, leveraging machine learning, remote sensing, and physics-based modeling to address climate-driven challenges in lakes and coastal systems. Dr. Abdelhady holds a PhD from Purdue University in Hydraulics and Hydrology Engineering and Computational Engineering (2024), an MS in Irrigation and Hydraulics Engineering from Cairo University (2020), and a BS in Civil Engineering (2018). His postgraduate certificate in Geospatial Information Sciences underscores his expertise in spatial data analysis. His research interests include coastal hydrodynamics, physical limnology, and AI-driven environmental modeling. Key projects involve understanding shoreline changes in Lake Michigan, predicting ice cover dynamics, and developing tools for climate adaptation. The Aggie CIS Lab , launching at Texas A&M in Fall 2025, will further advance these efforts, bridging disciplines to enhance coastal resilience. Recent work highlights include studies on climate impacts on lake temperatures, machine learning applications for wave modeling, and automated shoreline detection algorithms. He actively recruits PhD students for Fall/Spring 2026 to investigate Great Lakes dynamics and resilience strategies. Collaborations span academic and governmental institutions, emphasizing interdisciplinary approaches to environmental challenges. His publications span high-impact journals and conferences, reflecting contributions to both theoretical and applied aspects of water resources engineering.

Giovanna Tinettiمشاهده پروفایل

استاد

• Extrasolar Planets
• Space Science
• Molecular Spectroscopy
• +۳ مورد دیگر

Giovanna Tinetti is a Professor of Astrophysics and Vice Dean (Research) at King's College London's Faculty of Natural, Mathematical & Engineering Sciences. She leads the European Space Agency's Ariel mission, a space telescope surveying exoplanet atmospheres, set to launch in 2029. As co-founder of the London Centre for Space Exochemistry Data and Blue Skies Space Ltd, she pioneers satellite technology for scientific data collection. She holds a PhD in Theoretical Physics from the University of Turin, with prior affiliations at Caltech/JPL, the Institute of Astrophysics in Paris, and University College London (UCL), where she was a Royal Society University Research Fellow. Her research focuses on exoplanetary atmospheres, molecular spectroscopy, and advanced data science techniques. With over 300 publications, her 2019 paper on water vapor in K2-18b's atmosphere achieved the highest altmetric score in Physical Sciences that year. She has delivered over 350 international talks and lectures. Education: PhD in Theoretical Physics (University of Turin) Affiliations: King's College London, UCL (past), ESA's Ariel Mission, Blue Skies Space Ltd Research Interests: Exoplanet atmospheres, molecular spectroscopy, space science, data-driven analysis methodologies, and atmospheric modeling. Her work bridges observational astronomy with computational chemistry to interpret exoplanet compositions and climates. Awards: Royal Society University Research Fellow Highest Altmetric Score (2019 Physical Sciences) Grants & Projects: Principal Investigator for ESA's Ariel mission Co-leader of the Ariel Data Challenge 2025 Labs/Teams: London Centre for Space Exochemistry Data, Blue Skies Space Ltd technical team, and the international Ariel collaboration network.

• Explainable AI
• Human-AI Interaction
• Machine Learning
• +۷ مورد دیگر

Andreas Holzinger is a Professor at Graz University of Technology, with additional affiliations at Medical University Graz and University of Natural Resources and Life Sciences Vienna in Austria. He is recognized as an IFIP Fellow (2021) for his significant contributions to information processing and computer science. His work spans multiple institutions across Europe, with notable collaborations extending to the University of Alberta in Canada. Professor Holzinger's research focuses on Human-Centered AI, Explainable AI (XAI), and their practical applications across diverse domains. His work bridges theoretical AI advancements with real-world implementations in healthcare, forestry, and human-robot interaction. He has pioneered approaches in counterfactual explanations, graph neural networks, and human-in-the-loop systems that emphasize transparency and trustworthiness in AI decision-making processes. His recent publications demonstrate a strong trend toward integrating large language models with traditional AI systems while maintaining explainability. Holzinger's work consistently emphasizes the human element in AI systems, ensuring that technological advancements serve human needs rather than obscuring decision processes. His research in medical AI, smart forestry, and agricultural applications shows a commitment to solving practical problems with human-centered technological solutions. Scientific Awards: IFIP Fellow (2021) Professor Holzinger has been instrumental in establishing design guidelines for explainable AI systems, particularly through his work on post-hoc versus ante-hoc explanations. His research on Kandinsky Patterns has provided valuable experimental frameworks for pattern analysis and machine intelligence. He has secured significant research funding for projects bridging AI with practical applications in healthcare and environmental monitoring. His leadership extends to the organization of major conferences and workshops, including the CD-MAKE conference series, where he has fostered interdisciplinary collaboration between AI researchers and domain experts. His work on the CLARUS platform demonstrates practical implementations of interactive explainable AI for medical applications.