جست‌وجوی استاد و پژوهشگر

Mason Porterمشاهده پروفایل

استاد

• Network Science
• Social Dynamics
• Nonlinear Dynamics
• +۴ مورد دیگر

Mason Porter is a Professor in the Department of Mathematics at the University of California, Los Angeles (UCLA). His research focuses on network science, nonlinear dynamics, and mathematical modeling of complex social systems. He explores topics such as opinion dynamics, temporal networks, multilayer networks, and the interplay between network structure and dynamical processes. Porter’s work spans theoretical and applied domains, including the analysis of social networks, epidemic spread, and infrastructure resilience. He has contributed to methods for detecting community structures, analyzing hypergraphs, and modeling collective behavior in systems ranging from online social media to biological networks. His recent studies emphasize bounded-confidence models, quantum walks on networks, and the application of topological data analysis to spatial systems. His research also intersects with interdisciplinary projects, such as modeling disease mitigation strategies, customer mobility in supermarkets, and the coevolution of disease spread and opinions. He has collaborated on initiatives like the NSF-funded project to predict microbiome assembly via multilayer networks. Porter’s publications reflect a deep engagement with both foundational theory and real-world applications, often leveraging computational and analytical techniques to uncover principles governing complex systems. His work bridges mathematics, physics, and social sciences, addressing challenges in data ethics, information diffusion, and network-driven phenomena.

Sai Manoj Pudukotai Dinakarraoمشاهده پروفایل

استادیار

• Hardware security
• Adversarial machine learning
• Internet of Things networks
• +۹ مورد دیگر

Sai Manoj Pudukotai Dinakarrao is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering and Computing. He leads the HArt (Hardware and AI Research) Group, focusing on cutting-edge research at the intersection of hardware security and artificial intelligence. His educational journey includes a BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University (2010), an MTech in Information Technology from International Institute of Information Technology Bangalore (2012), and a PhD in Electrical Engineering from Nanyang Technological University, Singapore (2015). Following his doctoral studies, he completed post-doctoral research at TU Wien, Vienna (2015-2017) and George Mason University (2017-2018). Dr. Dinakarrao's research spans hardware security, adversarial machine learning, IoT networks, and deep learning in resource-constrained environments. His work integrates hardware design with AI techniques to address security challenges in computing systems, with particular focus on side-channel attack detection, malware detection in IoT networks, on-chip security, and hardware accelerator design for machine learning applications. His research has resulted in numerous publications in top-tier conferences and journals including IEEE Transactions, ACM conferences, and Design Automation Conference. Analysis of his recent publications reveals a strong trend toward hardware security solutions using machine learning techniques. His work increasingly focuses on Processing-in-Memory architectures, energy-efficient security solutions for IoT devices, and innovative approaches to hardware Trojan detection. Many publications demonstrate interdisciplinary collaboration across electrical engineering, computer science, and cybersecurity domains. Young Research Fellow Award at Design Automation Conference (DAC) 2013 Best paper award at International Conference on Data Mining (ICDM) 2019 Best paper award at International Conference on Consumer Electronics (ICCE) 2020 Best paper nomination at International Conference on Computer-Aided Design (ICCAD) 2019 Best paper nomination at Design Automation and Test in Europe (DATE) 2018 Dr. Dinakarrao has successfully mentored numerous PhD and MS students, with alumni securing positions at AMD-Xilinx, US Government agencies, and academic institutions. His research has been supported by significant grants from NSF, DARPA, and Virginia Commonwealth Cyber Initiative. Current projects include securing supply chains with UVA, developing novel architectures for machine learning acceleration, and creating energy-preserving cryptography protocols. The HArt Group maintains active collaborations with industry partners including AMD-Xilinx and government agencies. The lab focuses on practical implementations of theoretical security concepts, with particular emphasis on creating deployable security solutions for real-world hardware systems. Current research directions include intermittent computing with energy harvesting, hardware fuzzing techniques, and robust machine learning models resistant to adversarial attacks.

Fuyuan Zhangمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Cybersecurity
• Formal Methods
• +۷ مورد دیگر

Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.

Jiong Tangمشاهده پروفایل

استاد

• System Dynamics
• Control
• Sensing and Automation
• +۷ مورد دیگر

Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.

Andrew J. Andersonمشاهده پروفایل

استادیار

• Neuroscience
• Neurophysiology
• Cognitive Neuroscience
• +۳ مورد دیگر

Dr. Andrew J. Anderson is an Assistant Professor in the Department of Neurology at the Medical College of Wisconsin. His research focuses on neurophysiological and neuroimaging approaches to understand language processing, cognitive fatigue in aging, and neural networks underlying semantic meaning. He employs techniques such as fMRI, EEG, and structural connectome analysis to explore how the brain encodes and decodes linguistic and experiential information. Dr. Anderson’s work also investigates potential biomarkers for early detection of neurodegenerative diseases like Alzheimer's through pattern analysis of brain activity. Key research areas include: Neurophysiological correlates of speech comprehension and semantic processing Functional connectivity in aging populations, particularly relating to cognitive fatigue Integration of artificial neural networks to model cortical language processing Brain decoding techniques for identifying individual cognitive patterns His publications (2018-2022) emphasize interdisciplinary methods combining neuroscience with computational models, revealing distributed neural networks involved in propositional sentence meaning, and dissociable electrophysiological measures of language processing in aging. While no specific scientific awards are listed, his research contributes to foundational understanding of brain-language interactions and healthy aging processes. Advising and grants: Details not explicitly provided in the text. His affiliations include the Medical College of Wisconsin Neurology department, with potential collaborative ties to affiliated hospitals or research institutes. No lab affiliations are mentioned.

Umberto Michelucciمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Astrophysics
• +۵ مورد دیگر

Umberto Michelucci is a Professor of Scientific Machine Learning at Lucerne University of Applied Sciences and Arts (HSLU), Switzerland. He holds a PhD in Machine Learning applied to Physics and has over 20 years of industry experience. He is the Subject Head of Applied Data Intelligence in Continuing and Executive Education, Head of Certificates in Machine Learning/Data Engineering, and founder of TOELT LLC and the AI Center of Excellence at Helsana Versicherung AG. His research focuses on machine learning applications in science, astrophysics, uncertainty quantification, and sensor technology. Education PhD in Machine Learning applied to Physics (Portsmouth University) Master in Theoretical Physics (University of Florence) Postgraduate Certificate in Higher Education (Open University, UK) Research Interests Michelucci’s work bridges machine learning and scientific disciplines. Key areas include: Machine learning for astrophysics (INAF collaborations) Uncertainty analysis in high-stakes ML systems Deep learning for optical sensing (e.g., olive oil quality analysis) Foundational mathematical concepts for ML in science Awards & Recognition World’s Top 2% Scientists (Stanford List) Google Developer Expert in Machine Learning AI Global Ambassador (2022) TOP AI Influencer in Switzerland (2021) Grants & Collaborations He collaborates with institutions like INAF (Italy) and NVIDIA/Google, leading projects on AI for agrifood, medical imaging, and astrophysics. His work includes $multi-million industry partnerships and EU-funded research. Labs & Teams Director of the TOELT AI Lab and oversees HSLU’s Applied Data Intelligence programs. Active in open-source initiatives and global AI standardization efforts.

Ryan T. Whiteمشاهده پروفایل

دانشیار

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.

Christos Diouمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +۳ مورد دیگر

Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.

Pouria Sarhadiمشاهده پروفایل

مدرس ارشد

• Control Theory
• Autonomous Vehicles
• Adaptive Control
• +۳ مورد دیگر

Pouria Sarhadi is a Senior Lecturer at the University of Hertfordshire's School of Physics, Engineering & Computer Science. He has held roles as a Research Fellow at Queen's University Belfast (2021-2022) and the University of Surrey (2019-2021), and served as an Adjunct Professor at Babol Noshirvani University of Technology (2016-2018). He is a Chartered Engineer (CEng), Member of the IET, and recognized as UK 'Global Talent' by UKRI. His research focuses on control theory applications, adaptive control, autonomous vehicles, and systems engineering. He has contributed to over 50 peer-reviewed publications and led projects such as HertsLynx CAM On-Demand (UKRI-funded). He is actively involved in editorial roles for Applied Ocean Research and advises on IFAC Technical Committees. Research interests include: Autonomous systems (ground, marine, aerial) Machine learning in control systems Adaptive control and system identification Autonomous vehicle safety and navigation Key awards include CEng, MIET, and UK Global Talent recognition. He has advised on UKRI and Horizon Europe projects and consults for industries. Current projects include hybrid navigation safety simulators and cooperative control of multi-AUV systems.

• Computational Modelling
• Optimization
• Simulation
• +۶ مورد دیگر

John Betts is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He serves as Course Director for the Bachelor of Information Technology and previously held roles as Chief Examiner and Lecturer for multiple IT units. His research focuses on computational modelling, optimization, simulation, and data science, with applications in societal polarization, healthcare, and retail inventory systems. Education: Doctor of Philosophy (Operations Research), Monash University (2003) Graduate Diploma in Statistics/Operations Research, RMIT University (1996) Postgraduate Diploma in Mathematics and Mathematics Education, University of Melbourne (1992) Diploma in Education, Monash University (1984) Bachelor of Arts, Monash University (1983) Research Interests: Dr. Betts explores computational methods to address variability in complex systems, including agent-based modeling for societal polarization, optimization in healthcare (e.g., prostate brachytherapy), and simulation-driven decision-making in retail and transportation. His work contributes to UN SDGs related to education and sustainable cities. Projects: Leading the Optimising multi-item retail inventories project (2021–2026) focusing on inventory optimization algorithms. Contributing to the Biofocussed Prostate Cancer RadioTherapy (BiRT) project (2017–2021), developing personalized radiation therapy plans. Investigating Societal Polarization dynamics via agent-based models and hate crime measurement frameworks. Advising & Grants: Dr. Betts has secured $2.5M+ in research funding, including ARC grants for retail optimization and prostate cancer treatment planning. He advises on interdisciplinary projects spanning computer science, healthcare, and social sciences. Labs/Teams: Collaborates with Monash’s Data Science Institute and the Australian Research Data Commons, contributing to the Temporal Networks Security group and Health Informatics initiatives.

• Fuzzy Set Theory
• Fractional Calculus
• Mathematical Inequalities
• +۶ مورد دیگر

Muhammad Bilal Khan is an Assistant Professor at the Department of Electronics and Power Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Pakistan. His research spans mathematical theory and practical applications in wireless communication sensing technology. His primary research interests include Fuzzy Set Theory , Fractional Calculus , Mathematical Inequalities , and RF Sensing Applications in healthcare. His theoretical work focuses on developing new mathematical frameworks for fuzzy-number valued functions, particularly exploring pre-invexity, harmonic convexity, and related integral inequalities. On the applied side, he has made significant contributions to non-contact health monitoring systems using software-defined radio technology for detecting conditions like text neck syndrome, spinal curvature disorders, and breathing abnormalities. Analysis of his 15 most recent publications reveals a dual research trajectory: approximately 60% focus on theoretical mathematical contributions in fuzzy analysis and integral inequalities, while 40% address practical applications of wireless sensing in healthcare. His mathematical work frequently appears in journals like Axioms and Symmetry , while his applied research is published in venues including IEEE Access and Sensors . His notable scientific contributions include developing novel frameworks for (q 1 , q 2 )-Linear Diophantine Fuzzy Sets and advancing the theory of Diamond Intuitionistic Fuzzy Sets. His healthcare applications research has pioneered non-invasive monitoring techniques using RF sensing for elderly fall detection, text neck syndrome diagnosis, and breathing pattern analysis. Dr. Khan maintains active collaborations with researchers across multiple institutions, particularly working with Mohamed S. Soliman (13 joint publications), Muhammad Aslam Noor (12 publications), and Savin Treanta (8 publications) on mathematical theory, while collaborating with Najah AbuAli, Mubashir Rehman, and Xiaodong Yang on healthcare sensing applications.

• Service-Oriented Architectures
• Cloud Computing
• Data Management
• +۷ مورد دیگر

Professor Kyriazis Dimosthenis holds a faculty position at the Department of Digital Systems, University of Piraeus. He earned his diploma in Electrical & Computer Engineering from the National Technical University of Athens (2001) and a cross-disciplinary MSc in Techno-Economic Systems (2004). His academic rank is Professor specializing in service-oriented architectures with a focus on quality of service and workflow management. He has led European projects like BigDataStack, CrowdHEALTH, and CYBELE, addressing challenges in cloud computing, edge computing, and AI-driven solutions for healthcare, finance, and industrial sectors. His research emphasizes resilient service-oriented systems, AI explainability, and human-centric digital transformation. Notable contributions include frameworks for dynamic resource allocation in hybrid cloud/edge environments, AI applications for maritime safety, and data governance solutions for cross-sector integration. He coordinates initiatives such as the Future Internet Architecture Board and Cloud QoS&SLAs, driving advancements in federated data marketplaces and sustainable computing practices. His recent work explores Large Language Model (LLM) applications in financial decision-making, conversational AI for MLOps, and neurosymbolic systems for defect detection. He also investigates XAI methodologies, including VirtualXAI, which leverages GPT-generated personas for explainability assessment. His projects often bridge technical innovation with societal impact, such as the iHELP platform for holistic health records and SmartCHANGE for behavioral change strategies in youth health. Key themes in his publications include bias mitigation in machine learning, dynamic deployment prediction in hybrid cloud settings, and energy-efficient data spaces for mobility. He has contributed to standards like the H2020-funded IRMOS and 5GTANGO, emphasizing interoperability and fault-tolerant architectures. His work frequently intersects with EU policy frameworks, particularly in data governance and ethical AI implementation.

Ahmad Esmaeiliمشاهده پروفایل

استادیار

• Distributed Artificial Intelligence
• Multi-Agent Systems
• Machine Learning
• +۳ مورد دیگر

Dr. Ahmad Esmaeili is an Assistant Professor of Computer Science in the School of Computing at Wichita State University (WSU). He leads the Multi-Agent Systems Research Lab, focusing on distributed artificial intelligence, multi-agent systems, and machine learning for cyber-physical systems. He holds a PhD from Purdue University's Department of Computer and Information Technology, where he also served as a graduate instructor. Education: PhD in Computer and Information Technology, Purdue University, West Lafayette Research Interests: His work emphasizes distributed machine learning frameworks, privacy-preserving collaborative systems, and applications of multi-agent systems in wearable sensor technologies. Current projects include Holonic Learning for scalable distributed training and cross-individual human activity recognition using decentralized approaches. Teaching: CS560: Design and Analysis of Algorithms (WSU) CS797O: Neural Networks and Deep Learning (WSU) CNIT175: Visual Programming (WSU) Introduction to Artificial Intelligence (Purdue) Introduction to Machine Learning and Deep Learning (Purdue) Labs/Teams: Directs the Multi-Agent Systems Research Lab at WSU, exploring distributed AI applications in next-generation technologies.

• Data Privacy
• Cyber Security
• Cloud Security
• +۵ مورد دیگر

Professor Xun Yi is a faculty member at RMIT University's School of Computing Technologies, specializing in cybersecurity, data privacy, and distributed computing. His research focuses on privacy-preserving technologies in cloud systems, blockchain applications, federated learning, and secure communication protocols. He has published over 150 papers in top-tier journals and conferences, including IEEE Transactions on Dependable and Secure Computing. Since 2014, he has served as an Associate Editor for the IEEE Transactions on Dependable and Secure Computing, and has organized major events like the Australasian Information Security Conference (AISC) in 2015 and 2016. Professor Yi actively supervises PhD and Master's research projects, focusing on topics like secure IoT systems, privacy-aware machine learning, and blockchain-enabled frameworks. His work emphasizes practical solutions for real-world challenges in data security and privacy. Research Interests: Data Privacy, Cyber Security, Cloud Security, Wireless/Mobile Security, Applied Cryptography Blockchain-based Systems, Federated Learning, Privacy-Preserving AI Recent Contributions: Developed frameworks for secure data aggregation in smart grids and healthcare systems Advanced techniques for privacy-preserving federated learning and graph neural networks Contributed to standards for secure authentication in vehicular networks (VANETs) Supervision: Open to mentoring students in cybersecurity, privacy-enhancing technologies, and IoT security.

Hugo Larochelleمشاهده پروفایل

دانشیار

• Neural Networks
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Hugo Larochelle is an Associate Professor at the Université de Montréal's Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Sciences. His expertise spans Neural Networks, Deep Learning, and Machine Learning, with contributions to fields like Computer Vision and Natural Language Processing. A graduate of DIRO (Bacc 2004, PhD 2009), he held roles at the University of Sherbrooke (2011–2016) and co-founded Whetlab (acquired by Twitter in 2015). Currently at Google Brain, he balances academic and industrial research, leading projects like the UNIQUE initiative exploring neuroscience-AI intersections. He actively contributes to Quebec’s AI ecosystem through MILA and has received prestigious awards, including the 2019 Diplômé d'honneur. His work emphasizes ethical AI, reproducibility, and interdisciplinary applications. Education: Baccalauréat (Computer Science), Université de Montréal, 2004 Doctorat (Computer Science), Université de Montréal, 2009 Research Interests: Hugo’s work focuses on advancing deep learning techniques for real-world applications, including environmental monitoring (e.g., tree crown segmentation via drone imagery), AI ethics, and model unlearning. He explores intersections between neuroscience and AI, leveraging interdisciplinary approaches to solve complex problems. Recent efforts emphasize benchmark standardization (e.g., EEVEE/GATE) and improving model efficiency through architecture innovations like SoftMoE. Articles Trends: Recent publications highlight contributions to instance segmentation (SAM models), ethical AI (unlearning frameworks), and interdisciplinary applications (e.g., bird species modeling with remote sensing). His work bridges theoretical advancements with practical tools for industries and environmental science. Scientific Awards: 2019 Diplômé d'honneur (Université de Montréal). Advising & Grants: Supervised PhD students in topics like neural networks, code modeling, and program execution (e.g., Sara Hooker, David Bieber). Co-PI on the UNIQUE project (2019–2024, FRQNT-funded), exploring neuro-AI synergies. Recipient of CIFAR funding for ICRA research (2017–2022). Labs & Collaborations: Active contributor to MILA as an associate member. Leads interdisciplinary efforts in AI for environmental challenges and healthcare imaging optimization.