جست‌وجوی استاد و پژوهشگر

Sonny Phamمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Vision
• Data Mining
• +۳ مورد دیگر

Associate Professor Sonny Pham leads research in artificial intelligence at Curtin University's School of EECMS. His work balances theoretical foundations with practical applications in computer vision, data mining, and deep learning. As head of the IAMAI research group, he collaborates with industry partners on security systems, healthcare AI, and sustainable technologies. His research explores: Computationally efficient deep learning architectures Compressed sensing for high-dimensional data Robust statistical methods for real-world problems Applications in computer vision and industrial automation Recent publications demonstrate a focus on medical imaging interpretation and efficient neural networks, with applications spanning radiology report generation, semantic segmentation for autonomous systems, and cybersecurity. His team's work consistently bridges theoretical AI advancements with industrial applications. Honors include: Multiple WANMA Awards (2021-2024) for industry-impactful research INCITE Award for social impact technology (2024) IEEE Young Author Best Paper Award (2010) Over $5M in competitive research funding including MRFF and DFAT grants He leads the IAMAI research group with 12+ graduate students and coordinates Curtin's Master of Artificial Intelligence program. Industry collaborations include Alcoa Australia, iCetana, and HyprFire.

• AI-based feedback
• Computational thinking education
• Game-based learning
• +۷ مورد دیگر

Dr. Jingyun Wang is an Assistant Professor in the Department of Computer Science at Durham University. Previously, she held an Assistant Professor position at Kyushu University, Japan. Her primary affiliations include the Centre for Neurodiversity & Development and the Artificial Intelligence and Human Systems Group (AIHS), as well as the Pedagogical Innovation in Computer Science Group (PICS). She is a Fellow of the Higher Education Academy and has led or contributed to research projects funded by JSPS, JST, NICT, Innovate UK, and industry partners. Her research focuses on AI-driven educational technologies, including AI-based feedback systems, computational thinking education, game-based learning, and ontology techniques. She actively contributes to editorial boards (e.g., Computers & Education: Artificial Intelligence ) and serves as a conference chair for AIED, ICCE, and LTLE. Current research includes adaptive learning systems for mathematics education, serious games for cybersecurity training, and visualization tools for e-learning. Her scientific contributions span over 50 peer-reviewed publications, with recent work emphasizing learning analytics, multimodal systems, and digital health interventions. She advises multiple PhD students and mentors in professional recognition pathways. Key projects include developing the BETTER speech training system, the MEMORABLE cybersecurity game framework, and ontology-based language learning platforms.

• Distributed Algorithms
• Programmable Matter
• Bio-inspired Algorithms
• +۵ مورد دیگر

Andrea W. Richa is a President's Professor at Arizona State University (ASU), holding positions in the School of Computing and Augmented Intelligence (SCAI), Barrett Honors College, and multiple research centers including the Biodesign Institute's Center for Biocomputing, Security, and Society. She specializes in distributed algorithms, programmable matter, and bio-inspired computing. Richa has led major research initiatives, including a DoD MURI award and an NSF CAREER Award, and has delivered keynote speeches at top conferences like DISC and LATIN. Her work focuses on self-organizing particle systems, wireless networks, and algorithmic foundations of active matter. Educations: PhD (Computer Science, Carnegie Mellon University, 1998), M.S. (Computer Science, Carnegie Mellon University, 1995), B.S. (Computer Science, Federal University of Rio de Janeiro, Brazil, 1989). Research Interests: Distributed algorithms, programmable matter, bio-inspired systems, wireless communication models, graph algorithms, combinatorial optimization, and resource allocation. She leads the Self-Organizing Particle Systems Lab and is part of SCAI's Theory and Algorithms group. Awards: 2024 ASU Mentorship Award, 2021 Mentor of the Year, 2017 SCAI Research Excellence Award, NSF CAREER Award (1999), and multiple grants including DoD MURI. Her research spans theoretical and applied domains, with over 150 publications in top venues. Grants: Current DoD MURI funding (2019-25), NSF awards on Markov chain algorithms and active matter (2021-25), and prior funding on programmable matter (2014-2017). Labs/Teams: SOPS Lab (sops.engineering.asu.edu), contributing to interdisciplinary research in algorithmic matter and bio-inspired systems.

Vassilios Tzerposمشاهده پروفایل

دانشیار

• Audio Processing
• Machine Learning
• Digital Signal Processing
• +۳ مورد دیگر

Vassilios Tzerpos is an Associate Professor at the Lassonde School of Engineering, York University, where he has been since 2001. He holds a Ph.D. in Computer Science from the University of Toronto (2001). His research focuses on audio processing for musical applications, deep learning, digital signal processing, machine listening, and software engineering education. He directs the APTLY lab exploring music-technology intersections and leads the LaSSoftE lab developing socially-oriented software solutions. Education: Ph.D. in Computer Science, University of Toronto, 2001 Research Highlights: Dr. Tzerpos' work spans music information retrieval (e.g., automatic music classification), synthetic speech detection using neural networks, and software engineering pedagogy. His recent projects include Music-STAR for audio re-instrumentation and OER-based learning path creation systems. He has pioneered methods in design pattern detection and software clustering evaluation. Grants & Labs: Leads two research groups: APTLY (music-tech) and LaSSoftE (social impact software). Active in developing adaptive cybersecurity solutions against DoS attacks and refining software architecture recovery techniques. Key Themes in Publications: Recent work emphasizes machine learning applications in music technology and cybersecurity, with foundational contributions to software clustering methodologies and design pattern detection algorithms. His work bridges theoretical computer science with practical applications in education and creative industries.

Huazhen Fangمشاهده پروفایل

دانشیار

• Systems and Control
• Advanced Battery Management
• Energy Storage Systems
• +۵ مورد دیگر

Huazhen Fang is an Associate Professor in the Department of Mechanical Engineering at the University of Kansas School of Engineering, where he joined in 2014. He leads the Information & Smart Systems Laboratory (ISSL) and holds a courtesy appointment in the Department of Electrical Engineering & Computer Science. His research focuses on enabling intelligence for complex systems through information-driven approaches. Dr. Fang received his Ph.D. in Mechanical Engineering from the University of California, San Diego in 2014, following an M.Sc. from the University of Saskatchewan and a B.Sc. in Computer Science & Technology from Northwestern Polytechnic University in China. He was a Visiting Faculty Fellow at Mitsubishi Electric Research Laboratories in 2022. His research interests span Systems and Control, Advanced Battery Management, Energy Storage Systems, and Robotics, with particular focus on system modeling, estimation, control design, machine learning and numerical optimization. Dr. Fang's work has significant applications in energy management, cooperative robotics, and environmental observing systems. His research has been supported by the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. His extensive publication record shows a clear trend toward increasingly sophisticated integration of physics-based modeling with machine learning approaches, particularly in battery management systems and autonomous vehicle control. Recent work demonstrates a growing emphasis on Bayesian inference methods, distributed control architectures, and safety-critical applications of intelligent control systems. Faculty Early Career Award from National Science Foundation (2019) University Scholarly Achievement Award (2024) Miller Professional Development Award (2022) Miller Faculty Scholar Award (2018, 2019, 2023) Wesley G. Cramer Outstanding Mechanical Engineering Faculty Award (2016) Big XII Faculty Fellowship (2015) IEEE Transactions on Transportation Electrification Prize Paper Award (2024) Dr. Fang has successfully mentored numerous graduate students through the Information & Smart Systems Laboratory, with many receiving awards for their research. His research has attracted significant funding from prestigious organizations including the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. He currently serves as an Associate Editor for multiple prestigious journals including Information Sciences, IEEE Transactions on Industrial Electronics, and IEEE Control Systems Letters. The Information & Smart Systems Laboratory (ISSL) under Dr. Fang's leadership has established itself as a center for cutting-edge research in information-driven smart systems. The lab focuses on pushing the frontiers of information extraction, analysis and exploitation for dynamic systems to deal with system complexity and enable system intelligence. The lab actively collaborates with industry partners and local communities, emphasizing research that serves societal needs.

Mark De Belsمشاهده پروفایل

استاد مهمان

• Cyber Security
• Network Security
• Trust Infrastructure
• +۳ مورد دیگر

Mr. Mark De Bels is an NSA Visiting Professor at the Center for Cyber Security Studies, United States Naval Academy. He holds an M.S. in Computer Science with an Emphasis on Network Security and a B.S. in Mathematics. His research focuses on Trust Infrastructure and Security of Software Cryptographic Applications. Mr. De Bels has extensive experience in both defensive and offensive NSA missions. He previously served as Chief of a 100-person Signals Intelligence analysis team targeting cryptographic applications, and as a technical SME at the Department of Defense Cyber Crime Center (DC3). His career spans roles such as Information Assurance Analyst, Operating Systems Analyst, and Cryptographic Application Analyst at NSA from 1994–2016. His professional contributions include leadership in securing defense contractor networks against foreign cyber attacks and advancing cryptographic application analysis methodologies. He currently engages in academic cybersecurity research and education through the Center for Cyber Security Studies, integrating his operational expertise into academic training programs.

Bryan Kian Hsiang Lowمشاهده پروفایل

دانشیار

• Probabilistic Machine Learning
• Bayesian Optimization
• Multi-Agent Systems
• +۴ مورد دیگر

Bryan Kian Hsiang Low serves as Associate Professor in the Department of Computer Science at the National University of Singapore's School of Computing, while simultaneously holding leadership positions as Director of AI Research at AI Singapore and Deputy Director of the NUS AI Institute. His academic journey includes a B.Sc. (2001) and M.Sc. (2002) in Computer Science from NUS, followed by a Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University (2009). His research spans probabilistic machine learning, multi-agent systems, and trustworthy AI, with particular focus on Bayesian optimization , federated learning , and data-efficient methodologies . The Low Lab develops frameworks for collaborative AI, automated machine learning, and AI applications in scientific domains through the Group of Learning and Optimization Working in AI (GLOW.AI), which maintains a multi-disciplinary approach bridging computer science, mathematics, and engineering disciplines. Analysis of his recent publications reveals a consistent emphasis on data valuation , privacy-preserving collaborative learning , and robust optimization techniques , with increasing integration of large language models into his research framework. His work demonstrates strong theoretical foundations coupled with practical applications in computational sustainability and robotics. Andrew P. Sage Best Transactions Paper Award (2006) NUS Overseas Graduate Scholarship (2004-2009) Faculty Teaching Excellence Award (2017-2018) IEEE RAS Distinguished Lecturer (2019) World Economic Forum Global Future Councils Fellow (2016-2018) Dr. Low actively mentors PhD students including Rachael Sim, Quoc Phong Nguyen, and Zhongxiang Dai, while leading major initiatives like the AI Phenome Platform for plant breeding optimization. His research group GLOW.AI operates at the intersection of theory and practice, with strong industry engagement through AI Singapore. Current projects focus on scalable AI systems for scientific discovery and developing frameworks for equitable collaborative machine learning with robust privacy guarantees.

R. Michael Alvarezمشاهده پروفایل

استاد

• Political Science
• Computational Social Science
• Voting Behavior
• +۶ مورد دیگر

R. Michael Alvarez , Flintridge Foundation Professor of Political and Computational Social Science at Caltech, is a leading scholar in election technology, political methodology, and machine learning applications in social science. Affiliated with the Caltech/MIT Voting Technology Project , the Social and Decision Neuroscience Program , and the Resnick Sustainability Institute , his work bridges technology and democracy. Education: B.A. from Carleton College, Ph.D. from Duke University Academic Career: Caltech faculty since 1992 His research spans: Election Integrity : Monitoring election security, fraud detection, and ballot systems Computational Social Science : Applying machine learning to voter behavior and policy analysis Climate Policy : Examining public attitudes and behavioral interventions for sustainability Online Behavior : Analyzing toxicity in gaming and social media dynamics Key article trends show focus on election forensics (2025 Nature Climate Change study), game toxicity analysis (2025 CHI Play paper), and LLM applications in social science. His students include Jacob Morrier, Mitchell Linegar, and teams of postdocs and undergraduates in Caltech's SURF program. Scientific recognition includes: Google Cloud Research Innovators Class of 2022 Co-editor of multiple academic series including Cambridge Elements in Quantitative Methods

Salman Durraniمشاهده پروفایل

استاد

• Internet of Things networks
• Satellite and UAV communications
• Machine learning for wireless communications
• +۲ مورد دیگر

Salman Durrani is a Professor and Associate Director Education at the School of Engineering, Australian National University. He holds a PhD in Electrical Engineering from the University of Queensland and a BSc (First Class Honours) from the University of Engineering & Technology, Lahore, Pakistan. His research spans Internet of Things networks, satellite/UAV communications, machine learning applications in wireless systems, early wildfire detection, and backscatter communications. Current projects focus on terahertz communication security, UAV-assisted networks, and IoT-based environmental monitoring. His recent publications demonstrate strong emphasis on wireless security, terahertz technology optimization, UAV network design, and IoT applications for environmental protection. Technological innovations include novel beamforming techniques and lightweight authentication protocols. AI 2000 Internet of Things Most Influential Scholar (2020, 2022, 2023) IEEE ComSoc Asia Pacific Outstanding Paper Award (2016) Australian Council of Graduate Research Excellence Award (2019) ANU Vice-Chancellor's Awards for Supervision (2018) and Education (2012) He has supervised 15 PhD students to completion and secured $1.9M in research funding as chief investigator for six grants. Current projects include participation in the ANU Optus Bushfire Research Centre of Excellence.

Pardis Pishdadمشاهده پروفایل

دانشیار

• Sustainable Built Environments
• Integrated Project Delivery (IPD)
• Digital Twin Technology
• +۹ مورد دیگر

Pardis Pishdad is an Associate Professor and Graduate Program Director in the School of Building Construction at Georgia Institute of Technology’s College of Design. She directs the Smart Built Environment Eco-System (Smart Bees) Laboratory, focusing on integrating cyber-physical systems, digital twins, and innovative project delivery methods (e.g., IPD, Flash Tracking) for sustainable built environments. Her research bridges technology adoption, trust-building in construction contracts, and supply chain optimization. Education: PhD, Environmental Design and Planning (Virginia Tech) Master’s Degrees: Civil Engineering (Virginia Tech), Design Studies in Project Management (Harvard), Architecture (University of Tehran) Bachelor’s in Architectural Engineering (Azad University of Shiraz) Research Interests: Her work emphasizes sustainable construction practices using IoT, BIM, and blockchain. Key areas include lifecycle cost analysis, lean construction, and smart building technologies. She explores trust dynamics and collaboration in construction projects through game theory and process optimization. Recognition: 2018 ENR Top 20 Under 40 Professionals 2016 CII National Outstanding Researcher Award 2020-2022 Georgia Tech Provost Teaching Learning Fellow Advisory Roles: Academic Advisor for CII’s Supply Chain Management Community, Vice Chair of BuildingSMART’s BIM Forum 5D Taskforce. Formerly advised the Construction Management Association of America’s Board (2016–2018). Industry Collaboration: Partnerships with Turner Construction, GDOT, and VDOT. Research on Flash Tracking and blockchain has been integrated into industry practices. Labs & Teams: The Smart Bees Lab pioneers cyber-physical systems for smart buildings, exploring AI-driven solutions and sustainable construction frameworks.

Prashanth Krishnamurthyمشاهده پروفایل

پژوهشگر

• Robotics
• Control Systems
• Cyber-Physical Systems
• +۳ مورد دیگر

Prashanth Krishnamurthy is a Research Scientist in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on robotics, control systems, and cybersecurity, particularly in cyber-physical systems such as power grids and embedded devices. He holds a Ph.D. in Electrical Engineering from NYU. Key research areas include hardware security (e.g., detecting Trojans in chips), anomaly detection in critical infrastructure, and resilient control strategies for robotic systems. He has led or contributed to projects funded by the U.S. Department of Energy (DOE), Office of Naval Research (ONR), and others, including the Tracking Real-time Anomalies in Power Systems (TRAPS) initiative and hardware Trojan detection using short-term aging phenomena. Education: Ph.D., Electrical Engineering, NYU His work bridges theoretical advancements and practical implementations, such as developing FPGA-based testbeds for hardware security validation and creating AI-driven cybersecurity tools like the CRAKEN LLM agent. Collaborators include institutions like SRI International, Karlsruhe Institute of Technology, and the NYU Center for Cybersecurity. Grants include a $1.94M DOE grant for TRAPS and a $359K DURIP grant for hardware Trojan detection. His technical contributions span control systems, anomaly detection algorithms, and cybersecurity frameworks for embedded systems. He is actively involved in advancing secure cyber-physical systems through innovations in real-time monitoring, robust control mechanisms, and AI-augmented security solutions.

Qiongxiu Liمشاهده پروفایل

استادیار

• Cybersecurity
• Distributed Optimization
• Privacy-Preserving Technologies
• +۵ مورد دیگر

Qiongxiu Li is a Tenure-Track Assistant Professor in the Cyber Security group at Aalborg University's Copenhagen campus, part of the Technical Faculty of IT and Design. Her research focuses on cybersecurity, distributed optimization, privacy/security, and federated learning. She has authored/co-authored 38 papers in top-tier venues including IEEE Transactions on Information Forensics and Security, ICLR, and EUSIPCO. Education: PhD in Privacy and Security from Aalborg University (2018-2021). Notable achievements include winning the EUSIPCO 2020 3MT Contest and co-delivering a tutorial on privacy-preserving distributed optimization at EUSIPCO 2024. She actively reviews for conferences like NeurIPS, ICLR, and journals such as TPAMI and TIFS. Research Themes: Privacy-preserving distributed algorithms, federated learning security, differential privacy, and adversarial machine learning. Recent Trends: Focus on securing AI systems (e.g., LLM vulnerabilities, federated clustering privacy), quantization for privacy, and theoretical bounds in decentralized learning. Awards: 2020 EUSIPCO 3MT Winner (outstanding finalist in EURASIP's annual doctoral research competition). Grants/Projects: Co-PI of the AI:SECURITY project (2025-2029) addressing AI security threats like phishing and malicious actors. Labs/Teams: Leads the Cyber Security group at Aalborg's Copenhagen campus, focusing on theoretical and applied research in secure distributed systems.

• International Political Economy
• Globalization and Economic Interdependence
• Transnational Regulation
• +۷ مورد دیگر

Abraham L. Newman is a Professor at the Edmund A. Walsh School of Foreign Service and the Government Department at Georgetown University, where he also serves as Director of the BMW Center for German and European Studies. His work bridges political science, international relations, and global regulatory studies, with a focus on the intersection of economic interdependence and political power. PhD in Political Science – University of California, Berkeley MA in Political Science – University of California, Berkeley MA in International Relations – Stanford University BA in International Relations – Stanford University His research centers on globalization, data privacy, financial regulation, and weaponized interdependence . He investigates how transnational networks, particularly in finance and digital infrastructure, are leveraged for geopolitical advantage. His work emphasizes the evolving role of private actors, regulatory institutions, and technological change in shaping international order. Analysis of his recent publications reveals a strong focus on economic statecraft, digital sovereignty, AI governance, and transatlantic regulatory dynamics . Themes such as financial sanctions, data privacy enforcement, and the weaponization of global networks recur across his scholarship, reflecting a cohesive research agenda on power in an interconnected world. While no specific scientific awards are listed in the provided text, his publications in top journals like Science , Nature , International Organization , and World Politics indicate significant scholarly recognition. He has advised or collaborated on major research projects related to transnational regulation, GDPR implementation, and global financial governance . Although no specific grants are listed, his leadership of the BMW Center suggests involvement in externally funded research initiatives. He has also edited or contributed to collaborative academic volumes across institutions such as Princeton and Rice Universities. His leadership of the BMW Center for German and European Studies places him at the center of a research team focused on transatlantic relations, EU policy, and comparative political economy. This center likely supports a network of scholars, students, and visiting researchers engaged in European and global governance studies.

Karl-Erik Årzénمشاهده پروفایل

استاد

• Control Engineering
• Cyber-Physical Systems
• Real-Time Systems
• +۹ مورد دیگر

Karl-Erik Årzén is Professor and Head of the Department of Control Engineering at Lund University's Faculty of Engineering. He is also Co-director of the Wallenberg AI, Autonomous Systems and Software Program (WASP) and a key member of ELLIIT, the excellence center in information technology. His roles include leadership in AI and digitalization profile areas at both LTH and Lund University. His research lies at the intersection of control engineering and computer science, with a focus on cyber-physical systems, real-time systems, embedded control, and resource management in cloud and edge computing environments. He has pioneered methods for predictable performance in cloud applications and dynamic resource allocation using control-theoretic approaches. The recent publications highlight a strong trend in control over the cloud and edge, real-time scheduling co-design, distributed camera systems, and reinforcement learning for auto-scaling. Key topics include model predictive control, LQG-based scheduling, bandwidth allocation, and robustness in cyber-physical systems. The work spans theoretical control design and practical implementation in distributed systems. His scientific awards include multiple Best Paper Awards from IEEE and ACM conferences in 2018, 2016, and 2004, recognizing excellence in autonomic computing, edge computing, and real-time systems. Best Paper Award, IEEE International Conference on Autonomic Computing, 2018 Best Paper Award, IEEE International Conference on Edge Computing (EDGE), July 2018 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Årzén has supervised over 20 PhD students, including Mikael Johansson, Anton Cervin, Yang Xu, and Per Skarin, and currently supervises Ahmed Al Bayati and Max Nyberg Carlsson. His grant portfolio includes major projects such as WASP, AORTA (VINNOVA), and ELLIIT's 'Robust and Secure Control over the Cloud'. He has also contributed to innovation through tools like TrueTime and Jitterbug. He leads the RobotLab LTH initiative and is involved in the Nordic University Hub on Industrial Internet of Things (HI2OT). His work bridges academia and industry, with collaborations on adaptive control, cloud-native systems, and autonomous robotics.

Nour Moustafaمشاهده پروفایل

دانشیار

• Cyber Security
• Computer System Security
• Networking and Communications
• +۷ مورد دیگر

Dr. Nour Moustafa is an Associate Professor and ARC DECRA Fellow at the School of Systems & Computing (SysCom) , University of New South Wales (UNSW) Canberra , Australia. He leads the Intelligent Security Group and focuses on developing AI/ML-driven cybersecurity frameworks for smart systems. Educated at Helwan University (BSc/MSc in Information Systems) and UNSW (PhD in Cybersecurity). Research Interests include intrusion detection, threat intelligence, privacy preservation, digital forensics, and cyber resilience, with methodologies spanning statistical analysis , machine learning , and deep learning applied to IoT , Edge/Cloud , and Industrial IoT environments. His work emphasizes federated learning for privacy preservation, blockchain for secure AI, and digital twins for network self-healing. Notable contributions include the TON-IoT , Bot-IoT , and UNSW-NB15 datasets for cybersecurity evaluation. Scientific Awards : 2020 Spitfire Memorial Defence Fellowship ACM Distinguished Speaker IEEE Senior Member He has served as guest associate editor for IEEE Transactions journals and held leadership roles in conferences like IEEE TrustCom . His research bridges academia and industry, with over 75 publications in top-tier venues.