جست‌وجوی استاد و پژوهشگر

Isaac Kofi Ntiمشاهده پروفایل

استادیار

• Applied Machine Learning
• Cybersecurity
• Health Informatics
• +۴ مورد دیگر

Dr. Isaac Kofi Nti is an Assistant Professor in the School of Information Technology at the University of Cincinnati, with over 16 years of interdisciplinary research experience in applied machine learning, health informatics, cybersecurity, and data governance. His work emphasizes responsible AI deployment in high-impact domains including healthcare, finance, and education. He concurrently holds affiliations with Ghanaian institutions (University of Energy and Natural Resources and Sunyani Technical University) and serves as a Visiting Assistant Professor at Cincinnati until 2024. Education PhD in Computer Science, University of Energy and Natural Resources, Ghana (2021) MSc in Information Technology, Kwame Nkrumah University of Science and Technology, Ghana (2016) BSc Computer Science, Catholic University of Ghana (2011) Higher National Diploma in Electrical & Electronic Engineering, Sunyani Technical University, Ghana (2007) Research Focus Dr. Nti develops lightweight, explainable AI systems for cybersecurity, healthcare diagnostics, agricultural optimization, and educational technology. His research integrates secure data analytics, ethical AI frameworks, and cloud-based learning infrastructures. Current projects explore generative AI in education, predictive modeling for disease outbreaks, and blockchain applications for health data verification. Publication Trends His 60+ publications (2022-2024) demonstrate strong emphasis on ensemble learning methods, healthcare AI applications, and cybersecurity innovations. Recent works frequently utilize hybrid models (LSTM/XGBoost/StackNet) for fraud detection, disease prediction, and renewable energy forecasting. Awards & Grants Provost Office Strategic Collaborative Faculty Team Award (2024-2025): $19,200 for AI integration in higher education Faculty Development Grant (2023-2024): $10,038 for emerging technology training Faculty Incentive Awards for Research (CECH, UC 2022-2024) Teaching & Service Teaches graduate/undergraduate courses in machine learning, cybersecurity, and data technologies. Leads multiple university committees including PhD Admissions Review and Applied Research teams. Professional memberships include ACM, IEEE, and AAAI.

Sherif Saad Ahmedمشاهده پروفایل

دانشیار

• Cybersecurity
• Dependable Systems
• Resilient Computing
• +۲ مورد دیگر

Sherif Saad Ahmed is an Associate Professor at the University of Windsor's School of Computer Science. His research focuses on cybersecurity, dependable systems, resilient computing, and applied machine learning. He teaches and conducts research in secure software engineering and intrusion detection systems. With a PhD in Electrical & Computer Engineering from University of Victoria, his work addresses practical challenges in cyber-physical system security and adaptive defense mechanisms.

Amit Kumar Mishraمشاهده پروفایل

استاد

• Machine Learning
• Radar Technology
• Synthetic Aperture Radar (SAR)
• +۷ مورد دیگر

Amit Kumar Mishra is a Professor of Communications Engineering and Director of the National Spectrum Centre at Aberystwyth University, UK. His primary affiliation is within the Faculty of Business and Physical Sciences. He leads interdisciplinary research in radar technology, machine learning, and cognitive architectures with applications in environmental monitoring, biomedical engineering, and cybersecurity. His research interests include Synthetic Aperture Radar (SAR) imaging, AI-driven environmental sensing (e.g., agricultural monitoring, biodiversity tracking), and advanced communication systems like IRS-enhanced NOMA networks. Recent work focuses on developing symbiotic AI-human cognitive systems for attention management and sensor data assimilation in industrial environments. Over 200+ publications span journals like Sensors , IEEE Transactions , and conference proceedings. Notable collaborations include projects on oil spill detection, underwater object recognition, and patient-specific medical modeling. He co-led the SMART Expertise project (2025) focused on innovation bridging. Media highlights include reports on medical imaging applications (Coarctation of Aorta studies) and the Aberystwyth University Technology Centre's innovation initiatives. His work aligns with UN SDGs related to sustainable cities and responsible consumption patterns.

Dipo Dunsinمشاهده پروفایل

مدرس

• Digital Forensics
• Cybersecurity
• Incident Response
• +۱ مورد دیگر

Dipo Dunsin serves as Lecturer in Computer Science & Applied Computing at London Metropolitan University, where he also manages the University's Digital Forensics Laboratory. His research develops innovative frameworks for malware investigation and cyber incident response. Dunsin's work applies reinforcement learning to automate threat analysis and evidence extraction, with recent focus on IoT forensics and dark web criminal activities. He creates specialized protocols like D2WFP for analyzing deep/dark web browsing and hybrid methods for detecting steganographic evidence in audio files.

Dharmendra Sharmaمشاهده پروفایل

استاد

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

Professor Dharmendra Sharma is a Professor of Computer Science at the University of Canberra, holding leadership roles such as Chair of the Faculty Board (Science and Technology) and former Dean of the Faculty of Information Sciences and Engineering. He specializes in AI, robotics, and distributed systems. With over 40 years of academic experience, he has published 320+ papers, supervised 40+ students, and led numerous research projects. His expertise spans constraint processing, machine learning, and applications in healthcare, security, and education. Education: PhD in Artificial Intelligence (Australian National University, 1988–1992) MCompSci, PGradDipMath, BSc (Maths/Chemistry) from the University of the South Pacific Research Interests: Distributed AI and multi-agent systems Machine learning/deep learning Constraint satisfaction models Applications in health, education, and security His work aligns with UN Sustainable Development Goals, particularly in education and technology access. Key Projects: Future Jobs Fund - Open Source Institute IAIM: Optimized Smart Data Centers eLiving Lab Collaboration Awards: Order of Australia (AM, 2019) Fellowships: ACS, South Pacific Computer Society Companion of Engineers Australia Advising and Grants: Supervised over 40 PhD/Master’s students Secured competitive grants for AI, cybersecurity, and health tech Collaborations with industry and international universities Labs/Teams: Leadership in AI and Robotics research groups Industry partnerships for innovation and training

Michael S. Branickyمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Internet of Things
• Artificial Intelligence
• +۵ مورد دیگر

Michael S. Branicky is Professor of Electrical Engineering & Computer Science (EECS) at the University of Kansas School of Engineering. Previously serving as KU's Dean of Engineering from July 2013 to June 2018, he maintains active research leadership following his tenure as professor and chair at Case Western Reserve University (CWRU) since 1996. His research spans Cyber-Physical Systems/IoT , Artificial Intelligence , Machine Learning , and Robotics , with particular expertise in switched and hybrid control systems. His work bridges theoretical control theory with practical applications in rehabilitation engineering and autonomous systems, evidenced by his h-index of 40 and nearly 17,000 citations. Recent publications demonstrate strong focus on reinforcement learning for human-robot interaction and cyber-physical systems. His editorial leadership in IEEE Transactions reflects his standing in the CPS community. Notable funding includes NSF awards for Active Sensing in Deformable Environments and CPS Week support. Fellow, IEEE (2016) NSF Director's Award for Collaborative Integration (2012) NSF Director's Superior Accomplishment Award (2010) Walter R. Fried Memorial Best Paper Award (2010) Multiple teaching awards at CWRU and Case School of Engineering Branicky leads significant research initiatives including the IUCRC Center for High-Assurance Secure Systems and IoT (CHASSI) and NRT-HDR: Internet of Catalysis. His laboratory work focuses on image-guided interventions and deformable environment mapping, supported by over $1 million in NSF funding.

Dong Chenمشاهده پروفایل

دانشیار

• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• Embedded AI
• +۵ مورد دیگر

Dong Chen is an Associate Professor in the Department of Computer Science at the Colorado School of Mines. His research focuses on building data-driven experimental systems in Cyber-Physical Systems (CPS), IoT, Embedded AI, and Embodied AI, with applications in smart devices, homes, cities, and renewable energy systems. He leads the Next Generation Cyber-Physical Systems Laboratory (CPSLab), emphasizing open-source systems and datasets. Dr. Chen holds PhDs in Electrical and Computer Engineering (2018, University of Massachusetts Amherst) and Computer Science (2014, Northeastern University). His work addresses security, privacy, sustainability, and efficiency in smart environments. Notable contributions include SolarFinder, SolarTrader, PrivacyGuard, and VoiceAttack, which tackle challenges in IoT privacy, energy trading, and adversarial attacks. He received the NSF CAREER Award (2023) and is a member of Sigma Xi, ACM, AAAI, and IEEE. His research spans system design, AI applications, and cross-cutting domains like solar energy modeling and edge computing. Current projects include AgileDART (edge stream processing) and SolarDetector (satellite-based PV array identification). Advising and collaborations: Dr. Chen seeks PhD and undergraduate students with strong CS/EE backgrounds. His lab focuses on CPS/IoT security, energy systems, and AI-driven solutions. He has published extensively on topics ranging from smart grid optimization to adversarial machine learning.

Yuan Hongمشاهده پروفایل

دانشیار

• Security
• Privacy
• Trustworthy Machine Learning
• +۸ مورد دیگر

Dr. Yuan Hong is an Associate Professor and Collins Aerospace Professor at the University of Connecticut's School of Computing, affiliated with the Connecticut Advanced Computing Center (CACC). He previously held an Assistant Professor position at Illinois Institute of Technology. His expertise spans Security, Privacy, and Trustworthy Machine Learning, with a focus on differential privacy, secure computation, adversarial attacks, and robustness in AI systems. He has received notable awards including the NSF CAREER Award and Cisco Research Awards, and his work has been published in top-tier venues like S&P, CCS, NeurIPS, and CVPR. Education: Ph.D. (Rutgers University), M.Sc. (Concordia University), B.Sc. (Beijing Institute of Technology). Research interests include adversarial machine learning, federated learning security, and privacy-preserving techniques. His recent work addresses challenges in machine unlearning, certified defenses, and robust watermarking for graph learning systems. He actively advises students (Ph.D., M.Sc., undergraduate) and leads the DataSec Lab. Current teaching includes Cybersecurity Lab, Computer Security, and Database Organization. Awards include the CCS Distinguished Paper Award (2024) and the Taylor L. Booth Graduate Fellowship for a student. He organizes conferences such as WPES'22 and DBSEC'22, and serves on editorial boards for IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Information Forensics and Security, and others. Key contributions include frameworks for certified adversarial robustness (e.g., UniCR), differential privacy mechanisms for federated learning, and defenses against poisoning attacks in video and code generation models.

Ali Dehghantanhaمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dr. Ali Dehghantanha is a Professor and Canada Research Chair in Cybersecurity and Threat Intelligence (Tier 2) at the University of Guelph's School of Computer Science. He founded the Cyber Science Lab, the Master of Cybersecurity and Threat Intelligence (MCTI) program, and the Canada Cyber Foundry. His expertise spans cybersecurity, AI, and critical infrastructure protection, with a focus on federated learning, threat intelligence, and enterprise risk management. Research Interests: Cybersecurity & AI Integration Federated Learning Security IoT and Industrial Cyber-Physical Systems Adversarial Machine Learning Cyber Threat Intelligence Automation His research emphasizes practical applications, such as securing agri-food systems, blockchain-enabled frameworks, and autonomous cybersecurity solutions. Notable achievements include the IEEE Outstanding Leadership Award (2021) and leadership in national cybersecurity initiatives. Recent Contributions: Recent work explores federated learning for privacy-preserving threat detection in IoT, AI-driven malware hunting, and autonomous systems for intrusion detection. His publications highlight interdisciplinary approaches to cybersecurity challenges in critical sectors like agriculture and transportation. Awards: Canada Research Chair (2020–present), IEEE Outstanding Leadership Award (2021). Labs & Initiatives: Cyber Science Lab: Pioneering research in AI-driven cybersecurity Canada Cyber Foundry: Bridging academic and industry cybersecurity innovation MCTI Program: Training next-generation cybersecurity professionals

Yidi Wangمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Cyber-Physical Systems
• +۵ مورد دیگر

Yidi Wang is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University (SCU). She holds a Ph.D. in Electrical and Computer Engineering from the University of California, Riverside (2023), and previously worked as a Postdoctoral Scholar at UCR (2023–2024). Her research focuses on real-time, embedded, and cyber-physical systems, particularly addressing challenges in GPU scheduling, energy efficiency, and batteryless device operation. She teaches courses such as Introduction to Embedded Systems and Operating Systems at SCU. Education: Ph.D. in Electrical and Computer Engineering, UC Riverside (2023) M.S. in Electrical and Computer Engineering, UC Riverside (2019) B.S. in Electrical Engineering, Huazhong University of Science and Technology (2018) Research Interests: Real-time scheduling for GPU-accelerated applications, energy-efficient computing, heterogeneous platforms, and reliable systems for intermittently powered devices. Her work spans system-level implementations and theoretical analysis, including novel scheduling algorithms and mathematical models for performance optimization. Awards: None explicitly listed in the provided texts. Advising & Grants: Currently mentoring 3 students (2 MS, 1 undergraduate) and recruiting PhD/MS candidates. She serves on technical committees for RTSS and RTAS conferences and has reviewed for journals like Transactions on Computers and Real-Time Systems. Labs/Teams: Leads research on GPU scheduling and batteryless systems, collaborating with industry and academic partners.

Adel Abusittaمشاهده پروفایل

استادیار

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

Dr. Adel Abusitta is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he conducts research at the intersection of artificial intelligence and cybersecurity. With expertise in secure and resilient AI systems, IoT security, and malware analysis, Dr. Abusitta has established himself as a significant contributor to the field of AI-powered cybersecurity solutions. Education: PhD in Computer Engineering from Polytechnique Montréal Postdoctoral Fellow at University of Montréal Postdoctoral Fellow at McGill University Dr. Abusitta's research focuses on developing secure and trustworthy artificial intelligence systems with applications in cybersecurity. His work spans several critical areas including explainable AI for security applications, AI-powered malware analysis, intrusion detection systems, and IoT security. He has made significant contributions to the understanding of how AI can be both secured against attacks and used to enhance security systems. His research addresses the dual challenge of making AI systems resilient to adversarial manipulation while leveraging AI's capabilities to detect and prevent cyber threats in complex environments like cloud computing and IoT networks. Analysis of Dr. Abusitta's recent publications reveals a strong focus on the intersection of AI and cybersecurity, particularly in developing robust anomaly detection systems, explainable security solutions, and resilient architectures for IoT environments. His work demonstrates a consistent trajectory toward making AI systems both more secure and more useful for security applications, with increasing emphasis on practical implementations that can withstand real-world challenges. Dr. Abusitta has collaborated extensively with Defence Research and Development Canada (DRDC) on projects related to AI-powered data analytics for discerning malware intent. He has also worked with industrial partners through the Institute for Data Valorization (IVADO) to develop privacy-preserving machine learning techniques that maintain accuracy while protecting sensitive information. His research has practical applications in critical infrastructure protection and secure AI deployment.

LIANG Zhenkaiمشاهده پروفایل

دانشیار

• system and software security
• Web security
• mobile security
• +۶ مورد دیگر

Dr. LIANG Zhenkai is an Associate Professor and Chairman of the Department of Computer Science at the National University of Singapore's School of Computing. He also serves as the Lead Principal Investigator for the National Cybersecurity R&D Lab (NCL). With extensive experience in academic leadership and cybersecurity research, Dr. Liang has established himself as a prominent figure in the field of system and software security. Dr. Liang received his Ph.D. in Computer Science from Stony Brook University in 2006 and his B.S. degrees in Computer Science and Economics from Peking University in 1999. His dual background provides a unique perspective on security challenges that bridges technical expertise with economic understanding. Dr. Liang's research focuses on system and software security , with particular emphasis on security in emerging platforms including Web, mobile, and Internet-of-Things (IoT) systems. His specific research interests include program analysis, Web and IoT system security, and virtualization. As the leader of the Curiosity Research Group, his team pursues missions centered around "Understanding systems (理解系统), abstracting knowledge (提炼知识), and connecting facts (参悟规律)". This philosophical approach to security research has yielded numerous significant contributions to the field. Dr. Liang's recent publications demonstrate a strong evolution from fundamental security mechanisms to sophisticated solutions addressing AI security, blockchain, and advanced vulnerability analysis. His work increasingly integrates machine learning techniques with traditional security approaches, focusing on developing robust defenses against sophisticated attacks while maintaining system usability. The trend shows a progression toward addressing contemporary challenges in large language models, secure system observability, and vulnerability propagation analysis. Dr. Liang has received numerous prestigious awards recognizing his research excellence: Outstanding Paper Award at ACSAC (2003) Best Paper Award at USENIX Security Symposium (2007) ACM SIGSOFT Distinguished Paper at ESEC-FSE (2009) Best Paper Award at W2SP Workshop (2014) Annual Teaching Excellence Award at NUS (2014, 2015) As an educator, Dr. Liang has taught various undergraduate and graduate courses including CS3235 Computer Security, CS5231 Systems Security, and CS5321 Network Security. His teaching philosophy, which he has published on in "Tool, Technique, and Tao in Computer Security Education," emphasizes both technical expertise and philosophical understanding of security principles. He has successfully mentored numerous students and researchers in the cybersecurity field. Dr. Liang leads the Curiosity Research Group, which actively seeks curious minds to join their exploration of security systems. The group maintains strong connections with industry and government cybersecurity initiatives, particularly through the National Cybersecurity R&D Lab (NCL). Their research environment encourages innovative thinking with the requirement that "Curiosity is required, while mentality for repairing things (such as bicycles) is a plus."

• Wireless Communication
• Network Optimization
• Machine Learning
• +۳ مورد دیگر

Athanasios Gkelias is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on advanced wireless communication systems, network optimization, machine learning applications, and quantum computing methodologies. He contributes to interdisciplinary projects involving IoT coalitions, adversarial signal detection, and cognitive behavior analysis. Key research areas include: (1) Network resource management in Software-Defined Networks (SDN), (2) Localization systems for indoor environments, (3) 3D reconstruction using photometric stereo and origami-based modeling, and (4) distributed optimization frameworks for ad-hoc networks. His recent work explores quantum approaches to combinatorial optimization and self-supervised learning methods. Publications span 20+ years with notable contributions in network coding, vehicular ad-hoc networks (VANETs), and cross-layer design for wireless mesh networks. His work emphasizes practical implementations through frameworks like iVisher for caller ID spoofing detection and cooperative MAC protocols for multi-antenna systems. No scientific awards are listed in the provided information. His research has been applied in healthcare technology through emotion understanding systems for Alzheimer’s patients and in battlefield communication through IoBT coalitions. Active participation in Imperial College's Engineering faculty reflects his commitment to advancing telecommunications and network science.

Akos Ledecziمشاهده پروفایل

استاد

• Computer Science Education
• Wireless Sensor Networks
• Model Integrated Computing
• +۳ مورد دیگر

Akos Ledeczi is a Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. His research focuses on computer science education and wireless sensor networks (WSN) , with notable contributions to visual programming tools like NetsBlox and anti-poaching systems like WIPER. He holds a Ph.D. from Vanderbilt University and a Diploma from the Technical University of Budapest. His education initiatives include the NetsBlox platform for K-12 STEM education and a popular Coursera MOOC on introductory programming. In WSN, his team developed a countersniper system with real-time shooter localization and a wearable system for military applications. He is also a leader in Model Integrated Computing , creating tools like the Web-based Generic Modeling Environment. Recent work includes NSF-funded projects on cybersecurity education in middle schools and animal-borne acoustic monitoring. His awards include the ACM SenSys Test of Time Award (2014) and the Best Showpiece Award at IEEE VL/HCC (2021). Key grants and collaborations span NSF projects, Vodafone innovation programs, and the Cyber Makerspace initiative. His lab develops tools like DeepForge and RoboScape Online , emphasizing open-source and collaborative platforms. Students supervised include Devin Jean (K-12 tools), Gordon Stein (robotics simulations), and Brian Broll (NetsBlox extensions).

George Kolliosمشاهده پروفایل

استاد

• Temporal Indexing
• Spatio-Temporal Data Management
• Data Mining
• +۴ مورد دیگر

George Kollios is a Professor and Chair of the Computer Science Department at Boston University. He holds a PhD in Computer Science from Polytechnic University, New York (2000), an M.Sc. from the same institution (1998), and a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, Greece (1995). His research focuses on temporal/spatio-temporal indexing, database security, privacy-preserving technologies, and large-scale data management. His work is supported by NSF (including an NSF CAREER Award) and IARPA. Education: PhD, Polytechnic University, New York (2000) M.Sc., Polytechnic University, New York (1998) Diploma in Electrical and Computer Engineering, National Technical University of Athens (1995) Research Interests: His expertise spans data mining , secure database systems , and privacy-preserving algorithms . He develops methods for efficient query processing on encrypted data, spatio-temporal data management, and scalable clustering techniques. Recent Research Trends: His publications emphasize differential privacy , secure encryption schemes , and large-scale graph algorithms . Notable contributions include optimizing privacy in database queries and evaluating encryption protocols for secure data access. Awards: NSF CAREER Award Grants & Collaborations: Recipient of NSF-funded projects on access pattern privacy and IARPA-supported research. He collaborates on interdisciplinary initiatives to bridge theoretical foundations with practical security frameworks.