جست‌وجوی استاد و پژوهشگر

• Deep Learning
• Cybersecurity
• Biomedical Engineering
• +۵ مورد دیگر

Faraz Hussain is an Associate Professor at Clarkson University's Coulter School of Engineering & Applied Sciences, with joint appointments in the Departments of Electrical & Computer Engineering and Computer Science. His research focuses on deep learning, cybersecurity, and biomedical engineering, with particular emphasis on applications in biometrics, edge computing, and healthcare technology. Dr. Hussain's work spans theoretical advancements and practical implementations, including explainable AI, fingerprint segmentation systems, and hazard detection in retail environments. He has contributed to interdisciplinary projects such as automated visualization of flow cytometry data and parameter estimation for biological models using Bayesian methods. His publications highlight a trajectory toward solving real-world challenges through machine learning and computational methods, with notable contributions in cybersecurity (e.g., keystroke dynamics for APT prevention), medical imaging (pulmonary disease classification), and autonomous robotics (ROS-based deep learning deployment). While no formal awards are listed, his active research portfolio demonstrates sustained innovation in AI-driven solutions across multiple domains. His advising and grants activities remain unspecified in the provided materials.

Yang Xuمشاهده پروفایل

استاد پژوهشی

• Software-Defined Networking
• Data Center Networks
• High-Speed Networks
• +۹ مورد دیگر

Yang Xu is a Research Professor in the Department of Electrical and Computer Engineering at the New York University Tandon School of Engineering. He previously served as a Visiting Assistant Professor at NYU-Poly (2007-2008) and holds a Ph.D. in Computer Science and Technology from Tsinghua University (2007), a Master of Science in Computer Science and Technology from Tsinghua University (2003), and a Bachelor of Engineering from Beijing University of Posts and Telecommunications (2001). Ph.D. (2007): Tsinghua University, Computer Science and Technology M.Sc. (2003): Tsinghua University, Computer Science and Technology B.Eng. (2001): Beijing University of Posts and Telecommunications His research focuses on network security, software-defined networks, high-speed networking, and hardware security. Key interests include developing solutions for data center networks, mitigating malware and rootkit threats using hardware performance counters, and evaluating side-channel vulnerabilities in FPGAs. He explores the dual impact of machine learning in hardware security, balancing its benefits and risks. His work also addresses cybersecurity challenges in industrial control systems. Dr. Xu’s recent articles highlight advancements in leveraging hardware resources for security, such as HPC-based malware detectors and signature-based ROP detection systems. His research trends emphasize practical cybersecurity transitions, embedded system security, and FPGA vulnerability assessments. No scientific awards or grants are explicitly listed in the provided text. He advises no current students, but his research has influenced embedded, firmware, and network security domains. He is affiliated with the High Speed Networking Lab , which specializes in data center network solutions and security tools. His office is located at 10.069 (2 MetroTech Center, 10th Floor), and he can be reached at yang@nyu.edu or (646) 997-3153.

• Cybersecurity
• Network Security
• Cloud Security
• +۸ مورد دیگر

Chhagan Lal is a Postdoctoral Fellow at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), Norway. He is part of the Critical Infrastructure Security and Resilience (CISaR) group and currently contributes to the EU-funded CYBERUNITY project. Certified in CISSP, CCSP, and Microsoft Azure Security (SC-100/200/300, AZ-500), he specializes in cybersecurity frameworks, compliance, risk assessment, threat intelligence, and machine learning applications. His research focuses on advancing security and resilience in critical infrastructure, particularly in network and cloud security technologies. Key areas include Internet of Things (IoT) and Industry 4.0, Cyber-Physical Systems (CPS), Software Defined Networking (SDN), Information Centric Networking (ICN), and blockchain technologies for smart contracts. He has also worked on secure communication protocols for underwater acoustic networks and mobile ad-hoc networks (MANETs). Chhagan has been actively involved in multiple EU projects, including H2020-SU-SEC-2018 (LOCARD), H2020-ICT30-2015 (TagItSmart!), and EU-India collaborations (REACH). His professional activities include serving as a journal reviewer for IEEE Transactions and Elsevier publications, Program Chair for ACM WiSec 2017, and Publicity Chair for ACNS 2020 and related conferences. He has also contributed as a Program Committee Member to several cybersecurity-focused events. His work emphasizes secure solutions for MANETs, VANETs, and industrial IoT systems, including projects like CENSOR (Cloud Enabled Secure IoT Architecture over SDN) and blockchain-based frameworks such as LEChain. Chhagan has authored book chapters on topics like secure machine-to-machine communication and vehicular cloud computing. No scientific awards are explicitly mentioned in the provided texts. His research spans theoretical and practical cybersecurity challenges, with a focus on developing robust countermeasures against DDoS attacks, enhancing QoS in multimedia networks, and privacy-preserving mechanisms in vehicular and industrial systems.

Nick Duffieldمشاهده پروفایل

استاد

• Data Science
• Network Science
• Machine Learning
• +۶ مورد دیگر

Nick Duffield is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Royce E. Wisenbaker Professorship I and serving as Director of the Texas A&M Institute of Data Science. He specializes in data science and network science, focusing on applications of probability, statistics, and machine learning to large-scale datasets in networks and beyond. His research bridges foundational data science with practical applications in transportation, hydrology, and network resilience. Education: B.A. (Cambridge, 1982), M.Math. (Cambridge, 1983), Ph.D. (University of London, 1987). Research Interests: Graph stream sampling, network measurement, machine learning for traffic prediction, soil moisture analysis, and public safety data science. He leads initiatives like the Texas A&M Data Science Bootcamp and collaborates on projects such as AI in construction and precision agriculture. Recent Articles Highlight: Focus on machine learning-driven solutions in agriculture (e.g., UAV-based cotton yield prediction), transportation (e.g., electric truck feasibility studies), and disaster response (e.g., social media analysis for damage estimation). Awards: ACM, IET, IEEE, and AT&T Fellowships; co-recipient of ACM Sigmetrics Test of Time Award (2012/2013). Grants/Advising: Over $20M in funding from NSF, NIH, and industry. Advises on 10+ student projects, including work on graph neural networks and hydrology. Labs/Teams: Texas A&M Institute of Data Science (TAMIDS), collaborations with Texas Transportation Institute, and hydrology teams analyzing soil moisture via machine learning.

• Volcanology
• Geophysics
• Seismology
• +۳ مورد دیگر

Mel Rodgers is a Research Assistant Professor in the School of Geosciences at the University of South Florida, affiliated with the College of Arts and Sciences. She holds a Ph.D. (2009–2013) and M.Sci. (2000–2004) from the University of South Florida and University of Durham, respectively. Her research focuses on volcanic processes and hazards, employing geophysical methods such as seismology, photogrammetry, and machine learning. Key projects include studies on volcanoes in Colombia, Montserrat, Nicaragua, and Italy, with an emphasis on improving hazard mitigation through advanced monitoring techniques and community outreach. She has pioneered UAV-based surveys for topographic change detection, magnetic anomaly mapping, and wetland monitoring. Rodgers has received the Richard A. Davis Endowed Fellowship in Geology (2013) and contributed to interdisciplinary grants, including a $1.5M initiative for coastal climate-risk apps. Her work bridges academia and public engagement, as seen in her podcast appearance ( Calling: Earth #041 ) and collaborations with volcano monitoring institutions. Key Projects: Blackfoot Reservoir volcanic field studies, UAV applications in geosciences, and eruption forecasting via machine learning. Grants: $1.5M NSF grant for coastal hazard assessment tools. Labs/Teams: Collaborates with international volcano monitoring groups and utilizes USF’s geology field station resources.

Ding Yuanمشاهده پروفایل

استاد

• Systems Software
• Distributed Systems
• Operating Systems
• +۱۰ مورد دیگر

Ding Yuan is a Professor at the University of Toronto in the Department of Electrical and Computer Engineering , holding the Canada Research Chair in Systems Software and serving as Chair of the Computer Engineering Group . His research focuses on improving the availability and performance of large-scale software systems , with notable tools like CLP (used by Uber) and YScope startup co-founded with PhD students. Education : Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus : Systems software, log compression/search, distributed system failures, and performance debugging Advising Alumni : Yongle Zhang (Purdue Assistant Professor), Xu Zhao (Facebook Research Scientist), Xiang (Jenny) Ren (Northeastern Assistant Professor), Kirk Rodrigues and David Lion (YScope co-founders) Scientific Recognition : Canada Research Chair, McCharles Prize, Gordon Slemon Teaching Award, Student Choice Award, Meta 2022 Systems Research Award Teaching : Courses like ECE344 (Operating Systems), ECE1759 (Advanced OS), ECE454 (Systems Programming) Leadership : Program Co-chair at SOSP, HotOS, APSys; Vice-Chair of ACM SIGOPS

Birhanu Esheteمشاهده پروفایل

دانشیار

• Trustworthy Machine Learning
• Cybersecurity
• Privacy
• +۶ مورد دیگر

Birhanu Eshete is an Associate Professor at the University of Michigan-Dearborn in the Department of Computer and Information Science within the College of Engineering and Computer Science. He leads the Data-Driven Security & Privacy Lab (DSPLab) and serves as a faculty affiliate at the Michigan Institute for Data Science (MIDAS) . His academic journey includes a Ph.D. from the University of Trento , M.Sc. and B.Sc. from Addis Ababa University . Research Focus : Trustworthy Machine Learning (security, privacy, transparency, fairness), Cyber Threat Intelligence, and Cybercrime Analysis. Teaching : Courses in Trustworthy AI, Data Security & Privacy, and Compiler Design. Awards : 2023 NSF CAREER Award (#2238084) 2021 NSF Research Grant (#2103596) 2020 U.S. Patent (#0059481) 2018 Distinguished Paper at USENIX Security His recent publications emphasize adversarial machine learning , privacy-preserving techniques , and cybersecurity innovation . Former students hold positions at Google , Amazon , and Tesla , while current advisees include Ph.D. candidates and Master's students focusing on cutting-edge security and ML research.

Vijay Bhuseمشاهده پروفایل

دانشیار

• Cybersecurity
• Intrusion Detection
• Software Security
• +۳ مورد دیگر

Vijay Bhuse serves as an Associate Professor and Program Chair for the Cybersecurity BS program at Grand Valley State University's College of Computing. With a Ph.D. in Computer Science from Western Michigan University and postdoctoral training at Dartmouth College, he brings extensive industry and academic experience to his role. Ph.D. in Computer Science, Western Michigan University (2007) Postdoctoral Fellowship, ISTS, Dartmouth College (2007) B.Tech in Computer Science, DBATU, India (2000) Dr. Bhuse's research spans Cybersecurity, Intrusion Detection, Software Security, and Wireless Sensor Networks , with recent work focusing on rogue switch detection, secure code execution, and healthcare security. His publications demonstrate consistent contributions to network security and educational methodologies since 2003. His research trends reveal a strong emphasis on practical security solutions for emerging technologies including IoT, connected vehicles, and social media platforms. Recent publications (2022-2023) focus on network automation for threat detection and secure wearable authentication systems. Administration of research grant ($24,617) as PI from MLL Inc. Networking equipment grant ($5,000) from ETSU Honors College Laker intramural grant ($2,000) at GVSU GenCyber 2024 participant (Sara Sutton PI) Dr. Bhuse actively mentors students through the "Hackers Analyzing Threats" (HAT) student club and serves as faculty advisor for the Indian Student Association. His teaching combines industry certifications (CompTIA Network+, Security+) with academic rigor in networking and cybersecurity courses.

• Software Testing
• Network Security
• Penetration Testing
• +۷ مورد دیگر

Dr. Ishbel Duncan is an Honorary Senior Lecturer at the School of Computer Science, University of St Andrews. Her research spans software testing, network security, penetration testing, data sciences, and computer-supported education. She leads the Cyber Nexus project (£420K, Scottish Government) and collaborates on EU-funded initiatives like CINE Northern Periphery and CUPIDO. Duncan's work includes virtual worlds for education and heritage (ViStA, Virtual Dive Experience) and security evaluation techniques for cloud systems and intrusion detection. Education: Ph.D., University of Durham (1989-1993, Strong Mutation Testing) B.Sc., University of St Andrews Her research focuses on Security Testing , Visualization of Software Testing , and Education Technology . She develops intelligent agents for failure analysis in programming education, explores OCR-based phishing prevention, and applies virtual environments for international student orientation and heritage preservation. Recent work emphasizes adaptive intrusion detection, subdomain threat detection, and energy-efficient protocols for wireless sensor networks. Key grants include the Cyber Nexus project (Scottish Government) and CUPIDO (EU funding). She supervises postgraduate researchers and contributes to the UN Sustainable Development Goal for Quality Education through digital tools. Technical Collaborations: NHS, BT, Microsoft, and academic institutions like Abertay University.

Fang Wangمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Nature-Inspired Computing
• Multi-Agent Systems
• +۱۲ مورد دیگر

Dr. Fang Wang is a Senior Lecturer in the Department of Computer Science at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences. Holding a PhD in Artificial Intelligence from the University of Edinburgh, she transitioned from a senior researcher role at the BT Group's research center to academia in 2010. Education: PhD in Artificial Intelligence (University of Edinburgh) Professional Background: Senior Researcher at BT Group Her research spans Artificial Intelligence and its interdisciplinary applications, focusing on nature-inspired computing (swarm intelligence, evolutionary computing), multi-agent systems , neural networks , and computer vision with applications in network optimization, healthcare, and education. Recent work explores fault detection , deepfake exposure , and medical informatics . The 15 most recent publications reveal trends in machine learning for industrial diagnostics, multi-agent reinforcement learning with formal logic constraints, and vision-language systems for medical applications. Subfields include neural networks , temporal/spectral analysis , and human-computer interaction . Scientific Recognition : Gordon Radley Technical Premium Highly Commended award (BT) ACM Best Student Paper Award (International Conference on Autonomous Agents) As an educator, she teaches programming, algorithms, digital innovation, and project-based courses to undergraduates and MSc students, with class sizes ranging from 8 to 350. She supervises PhD students in topics like self-organizing agents, intelligent intrusion detection, and human action recognition.

Gregory BLANCمشاهده پروفایل

مدرس

• Network Security
• Intrusion Detection Systems
• Software-Defined Networking Security
• +۵ مورد دیگر

Gregory Blanc is a Lecturer in SCN at Telecom SudParis, a leading French graduate school of engineering specializing in digital technologies. His academic work focuses on network security, with particular expertise in intrusion detection systems, software-defined networking security, and machine learning applications for cybersecurity. Dr. Blanc's research spans several critical areas in modern cybersecurity: Development of reinforcement learning frameworks for DDoS mitigation while preserving quality of service Analysis of adversarial attacks against machine learning-based security systems Privacy considerations in intrusion detection systems Security challenges in 5G networks and Internet of Things environments Methods for reproducible evaluation of security tools and frameworks His recent publication record demonstrates consistent contributions to top venues in the cybersecurity field. Over the past two years (2024-2025), he has published 15 papers across journals and conferences, reflecting active research engagement. His work shows strong international collaboration, with co-authors from institutions across Europe and Asia. Dr. Blanc is involved in significant research projects, including the SuperviZ initiative focused on security supervision and orchestration, indicating ongoing research funding and institutional support for his work.

Yih-Chun Huمشاهده پروفایل

دانشیار

• Network Security
• Wireless Networks
• Vehicular Networks
• +۶ مورد دیگر

Yih-Chun Hu is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, where he has been a faculty member since August 2005. He is also an affiliate faculty member of the Computer Science department and faculty of the Advanced Digital Sciences Center. Dr. Hu is a member of the Coordinated Science Laboratory and the Information Trust Institute at UIUC. Dr. Hu received his Ph.D. in Computer Science from Carnegie Mellon University in 2003, where his advisor was David B. Johnson. His dissertation was titled "Enabling Secure High-Performance Wireless Ad Hoc Networking." Prior to joining UIUC, he was a Postdoctoral Researcher at the University of California, Berkeley under Doug Tygar. He earned his B.S. Magna Cum Laude in Computer Science and Mathematics from the University of Washington in 1997. Dr. Hu's research focuses on network security and wireless systems, with particular emphasis on secure routing protocols, vehicular networks, and defense against denial-of-service attacks. His work spans multiple domains including secure Internet routing, privacy in wireless networks, and security mechanisms for ad hoc networks. He has developed innovative approaches to insider attacks in wireless networks, DDoS defense mechanisms, and security for vehicular communication systems. His research often combines theoretical analysis with practical system implementation and evaluation. Dr. Hu's recent publications demonstrate a continued focus on security challenges in emerging network environments, including IoT security, blockchain applications for security, and privacy-preserving techniques for vehicular networks. His work shows a consistent theme of developing practical security solutions that balance strong security guarantees with performance considerations. Dr. Hu has served in numerous editorial and conference organization roles, including as Associate Editor for IEEE Transactions on Networking (2012-2016) and ACM Transactions on Mobile Computing (2013-2018). He has held significant roles in major networking conferences such as Technical Program Chair for ACM MobiHoc 2012 and various committee positions for ACM MobiCom. Within the Coordinated Science Laboratory at UIUC, Dr. Hu leads research efforts in network security and wireless systems, mentoring students and collaborating with other faculty on interdisciplinary security projects. His lab focuses on developing security mechanisms that are both theoretically sound and practically deployable in real-world systems.

• Mission-Critical Networked Systems
• Industry 4.0
• Control over the Cloud
• +۳ مورد دیگر

Maria Kihl is a Professor at Lund University's Department of Electrical and Information Technology, where she also serves as Assistant Head of Department. She leads research in mission-critical networked systems, focusing on Industry 4.0, cloud-based control systems, and 5G/6G architectures. As Director of the NEXTG2COM Competence Centre, she coordinates advanced digitalization initiatives. She is also affiliated with ELLIIT, Sentio, and multiple university profile areas including AI and Digitalization. Her research explores autonomous vehicle systems, intelligent traffic management, and network security, with recent work emphasizing machine learning integration in transportation and 5G readiness for industrial applications. Publications demonstrate consistent focus on real-time control systems and cybersecurity in emerging technologies. Awards: Best Paper Award (2018) She directs the NEXTG2COM centre, managing projects on next-gen communication infrastructures funded by Vinnova, and collaborates extensively with industry partners. Her team develops experimental testbeds for autonomous intersection management and vehicular communication systems.

• Machine Learning
• Data Analysis
• Data Mining
• +۱۲ مورد دیگر

Hua Wang is a Professor at Victoria University, Melbourne, Australia, affiliated with the Institute for Sustainable Industries and Liveable Cities (ISILC) and the Oceania Cyber Security Centre (OCSC). His research spans machine learning, data analysis, cyber security, and privacy-preserving technologies, with applications in healthcare, IoT, and social media analytics. PhD in Computer Science (University of Southern Queensland, 2004) Research focuses on privacy-preserving data publishing , deep learning for cyber security , graph neural networks for IoT , and EEG-based neurological disorder detection . His work integrates federated learning and transformer models for secure, efficient data processing. Recent publications emphasize multi-objective optimization for privacy, adaptive neural networks , and domain-generalized tracking systems . Collaborations include Japan Society for the Promotion of Science (JSPS) and Data61 University Planning Committee. Hua supervises 14 PhD students, serves as ARC College of Expert (2021–present), and leads editorial roles for EAI Transactions on Scalable Information Systems , PLOS ONE , and World Wide Web Journal . He holds 14 grants totaling $3.1M, including ARC Discovery and Linkage projects.

Man-Ki Yoonمشاهده پروفایل

استادیار

• Trustworthy and Accountable Computing
• Autonomous Systems
• Real-Time Embedded Systems
• +۴ مورد دیگر

Man-Ki Yoon serves as an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering. His academic journey includes a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (2017), an M.S. from the same institution (2011), and a B.S. in Computer Science and Engineering from Seoul National University in Korea (2009). Prior to joining NC State, he worked as a research scientist in Computer Science at Yale University. His educational background demonstrates a strong foundation in computer systems with progressive specialization in real-time and embedded computing. The transition from his undergraduate studies in Korea to doctoral research in the United States reflects an international academic trajectory focused on increasingly specialized aspects of computer systems. Yoon's research centers on trustworthy and accountable computing for autonomous systems, with particular emphasis on autonomous vehicles and unmanned aerial vehicles. His work bridges theoretical formal methods with practical security applications in real-time embedded systems and the Internet of Things. This research agenda addresses critical challenges in verifying temporal behavior of real-time applications while maintaining strong security guarantees against timing-based side channel attacks. His approach combines insights from operating systems, security, and distributed systems to develop novel solutions that enable reasoning about fault events, component responsibility, and evidence trustworthiness in complex cyber-physical environments. Analysis of his recent publications reveals a clear evolution in research focus from foundational real-time scheduling problems toward increasingly sophisticated security mechanisms for autonomous systems. His work demonstrates consistent progression from theoretical verification techniques to practical implementations addressing accountability in distributed systems and security challenges in autonomous vehicle ecosystems. The publications show growing emphasis on AI/ML integration with traditional real-time systems, particularly in safety-critical applications where accountability and verifiability remain paramount. NCDOT Transportation Center of Excellence: Sustainable and Resilient Infrastructure – Cyber–Resilient Traffic Management Centers Faculty Research and Professional Development Award IBM Workforce Development Initiative Award SaTC: CORE: Small: Partition-Oblivious Real-Time Hierarchical Scheduling Yale Course-based Undergraduate Research Experience (CURE) Grant Qualcomm Roberto Padovani Scholarship Intel PhD Fellowship Qualcomm Innovation Fellowship Yoon actively mentors students through research projects and teaching, with numerous publications featuring student co-authors spanning topics from autonomous vehicle security to real-time system verification. His current NSF-funded research on partition-oblivious scheduling demonstrates significant grant acquisition capability, addressing fundamental security challenges in real-time hierarchical scheduling where conventional time-partitioning mechanisms fail to achieve strong temporal isolation from a security perspective. This work develops algorithmic solutions enabling non-interference-based security among partitions, allowing systems to employ advanced hardware and software technologies for high-end real-time applications in secure environments. His laboratory environment emphasizes hands-on experimentation with autonomous systems, evidenced by his self-driving car course where students build software systems for miniaturized autonomous vehicles. This practical approach complements theoretical research with tangible implementations, creating a comprehensive research ecosystem that spans from formal verification to physical system deployment.