جست‌وجوی استاد و پژوهشگر

Ping Jiمشاهده پروفایل

استاد

• Network Measurement and Data Analysis
• Network Security
• Wireless Network Security Monitoring
• +۶ مورد دیگر

Ping Ji is a Full Professor and Chair of the Department of Computer Science within the School of Arts and Sciences at the City University of New York (CUNY), with dual appointments at Hunter College and John Jay College of Criminal Justice. She serves as Executive Officer of the Computer Science PhD Program and former Director of the Master's Program in Data Science at the CUNY Graduate Center (2018-2025). Educational background: PhD in Computer Science, University of Massachusetts Amherst (Advised by Jim Kurose and Don Towsley) MS in Computer Science, University of Massachusetts Amherst BA in Computer Science and Technology, Tsinghua University Her research spans network measurement and data analysis , security monitoring for computer and wireless networks , and Internet of Things (IoT) , with significant contributions to mobile network security and underwater acoustic communications. She founded John Jay College's Master's Program in Digital Forensics and Cybersecurity and the Graduate Center's Master's Program in Data Science. Analysis of her publication trends reveals a strategic evolution from foundational network security work toward AI-integrated systems for censorship detection, ethical security frameworks for small businesses, and transformative educational ecosystems. Her recent work increasingly incorporates large language models and multi-modal sensor analysis while maintaining core expertise in network measurement. Scientific recognition: Google Award ($3M) for CyberNYC Institutional Research Multiple NSF grants totaling over $2.1M including CCRI, I-Corps, and SFS programs Leadership in national cybersecurity workforce development initiatives Dr. Ji has secured over $3 million in research funding while advising numerous PhD students through the NeMo (Networks & Mobile Systems) Lab. Her grant portfolio demonstrates sustained success in translating theoretical research into practical applications for security monitoring, digital forensics, and educational technology. Current projects include AI-enabled censorship detection systems and ethical security frameworks tailored for resource-constrained small businesses. The NeMo Lab under her direction conducts cutting-edge research in wireless network measurement, mobile security, and IoT applications, with recent expansions into underwater acoustic communications and AI-driven educational transformation through the AeDA (AI-Enabled Attainable Education for All) initiative.

• Granular Mechanics
• Mechanical Engineering
• Neutron Diffraction
• +۴ مورد دیگر

Associate Professor Chris Wensrich is a faculty member in the School of Engineering at the University of Newcastle, specializing in Mechanical Engineering. He holds a PhD, Bachelor of Mathematics, and Bachelor of Engineering from the same university. His research focuses on granular mechanics, neutron diffraction, and strain tomography, with pioneering work in Bragg-edge transmission strain tomography and granular dynamics modeling. Wensrich has held visiting appointments at Clare Hall College, Cambridge University, and the Isaac Newton Institute for Mathematical Sciences in the UK. He currently serves as President of the Australian Neutron Beam User Group (ANBUG) and is a member of the ACNS Program Advisory Team at ANSTO. His expertise spans applied mechanics, computational modeling (DEM), and experimental techniques involving neutron diffraction. His research interests include granular material behavior, stress distribution measurement, and validation of computational models using neutron imaging. Notable contributions include studies on silo quaking dynamics, force chain analysis in granular assemblies, and residual stress characterization in additive manufacturing. Wensrich has supervised 11 PhD students and secured over $5.4M in grants, including ARC Discovery Projects and industry-linked initiatives. Publications span granular mechanics, strain tomography, and material characterization, with over 60 journal articles and 38 conference papers. His work bridges theoretical, computational, and experimental methods to advance understanding of particulate systems and engineering materials.

Giacomo Parigiمشاهده پروفایل

دانشیار

• Nuclear Magnetic Resonance (NMR)
• Paramagnetism-Based Effects
• MRI Contrast Agents
• +۴ مورد دیگر

Giacomo Parigi is an Associate Professor of Chemistry at the University of Florence, affiliated with the Magnetic Resonance Center and the Department of Chemistry. His work focuses on paramagnetic effects in NMR, MRI contrast agents, and protein dynamics. He holds a Physics degree (1992) and a Chemistry PhD from the University of Florence, with postdoctoral and research roles at CERM since 1999. Research Interests: Parigi’s research explores paramagnetic effects in biological molecules, including protein structure determination, MRI contrast agent design, and relaxometry. He co-authored seminal books on NMR of paramagnetic molecules and pioneered methods for studying protein dynamics via field-cycling NMR. His lab integrates computational biology, bioinformatics, and experimental NMR to address biomedical challenges. Publications & Trends: Recent work emphasizes machine learning-enhanced NMR analysis, novel MRI probes (e.g., Mn-based nanogels), and structural studies of metalloproteins. His articles highlight innovations in paramagnetic NMR restraints, dynamic aggregation imaging, and low-field MRI applications. Awards & Grants: No explicit awards mentioned, but his extensive publications reflect significant contributions to NMR methodology. Active in securing grants for structural biology and biomedical imaging projects. Labs & Teams: Leads the Magnetic Resonance Center’s NMR group, collaborating on interdisciplinary projects involving biomaterials, drug design, and protein engineering. His team develops cutting-edge tools for in-cell NMR and metabolomics analysis.

Alfred Steinمشاهده پروفایل

استاد

• Spatial Statistics
• Image Analysis
• Remote Sensing
• +۷ مورد دیگر

Prof. Alfred Stein is a Full Professor in Spatial Statistics and Image Analysis at the Department of Earth Observation Science, Faculty ITC, University of Twente. He earned his MSc in Mathematics and Information Science from Eindhoven University of Technology and a PhD in Spatial Statistics from Wageningen University. His career spans roles at Wageningen University (1988–2002), ITC (2002–present), including leadership positions as department head, vice-rector research, and portfolio holder for education. Education: MSc (Eindhoven University of Technology), PhD (Wageningen University) Leadership: Department Head (Earth Observation Science), Vice-Rector Research (2008–2012), Portfolio Holder Education (2012–) His research focuses on Spatial and Spatio-Temporal Statistics , emphasizing Bayesian inference , data quality , image analysis , and fuzzy techniques . Key application domains include agriculture, health, urban land use, coastal systems, hazards, and wildlife. He has mentored over 30 PhD students since 1998, with 11 currently under supervision. Recent research trends highlight AI-driven remote sensing for glacier mapping, urban livability, and disease modeling. Publications span Deep Learning for SAR tomography, Bayesian hierarchical models for health data, and multitemporal SAR analysis for environmental monitoring. Awards include the Best Paper Award (2019) and ISARA Founder's Award (2020) . Scientific Awards Best Paper Award (2019) ISARA Founder's Award (2020) As Editor-in-Chief of Spatial Statistics and associate editor for multiple journals, he leads academic discourse. Collaborations include the University of Cape Town and University of Pretoria as Honorary Professor. His work contributes to UN Sustainable Development Goals, particularly in climate action and sustainable cities.

Robson De Grandeمشاهده پروفایل

دانشیار

• Vehicular Networks
• Intelligent Transportation Systems
• Cloud Computing and Networking
• +۵ مورد دیگر

Robson E. De Grande is an Associate Professor in the Department of Computer Science at Brock University, Canada. He holds a PhD from the University of Ottawa (2012) and BSc/MSc degrees from the Federal University of São Carlos, Brazil. His research focuses on vehicular networks, intelligent transportation systems, distributed systems, and cloud computing. He serves on program committees for conferences like DS-RT, MobiWac, and MSWiM, and has organized multiple workshops and special sessions. Education: PhD in Computer Science, University of Ottawa, Canada (2012) MSc and BSc in Computer Science, Federal University of São Carlos, Brazil (2006, 2004) Research Interests: Vehicular Networks (5G, Handover Management) Edge Computing and IoT Performance Modeling/Simulation High-Performance Distributed Systems Intelligent Transportation Systems Publications: Over 100 peer-reviewed articles across journals like IEEE Transactions on ITS, Elsevier Internet of Things, and conferences like IEEE ICC and ACM MobiWac. Recent work emphasizes ML-driven vehicular network optimization and distributed simulation frameworks. Teaching: Teaches Advanced Computer Networks (COSC 4P14), Parallel Computing (COSC 3P93), and graduate-level Mobile Cloud Computing courses. Research Team: Supervises PhD/MSc students and undergraduate researchers in topics like vehicular edge computing, traffic prediction, and simulation systems.

• VLSI CAD
• Low-power IC Design
• Mixed-signal IC Design
• +۵ مورد دیگر

Riadul Islam serves as an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), maintaining his primary office in room 316 of the Information Technology and Engineering (ITE) Building. His academic appointment focuses on hardware design and verification within the institution's engineering framework. His educational qualifications include: Ph.D. in Computer Engineering from UCSC (2017) M.A.Sc. in Electrical and Computer Engineering from Concordia University, Montreal (2011) B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2007) Professor Islam's research centers on VLSI CAD tools and low-power digital/mixed-signal IC design , with significant contributions to current-mode clock networks, vehicular security systems, and error-robust circuit architectures. His work increasingly integrates machine learning for design automation while exploring neuromorphic computing applications and secure hardware implementations. This multidisciplinary approach bridges traditional IC design with modern AI-driven optimization techniques. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) Machine learning applications in early-stage Design Rule Checking (DRC) prediction and clock network optimization, (2) Graph-based intrusion detection systems for automotive networks (particularly CAN bus security), and (3) Event-based vision systems and neuromorphic computing architectures. These areas demonstrate consistent innovation in merging hardware design with AI/ML methodologies for enhanced system reliability and efficiency. He directs the UMBC VLSI and SoC Research Group , which develops energy-efficient clocking networks, secure vehicular communication protocols, and compute-in-memory architectures. The lab maintains active collaboration with industry partners on hardware security and neuromorphic computing initiatives while supporting graduate student research in cutting-edge IC design methodologies.

• Wireless Sensor Networks
• Indoor/Outdoor Localization
• QoS/QoE in Vehicular Networks
• +۹ مورد دیگر

Mohammad Ali Salahuddin is a Research Assistant Professor at the David R. Cheriton School of Computer Science, University of Waterloo , specializing in networking and machine learning. He holds a Ph.D. in Computer Science from Western Michigan University (2014), with prior academic roles at Université du Québec à Montréal and Concordia University. His research spans 5G network slicing, vehicular networks, and secure content delivery systems. Education: Ph.D. (2014, Western Michigan University); M.S. (2003, Western Michigan University); M.S. (2001, SZABIST); B.S. (1999, FAST-NUCES) Dr. Salahuddin's research focuses on 5G/6G network softwarization , autonomous threat mitigation , and machine learning for network management . His work integrates reinforcement learning and federated learning for scalable solutions in SDN/NFV , IoT , and edge computing . Recent studies address data drift in encrypted traffic classification and DDoS detection using outlier exposure-based federated learning . He has received multiple best paper awards at IEEE/IFIP NOMS (2023, 2022), IEEE CNOM (2021), and Kenneth C. Sevcik Outstanding Student Paper Award (ACM SIGMETRICS, 2021). His NSF-funded projects include vehicular cloud resource management and localization techniques. Dr. Salahuddin actively contributes to academic service as Vice-Chair of IEEE KW Section's Communications Society and TPC member for top conferences.

• In-Memory Computing
• Energy-Efficient Systems
• Machine Learning Accelerators
• +۳ مورد دیگر

Prof. Naresh R. Shanbhag holds the Jack S. Kilby Professorship in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. He is affiliated with the Coordinated Science Lab and the Information Trust Institute, both part of the College of Engineering. His research focuses on energy-efficient computing architectures, in-memory computing, nanoelectronics, and secure machine learning systems. Prof. Shanbhag has pioneered work on statistical error compensation techniques and resistive crossbar-based architectures, addressing critical challenges in low-power, high-performance computing. Education and career details are not explicitly provided in the text, but his extensive publications (313+) and honors indicate deep academic involvement. His research interests emphasize the intersection of hardware architecture and machine learning, with a focus on emerging technologies like MRAM and SRAM-based in-memory computing. Key awards include the IEEE Fellow designation (2006) and the prestigious Semiconductor Industry Association (SIA) University Research Award (2018). His recent work explores security vulnerabilities in in-memory architectures, energy-accuracy trade-offs in resistive systems, and adversarial robustness of neural networks. Prof. Shanbhag’s contributions span over 20 years, bridging theoretical computer science with practical hardware implementations. His lab (Coordinated Science Lab) and collaborations drive innovations in nanoscale information processing and trustworthy computing systems.

Konrad Kowalczykمشاهده پروفایل

دانشیار

• Speech and Audio Signal Processing
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.

Baibhab Chatterjeeمشاهده پروفایل

استادیار

• Biomedical IoT
• Wireless Implants
• Low-Power Circuits
• +۲ مورد دیگر

Baibhab Chatterjee is an Assistant Professor in Electrical and Computer Engineering at the University of Florida. His research focuses on developing energy-efficient integrated circuits and systems for biomedical IoT applications, neural implants, and secure body area networks. Research interests include ultra-low power circuits for biomedical sensors, novel wireless communication techniques through human tissues, brain-computer interfaces, and hardware security for implantable devices. His lab develops innovative solutions for next-generation healthcare monitoring and intervention systems. Recent publications demonstrate expertise in wireless power transfer for implants, in-memory computing architectures, and communication techniques optimized for the human body environment. The work consistently addresses challenges of miniaturization, power efficiency, and reliability in biomedical systems. Best 3-Minute Thesis Award, IEEE Microwave Week 2020 Best Paper Awards at IEEE CICC and HOST conferences Bilsland and Andrews Fellowships

• Cyberphysical systems
• Internet of Things (IoT)
• Distributed systems
• +۷ مورد دیگر

Prof. Dimitrios Georgakopoulos is a Professor of Computer Science at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. He serves as Director of the ARC Industrial Transformation Research Hub for Future Digital Manufacturing and Swinburne's IoT Lab. Previously, he was Research Director at CSIRO's ICT Centre and a Professor at RMIT University. Affiliations: CSIRO Adjunct Fellow since 2014 Leadership: Directed 7 large cross-disciplinary initiatives with $100M+ funding Research Focus: IoT, Cyber-Physical Systems, Digital Manufacturing, Machine Learning Funding: Secured $77.1M in external grants; $59.7M at Swinburne Research Interests: Digital twins and AI for manufacturing IoT sensor sharing ecosystems 5G-enabled smart cities Autonomic IoT systems Quality assurance in Industry 4.0 Awards: 2023 National iAward (Public Sector), Vice Chancellor’s Innovation Award (2018), and multiple industry and academic recognitions. Grants & Projects: Lead researcher on ARC-funded initiatives in digital manufacturing, cybersecurity, and steel innovation. Collaborates with industry partners like Bega Cheese and FIA on IoT-driven solutions. Labs & Teams: Oversees Swinburne's IoT Lab and the ARC Future Digital Manufacturing Hub, advancing Industry 4.0 applications in manufacturing, healthcare, and smart infrastructure.

• Intelligent Distributed Systems
• Network Security
• Edge Computing
• +۱ مورد دیگر

Dr. Raphaël Frank is an Assistant Professor and Senior Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where he leads the UBIX Research Group and directs the ATLAS IPBG Programme. With over 80 peer-reviewed publications, his research focuses on intelligent distributed systems, network security, and ubiquitous computing architectures. Dr. Frank serves on the University Council and actively participates in international scientific committees as a senior IEEE member. His professional experience includes co-founding Motion-S (the University of Luxembourg's first tech spin-off) where he served as Operations Director. He holds a Master's in Cryptography and Network Security from Université Joseph Fourier and a Ph.D. in Computer Science from University of Luxembourg, including research at UCLA.

Steffen Beckerمشاهده پروفایل

استاد

• Software Engineering
• Cloud Systems
• Model-Driven Engineering
• +۷ مورد دیگر

Steffen Becker is a Professor at the University of Stuttgart's Faculty of Computer Science, Electrical Engineering and Information Technology, affiliated with the Institute for Software Engineering's Software Quality and Architecture group. His work focuses on software engineering, cloud systems, model-driven engineering, and cybersecurity. He leads research in architectural modeling tools like Slingshot, GUI testing frameworks (ViMoTest), and hardware security analysis. Recent studies explore AI integration in testing, education, and automotive systems (CARISMA). Research interests include elasticity modeling, self-adaptive systems, and educational technology. His 2025 publications address issues like end-user hardware comprehension, FPGA security, and LLM-driven test generation. Notable tools developed include the Slingshot Simulator for cloud-native systems and Gropius for cross-component issue management. Becker contributes to both theoretical advancements and practical implementations in software quality, security, and cloud infrastructure. Key Areas: Software Architecture, Cyber-Physical Systems, Testing, Reverse Engineering Tool Developments: ViMoTest, Slingshot, Gropius Education Focus: Online programming pedagogy and curriculum innovation His work bridges foundational research with industry applications, addressing challenges in automotive computing, cloud elasticity, and human-centric security awareness. Recent efforts emphasize explainable hardware (XHW) and AI's role in qualitative analysis automation.

Stavrakakis Ioannisمشاهده پروفایل

استاد

• Networking
• Wireless Communication
• Distributed Systems
• +۵ مورد دیگر

Stavrakakis Ioannis is a Professor at the Department of Informatics and Telecommunications, School of Science, University of Athens, where he has served since 2002. He previously held academic positions at Northeastern University (1994-1999) and University of Vermont (1988-1994). Ph.D., Electrical Engineering (1988), University of Virginia Diploma, Electrical Engineering (1983), Aristotle University of Thessaloniki His research focuses on network resource allocation algorithms , cooperative content dissemination , mobile ad hoc networks , and privacy-aware protocols . He leads the Advanced Networking Research (ANR) Group. Recent publications highlight trends in AI-driven network optimization , edge computing for VR , drone-assisted sensor networks , and privacy in vehicular systems . Key themes include game theory applications, energy-efficient protocols, and distributed learning frameworks. Contact: ioannis@di.uoa.gr

Greg Gangerمشاهده پروفایل

استاد

• Cloud Computing
• Storage Systems
• Operating Systems
• +۳ مورد دیگر

Greg Ganger is the Jatras Professor of Electrical and Computer Engineering at Carnegie Mellon University and Director of the Parallel Data Lab (PDL). His research focuses on computer systems, including cloud computing, storage systems, distributed systems, and machine learning infrastructure. He holds a Ph.D. in Computer Science and Engineering from the University of Michigan and completed postdoctoral work at MIT. Education: Ph.D., M.S., and B.S. in Computer Science from the University of Michigan (1991–1995). Research Interests: Ganger leads projects in cloud computing, storage/file systems, operating systems, and systems for big data and large-scale machine learning. Recent work includes optimizing cloud resource scheduling, developing sustainable storage solutions, and improving ML cluster efficiency. The PDL explores storage system architecture, file systems, and leveraging new storage technologies like non-volatile memory (NVM). Awards: 2021 OSDI Best Paper, 2021 SOSP Best Paper, 2021 SoCC Test of Time Award, and 2021 R&D 100 Award. His team's work on Kangaroo caching and MACARON cloud caching exemplifies cutting-edge contributions. Advising & Grants: Advises graduate students in ECE and Computer Science. Active in grants related to distributed storage, cloud systems, and ML infrastructure. Collaborates with industry partners like Los Alamos National Lab on storage systems. Labs/Teams: Directs the Parallel Data Lab (PDL), a leading research group in storage and distributed systems. Collaborates with CMU’s CyLab on security aspects of storage systems and ML infrastructure.