جستجوی استاد راهنمای دانشگاهی

Chrysanthos Maraveasمشاهده پروفایل

استادیار

• Agricultural Engineering
• Sustainable Materials
• Artificial Intelligence Applications
• +۵ مورد دیگر

Chrysanthos Maraveas is an Assistant Professor at the Department of Technology and Natural Resources Management & Agricultural Engineering in the School of Environment and Agricultural Engineering at the Agricultural University of Athens since 2022. He earned his PhD in Structural Engineering from the University of Manchester and has held postdoctoral research positions at the University of Liège (EU-funded) and the University of Patras (Greek Ministry of Development-funded). Research Interests : Applications of AI and quantum computing in agriculture Sustainable materials and biodegradable polymers from agricultural waste Internet of Things (IoT) for greenhouse optimization Structural durability and corrosion resistance in agricultural environments Plastic waste management in agrifood systems 4D printing for sustainable agricultural plastics Scientific Recognition : Ranked in the top 2% of scientists worldwide by Stanford University (based on citations and h-index) Publications focus on: Cybersecurity in Agriculture 4.0/5.0 Smart sensors and edge computing for resource management Biopolymer innovations and nanotechnology Structural analysis of agricultural systems Sustainable construction materials from agro-waste Fire resistance in steel and composite structures

• Internet of Things (IoT)
• Fog Computing
• Edge Computing
• +۱۶ مورد دیگر

Amir Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, and mobility in distributed systems for emerging technologies like IoT, Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). University: University of Oslo Department: Informatics Academic Rank: Professor His research spans IoT, Edge/Fog Computing, and Cyber-Physical Systems, emphasizing energy efficiency, privacy preservation, and self-adaptive architectures. Key areas include network traffic classification, computation offloading, and federated learning applications in vehicular systems. Recent publications highlight advances in latency-aware IoT data transmission , federated vehicular networks , energy-efficient wireless charging , and privacy-preserving data integration . These works often integrate machine learning with network optimization. Projects include the CPS Lab at UiO, DILUTE (Fluid Service Abstraction), and the Gemini Centre on IoT . He collaborates on initiatives like PACE for energy informatics curricula development.

James Schnableمشاهده پروفایل

استاد

• Plant Genomics
• Crop Phenomics
• Quantitative Genetics
• +۳ مورد دیگر

James Schnable serves as the Charles O. Gardner Professor of Agronomy in the Department of Agronomy and Horticulture at the University of Nebraska-Lincoln. His research integrates genomic, phenomic, and environmental data to advance crop breeding methodologies for maize and sorghum, with particular emphasis on climate-resilient varieties. Dr. Schnable's research program spans plant genomics , high-throughput phenomics , and quantitative genetics , focusing on genetic dissection of nitrogen use efficiency, photosynthetic traits, and stress tolerance mechanisms. His laboratory pioneers UAV- and satellite-based phenotyping systems, machine learning applications for image analysis, and genomic prediction models that bridge genotype-phenotype gaps under variable environmental conditions. Key innovations include nighttime fluorescence phenotyping to reduce environmental noise and spectral feature extraction for accelerated trait assessment. Analysis of his 2021-2025 publications reveals consistent leadership in genotype-environment interaction studies and computational phenotyping , with dominant themes including transcription factor binding site variation explaining heritability, nonphotochemical quenching kinetics in stress responses, and scalable satellite-based yield prediction methods. His work frequently employs the Genomes to Fields Initiative infrastructure for multi-state field validation. Charles O. Gardner Professorship Dr. Schnable directs an active research program involving advanced sensor networks, genomic selection pipelines, and multi-institutional field trials. His team develops computational frameworks like PlantSegNet for 3D plant reconstruction and SPARC-LoRa for agricultural IoT applications. While specific grant details aren't provided in source materials, his extensive publication record in high-impact journals indicates sustained funding from major agricultural research programs. The laboratory maintains cutting-edge phenotyping infrastructure including UAV fleets, hyperspectral imaging systems, and gas sensor networks for early stress detection. Current projects focus on nitrogen-responsive growth trajectories, chilling tolerance mechanisms in panicoid grasses, and gut microbiome interactions with grain composition, reflecting an integrative approach from molecular mechanisms to field performance.

Yuvraj Agarwalمشاهده پروفایل

استاد

• Systems and Networking
• Embedded Systems
• Mobile Computing
• +۴ مورد دیگر

Yuvraj Agarwal is a Professor at Carnegie Mellon University's School of Computer Science, where he founded and directs the SYNERGY Lab. He previously served as Executive Director of the NSF Expeditions in Variability (2010-2013) and was affiliated with UCSD's Microelectronic Embedded Systems Lab (MESL) and Systems and Networking Group (SysNet). Research Themes : Systems & Networking, Embedded Systems, Mobile Computing, with focus on energy efficiency and privacy. Leadership : Director of SYNERGY Lab, Co-PI in NSF CoDec expedition, Brick Consortium member. His research bridges hardware-software systems with societal impact, including smart building energy optimization using occupancy sensing, mobile privacy tools like ProtectMyPrivacy, and IoT security labeling frameworks . Recent work includes the Computational Decarbonization NSF expedition ($12M over 5 years) to reduce carbon footprints in societal infrastructure. Scientific Awards : 2024: Promoted to Full Professor 2012: UCSD Outstanding Faculty Award for Sustainability 2016: Google Faculty Research Award Advising : Guided 11 PhD students to completion (now in academia/startups) and mentored 10+ MS/undergraduate researchers. Current advisees include 4 PhD students in Carnegie Mellon's Societal Computing program. Grants : NSF Expeditions in Variability (2010-2013), NSF #1564009 (2016), NSF #1526237 (2015), NSF #1513957 (2015), DARPA BRANDEIS program (2015), and Google research funding (2015). Labs & Collaborations : SYNERGY Lab at CMU focusing on IoT and smart campus systems. Key collaborations with the Brick Consortium (Johnson Controls, Schneider Electric), Microsoft Research, Intel Research, and academic institutions including UCSD and UMass Amherst.

• Cybersecurity
• Machine Learning
• Network Security
• +۶ مورد دیگر

Dr. Zhiyuan Tan is an Associate Professor in the School of Computing at Edinburgh Napier University (ENU), specializing in cybersecurity research. He holds a PhD in Computer Systems from the University of Technology Sydney (UTS), Australia (2014), an MEng from Beijing University of Technology, China (2008), and a BEng with high distinction from North-eastern University, China (2005). Before joining ENU in 2016, Dr. Tan held research positions at the University of Twente (Netherlands), University of Technology Sydney (Australia), and La Trobe University (Australia). Dr. Tan's research focuses on cybersecurity, machine learning, data analytics, virtualisation, and cyber-physical systems. His work has resulted in over 44 scholarly publications with an H-Index of 13 and more than 830 citations according to Google Scholar. His recent publications demonstrate a continued focus on network security, intrusion detection systems, and the application of machine learning techniques to cybersecurity challenges, with publications spanning from 2022-2025 in top venues including IEEE Transactions and international conferences. Dr. Tan has received significant research funding, including AUD 27,800 from CSIRO and UTS for autonomous network intrusion detection research and £6,987 from ENU for securing future 5G health care systems. His research has been recognized with awards including the National Research Award 2017 from the Research Council of the Sultanate of Oman, a Best Paper Award, and the Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award. National Research Award 2017 from the Research Council of the Sultanate of Oman Best Paper Award Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award Dr. Tan has mentored 9 PhD students over the past 5 years, with 6 successfully completing their studies. His students have produced 12 journal and 10 conference publications. He has also served as an editorial board member for international journals, organized special issues, and participated as a technical program committee member for major international conferences. Dr. Tan is currently recruiting PhD students for research projects on network security, adversarial machine learning for anomaly/malware detection, virtualization security, and IoT security.

Sangtae Haمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning Systems
• Networks
• +۶ مورد دیگر

Sangtae Ha is an Associate Professor in the Computer Science Department at the University of Colorado Boulder. His research focuses on building practical computer systems spanning multiple disciplines, including machine learning/deep learning systems, networks and distributed systems, internet protocols, wireless networks, video streaming, storage systems, and security. He specializes in creating efficient and scalable solutions for real-world computing challenges. His work emphasizes interdisciplinary approaches, bridging theoretical computer science with practical system design. Key areas of exploration include optimizing neural network execution for edge computing, developing adaptive streaming protocols, and enhancing wireless network performance through novel signal processing and spectrum management techniques. Recent projects include frameworks for semantic offloading in neural networks and reinforcement learning-based cloud scheduling. No scientific awards or notable grants are explicitly mentioned in the provided materials. While no formal advisees are listed, his research team likely involves graduate students and collaborators. No dedicated labs or teams are named, though his work aligns with broader university initiatives in computer systems and networking.

• Information Systems Engineering
• Privacy Threat Modeling
• IoT Security
• +۴ مورد دیگر

Dimitri Van Landuyt serves as an Associate Professor in the Department of Computer Science at KU Leuven , affiliated with the Information Systems Engineering Research Group (LIRIS) . He leads and co-promotes multiple high-impact research projects focused on security and privacy engineering, including initiatives on model-driven security risk analysis , privacy by design , and IoT security . His work spans GDPR compliance, synthetic data management, and threat modeling innovations. Academic Leadership : Member of the Council of FEB and Campus Council Leuven/Kortrijk Research Pillars : Privacy threat modeling, security automation, IoT systems, GDPR technical implementation His publications demonstrate expertise in privacy-enhancing technologies, with recent work analyzing LLM applications in threat modeling, developing tree-based privacy analysis frameworks, and creating adaptive trust management architectures. He explores serious games for security training, synthetic data quantification standards, and runtime threat assessment mechanisms. Dimitri contributes to educational programs through courses in ICT Service Management , Security & Privacy by Design , and Research Methodologies . He supervises student research while collaborating with industry and academia on data protection challenges.

Ghadeer Yassinمشاهده پروفایل

مدرس

• Internet of things
• Cloud and edge computing
• Smart communities and security

Ghadeer Yassin is a Lecturer in the School of Computing at Binghamton University. She holds a PhD in Computer Science from the University of Georgia, along with a Software Engineering Diploma from ITI, Egypt, and a BSc from Mansoura University. Her research focuses on IoT, cloud/edge computing, and securing smart communities. Her work emphasizes practical solutions for integrating IoT technologies into smart communities while addressing security challenges. Recent publications explore semantic access control for smart farms and optimizing machine learning systems via user feedback analysis. Yassin has received awards including the Outstanding Teaching Assistant honor, NSF Travel Award, and a Ministry of Communication scholarship. She previously worked as a software engineer and lab instructor. While no specific advising or grants are detailed, her teaching credentials include the GradTeach certificate. No dedicated labs or teams are explicitly mentioned in her profile.

• Network Protocols Engineering
• Cybersecurity
• Distributed Systems
• +۳ مورد دیگر

Klaus Wehrle is a Professor of Computer Science and Head of the Communication and Distributed Systems Group (Informatik 4) at RWTH Aachen University. He holds a diploma (1999) and doctorate (2002) from the University of Karlsruhe (now KIT). His research focuses on network protocols, cybersecurity, and distributed systems, funded by DFG, ERC, EU, and industry. He leads the DFG Priority Program on Cyber-Physical Networking and holds ERC grants. Awards include Südwestmetall and FZI Dissertation Prizes. He serves on IEEE, ACM, and DFG committees. His work spans protocol engineering, network simulation, and reliable communication software. He advises the Department of Computer Science at RWTH and collaborates on projects like In-Network Computing and Industrial IoT security. His research addresses industrial systems, smart grids, and privacy-preserving data sharing.

Jeremy Gummesonمشاهده پروفایل

استادیار مدعو

• Wearable Technology
• Energy Harvesting
• Wireless Communication Systems
• +۷ مورد دیگر

Jeremy Gummeson is an Adjunct Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Science. He currently works in the Sensors Lab under Deepak Ganesan and the Laboratory for Advanced Systems Software under Prashant Shenoy. His research focuses on energy harvesting for wearables, backscatter communication systems, and distributed wireless sensing technologies. Education: Ph.D. in Computer Systems Engineering from the University of Massachusetts Amherst (2013). Research Interests: Jeremy’s work spans energy-efficient wearable devices, privacy in sensor systems, metamaterial-based mmWave networks, and low-power environmental monitoring. His innovations include intra-body power transfer for health wearables and acoustic-based sensing for vision-impaired environments. Recent projects emphasize sustainable computing and secure authentication mechanisms within smart spaces. Article Trends: His recent publications (2024-2025) highlight advancements in privacy-preserving audio systems, metamaterial networks, and intra-body communication. These reflect a focus on integrating biomedical applications with cutting-edge wireless technologies to enhance both functionality and security. Leading the Way award (Hewlett Packard, 2015) IEEE Outstanding Senior (2006) Betterment of the ECE Department award (2006) Outstanding Teaching Assistant (2006) Advising & Grants: Jeremy has no listed advisees. He has served as a reviewer for ACM MobiSys, ACM UbiComp, ACM CHI, IEEE RFID, and multiple transactions journals. As Web Chair for ACM HotMobile (2016), he contributed to conference organization. His current lab affiliations drive collaborative research in sensor systems and network software. Labs/Teams: Active member of the Sensors Lab and the Laboratory for Advanced Systems Software, working on projects involving energy autonomy, wireless communication, and health monitoring through wearable technologies.

Venkatesh Saligramaمشاهده پروفایل

استاد

• Machine Learning
• Video Analysis
• Statistical Signal Processing
• +۳ مورد دیگر

Venkatesh Saligrama is a Professor in the Department of Electrical and Computer Engineering at Boston University, with affiliations in Systems Engineering, the Center for Information and Systems Engineering, and the College of Arts & Sciences’ Computer Science division. He holds a PhD from MIT (1997) and leads the Data Science & Machine Learning Lab. His research focuses on Machine Learning, Video Analysis, Statistical Signal Processing, and Network Science, with notable contributions to vision-language models, bandit algorithms, and synthetic data strategies. He has received prestigious awards like the NSF CAREER Award (2005) and Presidential Early Career Award (2003). His teaching includes courses on signals, stochastic processes, and digital communication systems. Saligrama’s work bridges theoretical foundations and applied systems, emphasizing efficient learning under constraints. His recent publications explore zero-shot detection, data-efficient pretraining, and safe optimization techniques. Beyond academia, his research impacts domains like autonomous systems, healthcare prediction, and cybersecurity. He actively contributes to editorial roles, conferences, and interdisciplinary initiatives, fostering collaborations across engineering and computer science.

Erik Mannensمشاهده پروفایل

استاد

• Semantic Intelligence
• Big Data Analytics
• Semantic Web
• +۴ مورد دیگر

Erik Mannens is a Professor at Ghent University and Research Valorisation Director at imec, specializing in Semantic Technologies and Artificial Intelligence since 2005. He leads the Data Science team at IDLab, managing over 50 researchers focused on advancing semantic data science and its applications. Education: PhD in Computer Science Engineering (2011), Master’s in Computer Science (1995), and Master’s in Electro-Mechanical Engineering (1992) The team explores the fusion of semantics and AI, aiming to: (1) analyze the Web’s societal impact, (2) study Web-enabled lifestyle changes, and (3) develop future Web innovations. Their work emphasizes Open Source , Open Access , and Open Knowledge principles. His research spans Semantic Intelligence , Big Data Analytics , and Linked Data applications, with notable publications on RDF mappings, IoT platforms, and distributed data querying. Key projects include MapVOWL, RMLEditor, and the MASSIF platform. Current affiliations include Ghent University’s IDLab , where he contributes to cutting-edge research in data science and semantic technologies.

Xin Liuمشاهده پروفایل

استاد مدعو

• Machine Learning
• Cybersecurity
• Network Security
• +۳ مورد دیگر

Xin Liu is an Adjunct Professor in the Department of Civil Engineering at the Faculty of Engineering. His research focuses on cybersecurity, machine learning applications, and IoT security, with a particular emphasis on network intrusion detection, data compression, and risk-aware access control systems. He holds a Ph.D. from the University of Ottawa, Canada, and M.Sc. and B.Sc. degrees from Hebei University of Technology, China. Education: Ph.D., University of Ottawa, Canada M.Sc., Hebei University of Technology, China B.Sc., Hebei University of Technology, China Research Interests: Dr. Liu's work bridges machine learning and cybersecurity, addressing challenges in IoT security, adversarial attacks, and network traffic analysis. His contributions include developing AI-driven intrusion detection systems, optimizing data compression techniques for IoT devices, and formulating risk-aware access control frameworks. Recent efforts focus on advanced persistent threats (APTs) and privacy leakage mitigation in language models. Articles Trends: His publications emphasize practical applications of machine learning in cybersecurity, including network attack detection, privacy-preserving methods, and resilient IoT infrastructure. Recent work highlights innovations in transformer-based models for intrusion detection and realistic benchmarking for APT simulations. Awards: No scientific awards explicitly mentioned in the provided materials. Advising & Grants: No advising records or grant information available in the current data. Labs/Teams: No specific lab affiliations or collaborative teams noted in the profile.

Paria Shiraniمشاهده پروفایل

استادیار

• Cybersecurity
• IoT Security
• Vulnerability Research
• +۴ مورد دیگر

Paria Shirani is an Assistant Professor and Tier 2 Canada Research Chair in Cybersecurity at the School of Electrical Engineering and Computer Science (EECS), University of Ottawa. She holds a PhD in Information Systems Engineering from Concordia University (FRQNT Doctoral Scholarship recipient) and completed an NSERC Postdoctoral Fellowship at Carnegie Mellon University (CMU). Her research focuses on cybersecurity, including IoT security, vulnerability detection, malware analysis, threat intelligence, and AI/ML applications. She leads funded projects across undergraduate, master’s, PhD, and postdoctoral levels, emphasizing equity, diversity, and inclusion (EDI). Research Highlights: Develops AI-driven solutions for IoT security and vulnerability detection. Pioneers binary code fingerprinting and firmware analysis techniques. Advances threat intelligence through machine learning and anomaly detection. Key awards include the NSERC Postdoctoral Fellowship, FRQNT Doctoral Scholarship, and the Tier 2 Canada Research Chair. Collaborations involve institutions like Concordia University, Carnegie Mellon University, and IBM’s Cyber Range. She actively serves on editorial boards (e.g., ACM Computing Surveys) and organizes conferences (e.g., SecureComm, PST).

Mohamed-Lamine Messaiمشاهده پروفایل

استاد

• Key Management in IoT Networks
• Cybersecurity
• Networking Security
• +۳ مورد دیگر

Mohamed-Lamine Messai is a Professor at Université Lyon 2, France, specializing in cybersecurity, IoT security, and networking. He teaches courses such as Computer Security, Hacking Labs, and Cryptography, and is actively involved in research projects like ROMANCE (knowledge graph modeling for organizational vulnerabilities) and GLADIS (graph-based intrusion detection). His work focuses on key management systems, resource-constrained networks, and AI-driven cybersecurity solutions. He has supervised numerous PhD and master's students, including Abel Oroke, Floribert Katembo, and Sami Bettayeb. His research spans IoT security, blockchain applications, and graph neural networks for threat detection. He frequently presents at international conferences and workshops, including GRASEC, ARES, and IWCMC. Current projects include Federated Learning with Hybrid Encryption for APT detection and autonomous bio-inspired 5G deployment strategies. Key Projects: ROMANCE, GLADIS, CyberSecGraph, DATACAP Teaching: M1/M2 courses in Computer Security, IoT, Networking, and Distributed Computing Lab Affiliations: Involved in L3i lab activities and Open Days at the University of La Rochelle