جست‌وجوی استاد و پژوهشگر

• Organizational Behavior
• Experimental Economics
• Entrepreneurship
• +۵ مورد دیگر

Alessandro Rossi is an Associate Professor in the Department of Economics and Management at the University of Trento, Italy. He holds roles including Rector’s Delegate for Support to the Productive System and coordinates doctoral programs in Innovation & Entrepreneurship. He earned a Laurea in Business Economics from Ca’ Foscari University of Venice (1995), a PhD in Organization and Management of Enterprises from the University of Udine (2000), and conducted postdoctoral research at the University of Trento (2000-2002). His research focuses on organizational processes in knowledge-intensive sectors, particularly software industries, using experimental and computational methods. Key interests include innovation, decision-making, and open-source ecosystems. He leads projects funded by EU initiatives (e.g., Communia Network, LiquidPublication) and national grants (MIUR-PRIN, COFIN). He teaches courses such as Start-Up Lab, Managerial Decision Models, and Innovation in Local Manufacturing Systems. As an advisor, he supervised 98 theses (69 bachelor’s, 29 master’s). Awards include HICSS-43 Best Paper Nomination (2010) and a 2001 Young Researchers Grant. Key Roles: Doctoral Program Coordinator, Erasmus Program Coordinator, Open Innovation Researcher Research Projects: LiquidPublication (FP7), Designing Complex Artifacts (MIUR-PRIN), Open Source Software Networks Professional Engagements: Program Committee Member (Wikisym, EURAM), Scientific Board (Open Culture Conferences) His work bridges theoretical economics with empirical studies, emphasizing organizational design and open innovation dynamics.

Yashar Deldjooمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

Yashar Deldjoo is an Assistant Professor specializing in artificial intelligence, machine learning, and their applications in healthcare, user modeling, and recommender systems. His research focuses on explainable AI, ethical AI practices, multimodal learning, and brain-computer interfaces. He has contributed to projects like WeBIUM (Wearable Devices and Brain-Computer Interfaces for User Modelling) and ARIEL (emotional support via BCI and LLMs). His work bridges technical advancements with societal impacts, addressing challenges in healthcare diagnostics (e.g., Alzheimer’s disease prediction), environmental sensing (real-time pollution-aware recommendations), and cybersecurity (data poisoning attacks on recommendation systems). He also explores fairness in large language models and the integration of EEG-based emotion recognition systems. Notable contributions include frameworks like BRAINEX (CNN model evaluation for brain age prediction), Ducho (multimodal feature extraction for recommendation systems), and KGUF (knowledge-aware graph-based recommenders). He chairs conferences like RecSys and contributes to industry-relevant standards for trustworthy AI systems. His research emphasizes interdisciplinary collaboration, combining neural networks, ethical considerations, and human-computer interaction. Current projects involve neurorehabilitation tools, quantum security for smart cities, and AI-driven usability testing.

FRANCISCO JAVIER GARCIA POLOمشاهده پروفایل

استاد

• Artificial Intelligence
• Reinforcement Learning
• Robotics
• +۴ مورد دیگر

Francisco Javier Garcia Polo is a Professor at the University of Santiago de Compostela (USC) , affiliated with the Department of Electronics and Computing within the Higher Polytechnic School of Engineering . His research focuses on Reinforcement Learning (RL) , particularly in areas such as safe decision-making, adversarial attack detection, and policy transfer across tasks. He holds a PhD from Universidad Carlos III de Madrid (2013), where his thesis explored safe RL in continuous state-action spaces. Key research interests include enhancing RL robustness against adversarial attacks, federated learning applications in robotics, and applying RL to financial systems like automated market makers. He is part of the GSI Group (Intelligent Systems Group) , contributing to projects involving social assistive robotics and multi-modal transportation planning tools like TIMIPLAN. His work bridges theoretical advancements with practical domains such as healthcare robotics, cybersecurity, and economic modeling. Publications highlight innovations in policy defense mechanisms, similarity metrics between Markov Decision Processes, and frameworks for evaluating assistive robots. Despite his prolific output, he has not publicly shared research papers on Academia.edu. Collaborations span interdisciplinary topics, reflecting a commitment to both foundational AI research and real-world applications.

Valeria Cardelliniمشاهده پروفایل

دانشیار

• Distributed Computing Systems
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Valeria Cardellini is an Associate Professor in Computer Engineering at the University of Rome Tor Vergata, with a PhD in Computer Science and Automation Engineering (2001). Her research focuses on distributed computing systems, particularly cloud-based and edge-based systems, with emphasis on resource provisioning, self-adaptation, and QoS-driven optimization. She has published over 90 peer-reviewed papers, including award-winning works at IEEE SOSE and ACM DEBS conferences, and has led collaborations in EU projects like EoCoE (2015-2018) and the Cloud for Europe tender (2016-2017). She has organized major events such as UCC 2018 and contributed to workshops like Auto-DaSP. Teaching includes distributed systems, cloud computing, and computer architectures at both undergraduate and graduate levels. Education: PhD in Computer Science and Automation Engineering (2001), University of Rome Tor Vergata Previous academic qualifications not explicitly listed Research Interests: Her work spans geo-distributed systems, serverless computing, edge-cloud continuum optimizations, and reinforcement learning for adaptive systems. Key areas include energy-efficient scheduling, fault-tolerant architectures, and QoS-aware service composition. Recent projects address scalability challenges in data stream processing and cybersecurity for distributed environments. Awards & Recognition: Best Paper Awards at IEEE SOSE 2011, ACM DEBS 2015, and ACM DEBS 2016 Keynote Speaker at IEEE MASCOTS 2016 Grants & Projects: Led EU H2020 projects (EoCoE), contributed to the European Cloud tender, and managed COST Action IC1304 (ACROSS). Current research involves cloud-edge continuum frameworks like Serverledge and FIGARO. Labs & Teams: Active in developing experimental platforms such as MOSES (for self-adaptation testing) and OpenCoarrays (coarray Fortran frameworks). Collaborates with interdisciplinary teams on HPC, distributed systems, and cybersecurity.

• Plant Biology
• Metabolism
• Stress Biology
• +۳ مورد دیگر

Prof. Thomas Nägele is a Group Leader at the Department of Biology I, Ludwig-Maximilians-Universität München. His research focuses on plant metabolism, particularly cold and temperature acclimation, stress responses, and systems biology approaches. His work integrates metabolomics, mathematical modeling, and subcellular analysis to understand metabolic regulation in plants under environmental changes. Key research areas include the role of chloroplast positioning in immunity, carbohydrate metabolism dynamics, and the molecular mechanisms underlying plant adaptation to abiotic stresses. He has pioneered methods like the Dark-Root device for non-invasive root analysis and developed computational tools such as MInfer for metabolomic network analysis. His contributions span plant cold acclimation, heat tolerance, CO₂ effects, and flavonoid metabolism. He emphasizes interdisciplinary approaches, combining experimental and computational methods to address complex biological questions in plant-environment interactions. Notable achievements include defining metabolic core modules of stress response and elucidating the role of SnRK1 kinase in energy signaling. His research bridges basic science with potential applications in crop resilience under climate change.

Peter McBurneyمشاهده پروفایل

استاد

• Autonomous Agents
• Blockchains and Distributed Ledgers
• Computational Finance
• +۱۰ مورد دیگر

Peter McBurney is a Professor specializing in Artificial Intelligence, Distributed Systems, and Computational Finance. His affiliations include roles in hubs such as the Informatics Finance Hub and Distributed Artificial Intelligence Security Hub. He holds a PhD in Rational Interaction from the University of Liverpool (2002) and a Bachelor of Arts in Pure Mathematics & Statistics from the Australian National University (1980). He serves on the Academic Advisory Council of the Financial Conduct Authority since 2018. His research focuses on Autonomous Agents, Blockchain Technology, Multi-Agent Systems, and the ethical implications of AI. Recent work includes studies on Sybil Attacks in blockchain, cooperative language learning in AI, and healthcare simulation modeling. McBurney has led or co-investigated multiple research projects, including grants from the Royal Academy of Engineering and EPSRC. His 132+ publications span conferences like IJCAI and journals such as the International Journal of Parallel, Emergent and Distributed Systems. Key themes in his articles include blockchain security, AI explainability, and multi-agent coordination.

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

Dr. Kehinde Babaagba is a Lecturer at the School of Computing (Edinburgh Napier University), specializing in cybersecurity, machine learning, and evolutionary computation. She holds a PhD in Computing and has contributed to malware analysis, AI-driven solutions for real-world challenges, and cybersecurity defense mechanisms. Her research emphasizes metamorphic malware detection, generative adversarial networks (GANs), and federated learning privacy. Education: BSc (Hons) Computer Science (First Class), Redeemers University, Nigeria MSc Computing Information Engineering (Distinction), Robert Gordon University PhD in Computing, Edinburgh Napier University Research Interests: Transfer learning, computer vision, cybersecurity applications of AI, evolutionary algorithms Awards: Outstanding Contribution Award (Evo* 2020) Best Paper (3rd Place, IEEE DSC 2022) Excellent Oral Presentation (ACM ICEIT 2019) Professional Roles: IEEE Member (Women in Engineering, Computational Intelligence) STEM Ambassador Associate Fellow of Advance HE Her recent work includes developing adversarial learning frameworks for malware evolution prediction and enhancing federated learning models for privacy compliance. She actively contributes to conferences as a reviewer and organizer, including roles as Finance Chair (IEEE DSC 2022) and Publication Chair (EvoAPPS 2022).

Asad Asaduzzamanمشاهده پروفایل

مدرس

• Weed Science
• Herbicide Resistance
• Agricultural Sustainability
• +۶ مورد دیگر

Asad Asaduzzaman is a Lecturer in Cropping, Horticulture and Soil Health at Charles Sturt University (CSU), based in Wagga Wagga. He holds a PhD in canola allelopathy from CSU, advancing the concept of self-weeding crops. Previously, he served as a Research Scientist at the NSW Department of Primary Industries, uncovering globally significant findings like world's first double-knock herbicide resistance and Australia's first paraquat resistance in Conyza sumatrensis . His work on herbicide hormesis has gained international acclaim. Education: PhD in Canola Allelopathy, CSU Postgraduate in Applied Statistics Bachelor's from Sher-e-Bangla Agricultural University, Bangladesh Research Interests: Weed ecology, herbicide resistance dynamics, climate adaptation in crops/weeds, remote sensing for precision agronomy, and machine learning applications in species distribution modeling. Key areas include Feathertop Rhodes Grass management, Tropical Soda Apple biology, and arsenic contamination in Bangladesh. Key Contributions: Unveiled paraquat resistance mechanisms in tall fleabane Developed statistical models linking crop traits to weed suppression Pioneered use of deep learning for spectral weed identification Awards: Crawford Fund Australia Fellowship (2009) supported his early research at the University of South Australia. Advising & Grants: Advised multiple postgraduate researchers (details not listed). Active in Australian cotton and grain industry partnerships, with funding from agricultural extension programs. Labs/Teams: Collaborates with CSU's Graham Centre and NSW Department of Primary Industries on integrated weed management projects.

• Cybersecurity
• Blockchain Technology
• Data Privacy
• +۳ مورد دیگر

Hisham Ali is a Researcher at the School of Computing Engineering and the Built Environment at Napier University. His research focuses on cybersecurity, blockchain-based systems, and privacy-preserving technologies. He holds a Doctorate (PhD) completed in 2024, supervised by Prof Bill Buchanan, Dr Jawad Ahmad, and Dr Zakwan Jaroucheh. His work spans multiple areas including privacy-preserving machine learning, blockchain security frameworks, and IoT network protection. Notable contributions include developing homomorphic encryption schemes for secure SVM learning, analyzing blockchain threats, and creating intrusion detection systems leveraging deep learning. Recent publications (2021–2025) highlight advancements in trusted threat intelligence sharing via Hyperledger Fabric, chaos-based image encryption, and systematic reviews of blockchain vulnerabilities. His research emphasizes practical applications of cryptography and distributed ledger technologies to real-world cybersecurity challenges. Awards and grants are not explicitly mentioned in the provided texts. Hisham has collaborated extensively with industry and academic partners, contributing to both theoretical and applied cybersecurity solutions.

• Machine Learning
• Edge Computing
• Networking Technologies
• +۵ مورد دیگر

Mounir Bensalem is a Ph.D. candidate and research assistant at the Institute of Computer and Network Engineering within the Faculty of Electrical Engineering, Information Technology, and Physics at Technical University of Braunschweig, Germany. His research focuses on integrating machine learning and edge computing into next-generation network architectures, particularly in optimizing LoRa, analyzing reconfigurable intelligent surfaces (RIS), and applying reinforcement learning to edge serverless functions. He holds an Engineering Diploma and M.Sc. in Information Systems from the National Engineering School of Tunis. Education: Master’s Degree in Information System Techniques (2017), National Engineering School of Tunis. Engineering Diploma in Industrial Engineering (2017), National Engineering School of Tunis. Key Research Areas: Machine Learning, Edge Computing, 5G/6G Networks, Network Security, and Reconfigurable Intelligent Surfaces. Projects: EU Horizon MANOLO (2024–2026), H2020 FISHY (2020–2023), DFG FOR 2863 (2019–2022), and H2020 mF2C (2017–2020). Awards: Best Paper Award (2022) for work on IoT buffer size benchmarking. Lab/Team: Part of Prof. Jukan’s group at TU Braunschweig, focusing on communication networks and edge computing.

Serdar Bozdagمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۵ مورد دیگر

Serdar Bozdag is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas, with a joint appointment in Mathematics. He serves as Director of the Center for Computational Life Sciences, focusing on interdisciplinary research at the intersection of computational methods and biological systems. His research expertise spans machine learning applications in bioinformatics, multi-omics data integration, and network biology. Key areas include developing algorithms for disease biomarker discovery, integrating genomic, transcriptomic, and clinical data through graph neural networks, and creating computational tools for precision medicine. Notable contributions include frameworks like CanMod2 for co-regulatory module inference and CTDPathSim for pathway-based similarity analysis. Bozdag’s work bridges computer science and life sciences, addressing challenges in Alzheimer’s disease diagnosis, cancer systems biology, and plant-microbe interactions. He has pioneered methods for analyzing dynamic biological networks and temporal data patterns, with applications in drug response modeling and electronic health record analysis. His lab develops open-source software tools widely used in computational biology research. Current projects emphasize AI-driven approaches for multi-modal healthcare data fusion, predictive modeling of disease progression, and scalable solutions for big biological data. His research has implications for improving diagnostic accuracy, personalized treatment strategies, and understanding complex biological systems.

Mehdi Baratiمشاهده پروفایل

استادیار

• Applied Microeconomics
• Health Economics
• Economics of Crime

Mehdi Barati is an Assistant Professor in the Department of Economics at the University of Southern Mississippi. His academic background includes a Ph.D. in Economics from the University of Wisconsin Milwaukee (2017), an M.S. from University of Tehran (2010), and a B.A. from Shahid Beheshti University (2007). His research focuses on applied microeconomics with specializations in health economics and crime economics. Key areas include efficiency in health production, healthcare consumption patterns, crime causation theories, and societal cost mitigation strategies. He employs advanced econometric modeling, statistical analysis, and machine learning techniques in his empirical investigations. Recent publications demonstrate a consistent focus on healthcare expenditure dynamics and crime policy effectiveness. His scholarly work utilizes sophisticated econometric approaches like autoregressive distributed lag modeling to analyze time-series data on US healthcare costs and crime statistics. Professional memberships include the American Economic Association and Southern Economic Association. He is fluent in Persian (native) and English (professional proficiency).

Chen Zhongمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Explainable AI in Cybersecurity
• +۳ مورد دیگر

Chen Zhong is an Assistant Professor of Cybersecurity in the Information and Technology Management Department at the University of Tampa. She holds a B.S. in Computer Science from Nanjing University (2011) and a Ph.D. in Information Sciences and Technology from Pennsylvania State University (2016), advised by Peng Liu and John Yen. Her research focuses on cybersecurity (intrusion detection, incident response), applied AI in cybersecurity, explainable AI, data analytics (cyber analytics, NLP), and cybersecurity education. She has published over 30 papers in venues like IEEE Systems Journal and IEEE CogSIMA, exploring topics ranging from AI-driven threat detection to gamification in security education. Her recent work highlights trends in integrating generative AI with cybersecurity defense, enhancing human-AI collaboration through explainability, and improving cybersecurity education via inclusive gamification strategies. Notable contributions include frameworks for transparent AI in auditing and models analyzing attacker persuasion techniques in smishing attacks. Awards: 2021 RISE Award, 2019 Teaching Award, multiple scholarships and travel grants Professional Activities: Technical program committee member for IEEE CogSIMA and WiCys conferences Teaching: Courses include Network Security, Cloud Systems Security, and Incident Response Management Affiliations: Center for Cybersecurity at University of Tampa

Glen Rainsمشاهده پروفایل

استاد

• Precision Agriculture
• Sensor Technology
• Insect Ecology
• +۴ مورد دیگر

Glen Rains is a Professor in the Department of Entomology at the University of Georgia's College of Agricultural & Environmental Sciences (CAES). With a PhD in Agricultural Engineering from Virginia Tech (1992), he combines engineering principles with entomological research to develop advanced sensor technologies for agricultural applications. Co-Director of the USDA-funded Agrability in Georgia project Developed handheld chemical detection instruments (U.S. Patent 7,607,338) Licensed technology for biological sensing using invertebrate learning (U.S. Patent 6,919,202) His research focuses on precision agriculture , insect behavior monitoring , and plant pathogen detection . Recent work includes autonomous robotic systems for cotton harvesting, deep learning-based weed detection, and bioengineered plant volatile emissions for pest management. He also contributes to agricultural safety through extension programs targeting farm injury prevention and youth education. Key publication trends show concentration on machine learning applications in cotton phenotyping, autonomous navigation systems for agricultural robotics, and advanced spray technologies for pest control. His 2025 work includes predictive modeling for cotton development and innovative pest monitoring approaches. 2009 - 7,607,338: Handheld Chemical Monitoring Instrument 2005 - 6,919,202: Biological Sensing via Invertebrate Learning As educator, he developed the course CRSS 4010 - Principles of Sustainable Management and contributes to K-12 outreach through programs like G.I.F.T. and Student Shadowing. His laboratory in Tifton, GA, collaborates with engineers and entomologists on cutting-edge agricultural technology solutions.

Reda BELLAFQIRAمشاهده پروفایل

پژوهشگر

• Federated Learning
• Homomorphic Encryption
• Data Security
• +۳ مورد دیگر

Reda BELLAFQIRA is an Enseignant Chercheur (Researcher) at IMT Atlantique's Data Science Department, based in Brest. His work focuses on securing sensitive data processing in distributed systems, combining cryptography with machine learning techniques. Key areas include federated learning, homomorphic encryption, and watermarking for intellectual property protection. He has contributed to privacy-preserving frameworks in healthcare and IoT applications, emphasizing secure collaborative data analysis without compromising confidentiality. Research Interests: Secure federated learning architectures and robustness against adversarial attacks Cryptography applications in AI, including homomorphic encryption for encrypted data processing Watermarking techniques for model integrity and ownership in distributed environments Privacy-preserving methods for healthcare data (e.g., EHRs, genomics) IoT cybersecurity and embedded system security for medical devices Key Contributions: His 2025 work on blockchain-enhanced watermarking for federated learning and 2023 research on automated EHR de-identification exemplify his focus on practical security solutions. Recent trends show increasing emphasis on interdisciplinary approaches merging cryptography with healthcare informatics and IoT systems. Labs/Teams: Active in IMT Atlantique's Data Science Department research groups, collaborating on EU-funded projects related to federated learning in healthcare and secure IoT ecosystems.