جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Business Analytics
• Energy Systems Optimization
• +۷ مورد دیگر

Dr. Nishan Mills is a Lecturer in Business Analytics and AI at La Trobe University, Melbourne, Australia. He holds a PhD from the same institution. His research focuses on advancing AI applications across energy systems, healthcare, education, and smart cities. Key areas include explainable AI, generative models, cybersecurity, and net-zero carbon solutions. He collaborates extensively with industry and academia, co-authoring over 30 publications. Dr. Mills’ work spans technical innovations like emotion analysis frameworks (EMOTION AWARE), energy forecasting systems, and AI chatbots for healthcare and education. His research emphasizes ethical AI deployment, human-centric design, and real-world impact. Notable projects include optimizing energy infrastructure, developing mental health chatbots, and designing AI literacy modules for universities. He has contributed to IEEE journals and conferences, focusing on interdisciplinary applications of AI. His work bridges theoretical advancements with practical implementations in sustainability, urban planning, and digital transformation.

• Artificial Intelligence
• Machine Learning
• Energy Systems Optimization
• +۴ مورد دیگر

Dr. Harsha Kumara Moraliyage is a Lecturer in Business Information Systems at La Trobe University, specializing in AI and machine learning applications. He holds a PhD in Artificial Intelligence and Data Science from La Trobe and has over a decade of software development experience. His research focuses on AI-driven solutions for energy systems optimization, cybersecurity, healthcare informatics, and sustainable development. He leads the award-winning La Trobe Energy Analytics Platform (LEAP), contributing to the university's net-zero emissions target by 2029. Education: PhD in Artificial Intelligence and Data Science (La Trobe University), certifications in Deep Learning, TensorFlow, and AI from Coursera. Research Interests: Adversarial machine learning, natural language processing, time series forecasting, energy analytics, MLOps, AutoML, and AI ethics. Current projects include developing smart energy platforms, cybersecurity frameworks for energy systems, and empathetic AI tools for healthcare. Teaching: Teaches Master of Business Analytics courses such as Data Wrangling (BUS5DWR), Principles of Business Analytics (BUS5PB), and Artificial Intelligence and Hyperautomation (BUS5PR1). Focuses on designing curriculum to enhance AI literacy. Grants & Labs: Leads the LEAP project, a smart energy analytics platform, and collaborates on AI-driven solutions for net-zero carbon emissions. Active in interdisciplinary teams involving energy infrastructure, robotics, and telehealth. Future Work: Expanding research in generative AI for sustainable cities, explainable AI frameworks, and ethical AI integration in education and healthcare sectors.

Seyed Ahmad Soleymaniمشاهده پروفایل

پژوهشگر ارشد

• Cybersecurity
• Edge Computing
• Internet of Things (IoT)
• +۳ مورد دیگر

Dr. Seyed Ahmad Soleymani is a Research Fellow at the University of Surrey's Institute for Communication Systems and part of the 5G/6G Innovation Centre. His research focuses on cybersecurity, edge computing, IoT, UAV systems, and 5G/6G networks. He has contributed to secure authentication protocols for medical and industrial IoT systems, UAV-assisted edge computing optimization, and multi-target tracking algorithms using Q-learning. His work emphasizes energy efficiency, data security, and real-time applications in smart manufacturing and vehicular networks. Research interests include secure communication in UAV networks, sustainable edge node deployment, and privacy-preserving authentication schemes. He has explored applications such as flood forecasting via UAV-assisted sensors and energy-efficient building systems. His publications span IEEE journals and conferences, addressing challenges in edge computing resource allocation, intrusion detection systems, and trust management in 5G-IIoT environments. Collaborations involve institutions like the University of Electronic Science and Technology of China and Universiti Teknologi Malaysia. Notable contributions include the MI3SE encryption scheme for outdoor IIoT devices and the TRUTH trust scheme for 5G industrial IoT. His work combines machine learning (e.g., SAC, Q-learning) with network optimization to address latency, security, and scalability in modern communication systems. He has also developed frameworks for cybertwin-based 6G networking and secured target tracking in O-RAN environments.

• Cyber-Physical Systems
• Control Theory
• Secure-by-Construction Synthesis
• +۳ مورد دیگر

Majid Zamani is an Associate Professor in the Computer Science Department at the University of Colorado Boulder, and a Guest Professor at Ludwig Maximilian University of Munich. He previously held an Assistant Professor position at the Technical University of Munich (2014–2019), leading the Hybrid Control Systems Group. His expertise spans verification and control of cyber-physical systems, secure-by-construction synthesis, and compositional analysis of interconnected systems. He holds a PhD in Electrical Engineering from UCLA (2012) and additional degrees from Sharif University of Technology and Isfahan University of Technology. Research Focus: Verification of safety properties, secure control synthesis, data-driven methods, and formal methods for stochastic systems. Notable contributions include barrier certificates, compositional verification, and abstraction-based control strategies. His work addresses challenges in autonomous systems, robotics, and safety-critical infrastructure. Major Awards: Recipient of the 2023 George S. Axelby Outstanding Paper Award, 2022 NSF Career Award, and ERC Starting/Proof-of-Concept Grants (2018/2023). Active in technical leadership roles, including serving on the IEEE CSS Technical Committee. Grants & Collaborations: Focused on NSF-funded projects exploring data-driven control and European initiatives in compositional system verification. His research bridges theoretical foundations with practical applications in robotics and autonomous systems.

Jordan Viceمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۷ مورد دیگر

Jordan Vice is a Research Fellow at The University of Western Australia's School of Physics, Maths and Computing, specializing in Computer Science and Software Engineering. They hold a Ph.D. in Mechatronic Engineering from Curtin University (2023) and a 1st Class Honours degree in the same field (2020). Their research focuses on AI transparency, fairness, security, and reliability, with expertise in generative models, explainable AI, and robotics. Education: Ph.D. in Mechatronic Engineering (2020-2023), Curtin University, Thesis: Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module Bachelor of Mechatronic Engineering (2015-2019), Curtin University, Thesis: Bi-modal Affect-Based Authentication Machines Research Interests: AI ethics, generative models, cybersecurity in AI systems, facial expression analysis, and AI applications in healthcare. They advocate for transparent and accountable AI deployment while addressing security and privacy challenges. Recent work explores bias quantification in text-to-image models and backdoor attacks in generative systems. Key Trends in Publications: Focus on adversarial machine learning, generative model vulnerabilities, and ethical AI frameworks. Notable contributions include frameworks for real-time affect assessment and methodologies to quantify bias in text-to-image systems. Awards: Chancellor's Commendation (2023) Proxima Consulting Prize (2020) RTP Scholarship (2019) Advising & Grants: No explicitly listed advisees or grants, though their work suggests involvement in collaborative research projects. Labs/Teams: Affiliated with the School of Computer Science and Software Engineering at UWA, contributing to interdisciplinary AI research groups.

Michelle S. Chongمشاهده پروفایل

استادیار

• Control Systems Theory
• Cyber-Physical Systems
• Secure Estimation
• +۲ مورد دیگر

Michelle S. Chong is Assistant Professor in Mechanical Engineering at Eindhoven University of Technology, specializing in control systems theory with applications to cyber-physical systems security. Her research develops theoretical frameworks for estimation and control of hybrid/nonlinear systems, particularly addressing cybersecurity challenges in networked systems. Current projects focus on resilient state estimation under adversarial attacks, safe control synthesis, and data-driven methods for power systems. Chong collaborates internationally on cyber-physical-human systems security and teaches graduate courses on fault detection and secure control systems. She obtained her PhD in Control Theory and maintains active research collaborations across Europe.

• Artificial Intelligence
• Knowledge Representation
• Ontology Engineering
• +۴ مورد دیگر

Romana Pernisch is a Researcher at the Department of Artificial Intelligence, Faculty of Science, Vrije Universiteit Amsterdam. Her research focuses on ontology engineering, knowledge graphs, and their applications in robotics and sustainability. She contributes to projects like the ORKA ontology for robotics and the CoPla ontology for companion planting. Research Interests: Ontology Development and Evolution Knowledge Graph Construction and Lifecycle Management AI Integration in Robotics and Agriculture Semantic Technologies and Data Visualization Her work emphasizes methodologies for aligning ontologies with knowledge graphs, improving robotic perception through semantic integration, and fostering sustainable practices via computational tools. Recent collaborations include studies on ontology change impact analysis and knowledge graph applications in dark web classification. Labs & Contributions: Contributed to the Dynamic Knowledge Graphs for Continual Learning of Embeddings dataset (2023). Co-developed the ChImp tool for ontology change visualization. Teaching assistant for Knowledge Graphs and Semantic Technologies (2024–2025).

• Semantic Web
• Linked Data
• Ontology Engineering
• +۷ مورد دیگر

Dr. Stefan Schlobach is an Associate Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Artificial Intelligence department, Network Institute, and Knowledge Representation and Reasoning group. His research focuses on Semantic Web, Linked Data, Ontology Engineering, and Hybrid Intelligence systems. He has published over 116 peer-reviewed works and supervised 6 PhD theses. Recent projects include robotic knowledge acquisition (ORKA ontology), explainable AI for security systems, and knowledge engineering for virtual heritage applications. He co-developed the LOTUS search framework for big linked data and contributed to the CoPla ontology for agriculture. His work bridges theoretical foundations (e.g., argumentation systems) with applied domains (e.g., cyber-physical systems, cultural heritage digitization). Research Interests: Semantic Web Technologies Knowledge Graphs & Ontology Engineering Hybrid Intelligence (Human-AI Collaboration) Robotic Perception Systems Explainable AI Entity Linking & Identity Resolution Key Contributions: Developed LOTUS adaptive search system (2016 prize-winning work) Created ORKA ontology for robotics knowledge acquisition Pioneered perceived-entity linking methodologies Advocated knowledge engineering principles for hybrid systems Teaching: Leads courses in Artificial Intelligence, Knowledge Representation, and Semantic Technologies at both undergraduate and graduate levels.

• Safety Engineering
• Systems Theory
• Industrial Engineering
• +۷ مورد دیگر

Prof. Gravio Giulio is a Full Professor at the Sapienza University of Rome within the Department of Industrial and Management Engineering . His work focuses on systemic safety, cyber-physical systems, and resilience engineering. He specializes in risk assessment methodologies like STAMP/STPA and FRAM, with applications in aviation, industrial automation, and healthcare. Research interests include Functional Resonance Analysis (FRAM) Cyber-socio-technical systems Emergency management systems Human reliability analysis Safety-critical systems design His recent work explores STAMP-based simulations for eVTOL safety, knowledge graphs in industrial near-miss reporting, and cyber resilience in critical infrastructure. He has pioneered tools like myFRAM for functional resonance analysis. Key projects include: - Systemic risk management for Advanced Air Mobility - Resilience engineering in anesthesia practices - Safety analysis of medical gas pipelines - STPA-Sec for security-critical systems Prof. Giulio collaborates with ANSPs (Air Navigation Service Providers) and industry partners, focusing on bridging theoretical frameworks with practical safety implementations.

Ho Kok Hoeمشاهده پروفایل

مدرس ارشد

• Manufacturing
• Industrial Engineering
• Operations Research
• +۶ مورد دیگر

Ho Kok Hoe is a Senior Lecturer (Practice) at the Malaysia School of Engineering, Monash University Malaysia, specializing in the Electrical and Robotics Engineering department. He holds a PhD in Engineering from Universiti Tunku Abdul Rahman (UTAR) and has extensive industrial and academic experience. His research focuses on manufacturing optimization, Industry 4.0, automation, and improving teaching pedagogy. Education: PhD in Engineering (Industrial Engineering, Operations Research and Manufacturing), UTAR (2014–2021) MSc in Engineering (Industrial Engineering, Operations Research and Manufacturing), Universiti Terbuka Malaysia (2008–2012) BEng (Hons) in Electrical and Mechatronic Engineering, Universiti Tun Hussein Onn Malaysia (1998–2002) Research Interests: Industrial collaboration, manufacturing processes, automation and robotics, Industry 4.0, modeling and simulation, statistics, and curriculum improvement. He has contributed to projects like the Integrated Industry 4.0 Solution for PCB Assembly. Awards: First Place in Doctoral Dissertation Competition (IEOM, 2022) Outstanding Performance Employee Award (Carsem, 2017) Scholarship for PhD Study (Mybrain15-MyPhd, 2014) Grants and Projects: Leading the 2024–2025 project on Industry 4.0 solutions for PCB assembly. Collaborates with industry partners for applied research. Professional Roles: Section Editor for Chip Design and Manufacturing journal, volunteer with Teach for Malaysia, and member of professional societies like IEM and AFEO.

Jiefei Weiمشاهده پروفایل

پژوهشگر ارشد

• Safe AI
• Explainable AI
• Computer Vision
• +۳ مورد دیگر

Jiefei Wei is a Postdoc Research Fellow in the Department of Statistics at the University of Warwick. They collaborate with Professor Giovanni Montana on a CRUK-funded project developing AI and computer vision techniques for breast cancer diagnosis and risk prediction. Their research focuses on Safe AI, Explainable AI, Computer Vision, Medical Imaging, and Machine Learning applications in healthcare. Research interests include adversarial machine learning robustness, medical image analysis, and AI-driven diagnostic systems. Their publications span topics like emotion recognition in video ads, adversarial attack defense, and autonomous vehicle decision-making. No scientific awards or advising roles are listed. Their work integrates statistical methodologies with cutting-edge AI, addressing both technical security challenges and clinical healthcare applications.

Olga Taranمشاهده پروفایل

مدرس

• Machine Learning
• Deep Learning
• Biomedical Data Science
• +۳ مورد دیگر

Dr. Olga Taran is a Lecturer at the Department of Health Sciences and Technology at ETH Zurich, specializing in machine learning and biomedical data science. She holds a PhD in Computer Science from the University of Geneva, with a focus on machine learning for complex problems. Her postdoctoral research at the Biomedical Data Science (BMDS) Lab involves developing data-driven metrics to improve outcomes in Spinal Cord Injury (SCI) clinical trials, addressing challenges in treatment evaluation. Her work aims to enhance clinical decision-making and accelerate therapy development. Olga's research also extends to anti-counterfeiting technologies using machine learning, focusing on copy detection patterns and authentication systems. Her research interests span machine learning applications in healthcare, deep learning methodologies, and the integration of digital twins for fraud detection. She has contributed to advancements in radio astronomical image reconstruction and self-supervised learning techniques. Olga's publications reflect a strong emphasis on interdisciplinary solutions, combining machine learning with fields like astronomy, cybersecurity, and biomedical science. Notable achievements include pioneering work on authentication systems using digital blueprints and physical fingerprints, as well as stochastic digital twin models for copy detection patterns. She actively collaborates on projects addressing the limitations of traditional clinical trial metrics and exploring machine learning's role in combating adversarial attacks on authentication systems.

Jonathan Woodruffمشاهده پروفایل

پژوهشگر ارشد

• Computer Architecture
• Security
• Programming Languages
• +۳ مورد دیگر

Dr. Jonathan Woodruff is a Senior Research Associate at the Department of Computer Science and Technology, University of Cambridge. His research focuses on secure computing architectures, with a particular emphasis on the CHERI (Capability Hardware Enhanced RISC Instructions) project. His work spans computer architecture, security, programming languages, and hardware-software co-design. Research Interests: Hardware-enhanced memory safety Secure processor architectures (RISC-V/CHERI) Formal verification of microarchitectures Compiler/runtime systems for secure execution Transient-execution attack mitigation Global address space security Recent work emphasizes validation of the Morello platform (ARM's CHERI implementation) and exploration of architectural contracts for safe speculation. His contributions include novel memory protection mechanisms and performance evaluation of secure architectures.

Mohammad Hasanمشاهده پروفایل

مدرس ارشد

• Computer Networks
• Cyber Security
• Cloud Computing
• +۵ مورد دیگر

Dr. Mohammad Hasan is a Senior Lecturer in Digital, Tech, Innovation & Business at Staffordshire University with extensive expertise in cybersecurity, cloud computing, and network engineering. His educational background includes a PhD in Mobile Ad-hoc Networks, MSc in Computer and Network Engineering, and BSc in Computer Science. His primary research focuses on: Computer networks including wireless systems and IoT Cybersecurity frameworks and threat analysis Cloud computing infrastructure and virtualization Machine learning applications in data analytics Recent publications demonstrate strong trends in machine learning applications for pattern recognition (eSports analytics), network security (XSS prevention), and biomedical signal processing (brain source localization). Dr. Hasan maintains professional certifications including CCSK, CompTIA Security+, and Cisco CyberOps Associate, and holds Senior Fellow status with the Higher Education Academy.

Siddhartha Dattaمشاهده پروفایل

پژوهشگر ارشد

• Distributed Machine Learning
• Model Adaptation
• Cybersecurity
• +۴ مورد دیگر

Siddhartha Datta is a Researcher at the University of Oxford's Department of Computer Science, specializing in distributed machine learning and adaptive systems. His research develops end-to-end solutions for model training, security, and deployment. Key research areas include: Adaptive learning algorithms for dynamic environments Defense mechanisms against adversarial attacks Human-centered AI interfaces and tools Cross-reality systems blending digital/physical spaces Efficient model compression techniques Current projects investigate prompt engineering for generative models, online learning under data drift, and 3D object detection refinement. His work combines theoretical frameworks with practical implementations, resulting in tools like GreaseTerminator for digital sovereignty and DeepObfusCode for source protection. Datta has collaborated with Google and Cornell University during his doctoral studies. His open-source contributions include neurocogpy (ECoG processing) and Polysemy Word Tagging Tool, demonstrating commitment to accessible research tools.