جست‌وجوی استاد و پژوهشگر

• Computational Neuroscience
• Machine Learning
• Neural Dynamics
• +۵ مورد دیگر

Dr. Emili Balaguer-Ballester is an Associate Professor in Computational Neuroscience at Bournemouth University, UK. He co-champions the Interdisciplinary Neuroscience Research Centre and serves as Senior Fellow of the Higher Education Academy. His research spans computational neuroscience, machine learning, and virtual reality applications, with collaborations across Europe (University of Barcelona, Heidelberg University, IDIBAPS, Polyra) and North America (Indiana Purdue University, University of British Columbia). PhD in Physics (Cum Laude) from University of Valencia (2001) MSc in Neuroscience and Biology of Behaviour from University of Seville (2004) Research focuses on cortical network dynamics, mesoscopic auditory cortex modeling, top-down modulation in cognition, and affective computing in VR. His work combines nonlinear time series analysis with neurodynamic modeling to study spontaneous and task-related brain activity states. Recent publications demonstrate expertise in causal inference, neuroevolutionary optimization, and affective state detection using EMG/PPG sensors in VR environments. Collaborators include neuroscience labs (Sanchez-Vives, Durstewitz) and tech companies (Sony London, Emteq). PhD thesis award - Culture Institute 'Juan Gil-Albert', Spain (2001) Senior Fellow Higher Education Academy (2020) Secured major grants including Royal Society funding for cortical network modeling (2022), Human Brain Project ERC support for neuromorphic hardware (2022), and Santander Bank research travel grants (2016). Supervised 14 PhD students through projects involving data streams, VR immersion, and cortical dynamics. Labs include Balaguer Lab (GitHub), Interdisciplinary Neuroscience Research Centre, and collaborations with Human Brain Project. Teaching encompasses doctoral-level neuroscience, MSc data analytics, and undergraduate systems design courses.

Stephen Pistoriusمشاهده پروفایل

استاد

• Medical Physics
• Medical Imaging
• Image Reconstruction
• +۴ مورد دیگر

Stephen Pistorius is a Professor in the Department of Physics and Astronomy at the University of Manitoba's Faculty of Science. He serves as Associate Head and Director of the Medical Physics Program, focusing on interdisciplinary research at the intersection of medical physics, biomedical engineering, and artificial intelligence. Role : Professor, Associate Head, Director of Medical Physics Program Contact : Office 205 Allen Building, Stephen.Pistorius@umanitoba.ca , +1 204-474-6205 Research Interests Medical Imaging : Development and optimization of radar-based microwave sensing systems for breast cancer detection. Image Reconstruction : Innovation in computational algorithms for PET and microwave imaging. Artificial Intelligence : Application of machine learning to tumor detection and signal analysis. Publication Trends His recent work emphasizes microwave imaging hardware (antenna arrays, phantom materials), machine learning integration for tumor detection, and image quality metrics across modalities. Key subfields include radar systems , stochastic optimization , and clinical translation of microwave sensing . Additional Contributions He contributes to radiation oncology via EPID-based positioning automation and dose verification systems, while also addressing technical challenges in 3D-printed phantom modeling and microwave propagation in biological tissues.

Fuyuan Zhangمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Cybersecurity
• Formal Methods
• +۷ مورد دیگر

Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.

• Computer-Supported Collaborative Learning (CSCL)
• Artificial Intelligence in Education
• Human-Computer Interaction
• +۳ مورد دیگر

Nikol Rummel is a Professor at Ruhr-Universität Bochum , specializing in Computer-Supported Collaborative Learning (CSCL) , Artificial Intelligence in Education , and Human-Computer Interaction . Her work bridges educational technology with hardware reverse engineering , focusing on AI-orchestrated learning environments and adaptive tools for collaboration. Her research spans AI-orchestration tools ( Pair-Up , Co-Designing Orchestration Support ), collaborative problem-solving ( Learning Alone or Together ), and hardware reverse engineering education ( Promoting Skill Acquisition ). She investigates how multimodal data and adaptive feedback enhance learning outcomes, teacher practices, and emotional regulation. Her publications highlight trends in generative AI for academic writing , collaboration scripts , and cognitive obfuscation in cybersecurity education. Collaborations with researchers like Vincent Aleven, Christof Paar, and Steffen Becker underscore her interdisciplinary approach. Nikol Rummel’s work emphasizes teacher dashboards ( What Information Should CSCL Dashboards Provide? ), dynamic student pairing ( Combining Dialog Acts and Skill Modeling ), and emotional engagement in learning ( How to Enjoy Writing Papers ).

Yang Liمشاهده پروفایل

استادیار

• Computer Architecture
• Machine Learning
• On-Device AI
• +۴ مورد دیگر

Yang Li is an Assistant Professor of Computer Science at Iowa State University, specializing in computer architecture, machine learning, and their intersection. He holds a Ph.D. and M.S. from Carnegie Mellon University (2020), an M.S.E. from the University of Texas at Austin (2013), and a B.E. from Tsinghua University (2011). Prior to academia, he worked as a Senior Research Scientist at Meta, a Research Scientist at Meta, and a Researcher at Microsoft. His research focuses on large language models (LLM) acceleration, on-device AI, cloud infrastructure optimization, and spatiotemporal forecasting. He has contributed to over 20 peer-reviewed publications at top venues like ASPLOS, EMNLP, and ICASSP. Research Interests: Algorithmic and systems-level acceleration of LLMs On-device AI co-design and privacy Cloud memory/power management Graph-based spatiotemporal forecasting Teaching: Taught COMS 6730 (Advanced Topics in ML), COM S 321 (Computer Architecture), and guest-lectured on graph signal processing. Recent teaching scores include 4.75/5.0 (Fall 2024) and 4.67/5.0 (Spring 2025). Awards: IBM Patent Application Award (2021) and multiple patents on power management systems for data centers. Service: Program committee member for DAC 2024, NeurIPS 2024, and ICLR 2025. Reviewer for ACM TACO, IEEE TPAMI, TPDS, and others.

• Smart Grids
• Power System Cybersecurity
• Cyber-Physical Systems
• +۷ مورد دیگر

Peter Palensky is a leading researcher in smart grids, power system cybersecurity, and cyber-physical systems. His recent work focuses on digital twins for power systems, quantum computing applications in energy analysis, and secure blockchain frameworks for distributed energy resources. He collaborates extensively with institutions across Europe, particularly in Dutch and Mongolian grid stability projects. Research areas include Smart grid resilience against cyber attacks Quantum-enhanced power flow analysis Electric vehicle grid integration (V2G) Machine learning for energy systems optimization High-voltage direct current (HVDC) security His publications emphasize practical implementations, such as hardware-in-the-loop testing for photovoltaic systems and real-time simulation models for energy storage. Recent articles explore large-scale synthetic data generation for grid analysis, dynamic tariff impacts on EV charging, and advanced control strategies for offshore MMC grids.

• Quantum Computing
• Quantum Machine Learning
• Quantum Simulations
• +۳ مورد دیگر

Marco Russo is a PhD Student in Computer and Systems Engineering (38th cycle, 2022-2025) at the Department of Automatic Control and Computer Science (DAUIN) of the Polytechnic University of Turin. He serves as an external teacher/teaching assistant in DAUIN and holds a Contract Professor position at the Center for Autonomous Management of the Interfaculty University School of Strategic Sciences (SUISS) from November 2023 to October 2024. His research focuses on Quantum Computing, Quantum Machine Learning, and Quantum Simulations, with ERC sectors emphasizing machine learning and quantum computing formal methods. He teaches Computer Architecture courses for Computer Engineering Master's students. Russo's recent publications explore cutting-edge applications in quantum control, embedded systems integration with quantum algorithms, quantum security protocols, and neural-symbolic AI for puzzles. His work bridges theoretical advancements in quantum computing with practical implementations across gaming, communications, and aerospace domains. His academic roles include collaboration with PhD guardians Bartholomew Montrucchio and Olivier the Third. While no formal awards are listed, his contributions span interdisciplinary research areas at the intersection of quantum technologies and classical engineering systems.

Elmar Rueckertمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Robotics
• Machine Learning
• +۹ مورد دیگر

Prof. Elmar Rueckert is the Chair of the Cyber-Physical-Systems Institute at Montanuniversität Leoben in Austria since March 2021. He holds a PhD in Computer Science from TU Graz (2014) and previously served as a Senior Researcher at TU Darmstadt (2014–2018) and Assistant Professor at the University of Lübeck (2018–2021). His research focuses on Cyber-Physical Systems, Robotics, Machine Learning, and Human Motor Control, with applications in industrial automation, healthcare robotics, and environmental sustainability. Key research interests include stochastic machine/deep learning, reinforcement learning, brain-computer interfaces, and tactile learning. His work bridges robotics, neuroscience, and AI, emphasizing practical applications such as autonomous navigation, exoskeleton control, and sensor-based recycling technologies. Rueckert has led projects like the AI Robot Lab funded by Robert Bosch Stiftung and the KIRAMET recycling initiative. Recent publications highlight advancements in neural networks for industrial condition monitoring, multimodal robotic learning, and environmental infrastructure modeling. Awards include the German Young Researcher Award (2019) and the Advanced Robotics Best Paper Award (2018). Rueckert advises a team of researchers, including notable students like Daniel Tanneberg (PhD graduate, 2019) and Linus Nwankwo (best student paper winner, 2023). His lab develops open-source tools like ROMR and collaborates on datasets like EnvoDat for robotic spatial reasoning.

• Smart Textiles
• RFID Technology
• Wearable Devices
• +۵ مورد دیگر

Johanna Virkki is an Associate Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University. Her research focuses on smart textiles, wearable technologies, RFID systems, and their applications in healthcare, assistive communication, and human-computer interaction. She has pioneered projects integrating RFID into clothing and environments for control interfaces, physiological monitoring, and gamified therapies. Key research areas include: Development of e-textiles for AAC (Augmentative and Alternative Communication) RFID-based wearable and furniture interfaces for accessibility Sensor systems for healthcare monitoring and rehabilitation Smart spaces and IoT-enabled environments Her work emphasizes interdisciplinary collaboration, with notable contributions in: Passive RFID sensor platforms for moisture, strain, and physiological signals Co-design approaches involving speech therapists, engineers, and end-users Prototyping wearable devices for children with disabilities and elderly care Recent publications highlight advancements in: Smart furniture and textile-based AAC solutions AI-driven smart spaces and edge-cloud computing Bio-inspired sensors using animal hair and conductive yarns Johanna's research bridges technology and human needs, addressing challenges in healthcare, education, and accessibility through innovative textile and RFID systems.

Matteo Biagiolaمشاهده پروفایل

پژوهشگر

• Software Testing
• Test Generation
• AI for Software Testing (AI4SE)
• +۴ مورد دیگر

Matteo Biagiola is a Researcher in the Faculty of Informatics at Università della Svizzera italiana (USI), Lugano, Switzerland, and a PostDoctoral researcher at the University of St. Gallen (HSG). He specializes in software testing, particularly test generation for Web applications, deep reinforcement learning systems, and autonomous driving software. His work focuses on enhancing AI robustness through testing and improving software testing via AI techniques. Biagiola holds a Ph.D. from Università degli Studi di Genova (Italy) in collaboration with Fondazione Bruno Kessler, Trento. He has conducted postdoctoral research at USI on the Precrime ERC Advanced Grant project under Paolo Tonella. His research tools include μPRL (mutation testing for RL agents), STILE (web test parallelization), and GenBo (boundary state generation for autonomous systems). Education: Ph.D.: Università degli Studi di Genova / Fondazione Bruno Kessler (2016–2020) M.Sc.: Università Politecnica delle Marche (2014–2016) Affiliations: PostDoc & Scientific Collaborator: University of St. Gallen / USI (2025–present) PostDoc: USI (2020–2025) Visiting Ph.D. Student: University of British Columbia (2018) His research interests span AI-driven testing tools, autonomous system validation, and simulation-based testing. He has received the Distinguished Paper Award at ICST 2025 and serves on program committees for major conferences like FSE, ICSE, and ICST. He co-organizes workshops like DeepTest (co-located with ICSE) and the Cyber-Physical Systems tool competition (SBFT).

Simon Birnbachمشاهده پروفایل

پژوهشگر ارشد

• Cyber-Physical Systems Security
• Aviation Security
• Aerospace Security
• +۴ مورد دیگر

Simon Birnbach is a Junior Research Fellow and Senior Postdoctoral Researcher in the Department of Computer Science at the University of Oxford, affiliated with the Systems Security Lab led by Professor Ivan Martinovic. His research focuses on securing cyber-physical systems, particularly in smart homes, aviation, and aerospace domains. He holds a DPhil (Oxford), MSc (Kaiserslautern, Germany), and BSc (Kaiserslautern, Germany). His work addresses critical security challenges such as satellite fingerprinting resilience, drone privacy invasion detection, and verifying smart home events via collaborative sensors. Notable contributions include a transferable fingerprinting scheme for aviation legacy systems and co-supervising six students across satellite, IoT, behavioral biometrics, and malware detection topics. Recent publications emphasize adversarial attacks on satellite systems, TTP-based malware detection, and securing space communication protocols. His research bridges theoretical advancements with real-world applications in aerospace, automotive, and smart environment security.

Federico Tartariniمشاهده پروفایل

مدرس ارشد

• Environmental Engineering
• Building Science
• Thermal Comfort
• +۵ مورد دیگر

Dr Federico Tartarini is a Senior Lecturer at the Sydney School of Architecture, Design and Planning and a Sydney Horizon Fellow at the University of Sydney. His research focuses on preventing heat stress through improved indoor environmental design, with expertise in thermal comfort, building performance, and health. He leads innovative projects at the intersection of environmental engineering and human well-being. Member of Sydney Environment Institute Member of Charles Perkins Centre Collaborator at UC Berkeley's Center for the Built Environment Research Themes: Combines laboratory and field studies with software/hardware development to address thermal health challenges. Specializes in occupant-centric building design, thermophysiological modeling, and real-time monitoring technologies. Scientific Awards 2023 Future Leader Award – AIRAH 2023 Excellence in HVAC&R Research – AIRAH 2023 Horizon Fellowship from University of Sydney Multiple awards from 2014-2021 including best paper and researcher accolades Tools & Impact: Creator of widely used open-source platforms like pythermalcomfort, CBE Thermal Comfort Tool, and Cozie smartwatch app. Founded EMU Systems for IEQ monitoring solutions. Key work includes modifying Sports Medicine Australia's heat stress policies and ASHRAE standard contributions.

Jianping Luمشاهده پروفایل

استاد

• Medical Imaging
• Radiology
• Biomedical Engineering
• +۳ مورد دیگر

Professor Jianping Lu is a leading academic at the University of North Carolina at Chapel Hill, affiliated with the Department of Physics and Astronomy within the College of Arts and Sciences. His work focuses on advancing medical imaging technologies, particularly in X-ray and computed tomography (CT) systems, with a strong emphasis on carbon nanotube (CNT) X-ray sources. He holds a Ph.D. in Physics from the City University of New York (1988). Education: Ph.D. in Physics, City University of New York, 1988 Research Interests: Development of novel imaging systems, including stationary tomosynthesis and multisource CBCT Optimization of X-ray technology for clinical applications (e.g., oncology, cardiology, dentistry) Integration of artificial intelligence (AI) for diagnostic accuracy and automated analysis Portable and low-cost medical imaging solutions Recent Work Trends: His 2025 publications highlight advancements in AI-driven diagnostics for pancreatic cancer, improved contrast in adaptive radiation therapy, and stationary chest tomosynthesis systems. Key innovations include low-cost dual-energy CBCT and carbon nanotube-based X-ray arrays, which enhance image quality while reducing radiation exposure. His 2024 studies further explore cardiac imaging, dental tomosynthesis, and system optimizations for clinical adoption. Awards: None explicitly listed in the provided text. Advising & Grants: While student advisees are not listed, his research is likely supported by grants focusing on medical imaging innovation. Collaborations span physics, engineering, and clinical departments to bridge technical and clinical challenges. Labs/Teams: Likely affiliated with UNC’s imaging research groups, particularly those developing CNT X-ray technologies and clinical imaging systems for cancer and cardiovascular applications.

Scott Millerمشاهده پروفایل

مدرس

• Physical Layer Security
• Visible Light Communication (VLC)
• Acoustic Telemetry Systems
• +۵ مورد دیگر

Scott Miller is a Lecturer in the Department of Industrial & Systems Engineering at Texas A&M University. His research focuses on physical layer security, wireless communication systems, and signal processing in harsh environments. He specializes in visible light communication (VLC), acoustic telemetry, and hybrid FSO-mmWave systems. Key contributions include secure IM-OFDMA system design, IQ imbalance analysis, and medium-specific communication protocols for downhole monitoring. Research interests span secure communication protocols, network coding, cognitive radio networks, and system reliability in fading channels. He has pioneered work on VLC-based downhole gas pipeline monitoring using hydrogen/nitrogen mediums and developed learning-based link selection approaches for hybrid wireless systems. His work emphasizes practical applications in industrial telemetry and secure wireless transmission. Notable trends in his publications include: Advancements in physical layer security mechanisms for uplink systems Innovative use of non-traditional mediums (e.g., CO₂, hydrogen) for VLC Integration of machine learning for signal detection and link optimization Robustness analysis of hybrid FSO-mmWave networks under various impairments His research bridges theoretical communication frameworks with real-world industrial applications, addressing challenges in energy, oil/gas sectors, and secure IoT deployments.

Wang Shirenمشاهده پروفایل

استاد

• Additive Manufacturing
• Bio/nano-manufacturing
• Brain-inspired Artificial Intelligence
• +۶ مورد دیگر

Professor Wang Shiren is a faculty member in the Department of Industrial & Systems Engineering at Texas A&M University, holding the Jill & Charles F. Milstead '60 Faculty Fellowship. He is affiliated with Biomedical Engineering and Materials Science & Engineering. His research focuses on additive manufacturing, bio/nano-manufacturing, brain-inspired AI, and decarbonization. He leads the Manufacturing Intelligence and Nanotechnology Innovation (MINI) Lab, emphasizing sustainable manufacturing and commercialization. Education: Ph.D. in Industrial & Manufacturing Engineering from Florida State University (2006) Research interests span advanced materials, energy-efficient manufacturing, and AI integration. Recent work includes low-carbon ceramics, thermoelectric materials, and nanozyme-based therapies. Over 100 peer-reviewed publications demonstrate expertise in additive manufacturing, nanocomposites, and energy harvesting. Awards include NSF CAREER Award (2010-15) and multiple faculty fellowships. Awards: NSF CAREER Award, AFOSR Fellowship, Texas A&M TEES Fellowship, and 3M Non-tenured Award. Grants and collaborations focus on AI-driven manufacturing, eco-friendly processes, and medical device innovation. The MINI Lab develops smart materials and sustainable solutions for industrial and biomedical applications. Labs/Teams: MINI Lab at Texas A&M, specializing in nanotechnology and manufacturing intelligence.