جستجوی استاد راهنمای دانشگاهی

• Digital Circuit Design
• VLSI Systems
• Low-Power Electronics
• +۳ مورد دیگر

Dr. Dimitrios Bakalis is an Assistant Professor at the Department of Physics, University of Patras, specializing in Digital Electronic Circuits and Systems. He joined the faculty in 2004 and focuses on the design and control of digital circuits, emphasizing low-power testing and built-in self-test (BIST) methodologies. His academic roles include teaching courses such as Computer Programming I, Digital Electronics, and Microcomputer Architecture at both undergraduate and postgraduate levels. Education: Diploma in Computer Engineering and Informatics (University of Patras) Master’s Degree in Computer Science and Technology (University of Patras) PhD in Computer Engineering and Informatics (University of Patras) Research Interests: His work revolves around VLSI design, arithmetic circuits, and low-power techniques. Key areas include modulo arithmetic circuits, fault-tolerant systems, and reconfigurable computing architectures. He has published over 50 papers in top-tier journals and conferences, contributing to advancements in digital circuit efficiency and reliability. Teaching: He instructs core courses in digital electronics and computer architecture, integrating cutting-edge research into his pedagogy. His MSc courses focus on FPGA-based digital system design, emphasizing practical applications. Labs/Teams: His research aligns with the Electronics & Computers sector within the Department of Physics, collaborating on projects involving arithmetic core optimization and low-power testing strategies.

Grzegorz J. Nalepaمشاهده پروفایل

استاد

• Artificial Intelligence
• Affective Computing
• Context-Aware Systems
• +۹ مورد دیگر

Prof. Grzegorz J. Nalepa holds the position of Full Professor at the Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Poland. He leads the Jagiellonian Human-Centered Artificial Intelligence Lab and is involved in international projects such as the CHISTERA XPM initiative on explainable AI in predictive maintenance. His academic journey includes a PhD (2004), habilitation (2012), and a philosophy MA (2012). Research focuses on AI, affective computing, context-aware systems, and explainable AI (XAI). He has authored over 200 papers, two monographs, and edited volumes. Key projects include the BIRAFFE2 dataset for emotion-based personalization and the GEIST team's semantic wiki tools. Holds leadership roles in conferences (e.g., ECAI 2023) and serves on editorial boards of journals like Sensors and Intelligent Sensors Section . Awards include the 2018 Outstanding Monograph Prize and multiple AGH-UST Rector's Prizes for scientific achievements. Advises PhD and master students, supervising 36 MSc theses. Active in professional organizations like the Polish Alliance for AI and the IEEE Computational Collective Intelligence TC. His contributions span industrial collaborations, software tools (e.g., InXAI, LUX, KnAC), and international research networks.

Xudong Fanمشاهده پروفایل

استادیار

• Urban Water Systems
• Resilience and Sustainability
• Artificial Intelligence
• +۳ مورد دیگر

Xudong (Andrew) Fan is an Assistant Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo (UB), part of the School of Engineering and Applied Sciences. His research focuses on urban water systems, resilience and sustainability in water infrastructure, and applications of artificial intelligence in complex networks. He holds a PhD in Civil and Environmental Engineering from Case Western Reserve University (2022), an MS in Structural Engineering from Tianjin University (2017), and a BS in Civil Engineering from Central South University (2014). Dr. Fan's work integrates advanced computational methods with civil engineering challenges, particularly in enhancing infrastructure resilience through AI-driven approaches. His research spans smart city technologies, climate change adaptation, and bio-based solutions for soil stabilization and frost heave mitigation. Notable contributions include developing graph neural networks for spatially embedded network analysis and machine learning models for predicting water infrastructure failures. His publications reflect a strong emphasis on interdisciplinary problem-solving, combining data science with traditional engineering domains. While no specific awards are listed, his prolific output since 2020 highlights his active engagement in cutting-edge research areas such as AI for water systems and sustainable materials innovation.

Francisco Ruizمشاهده پروفایل

دانشیار

• Fuel Atomization
• Internal Combustion Engines
• Regenerative Thermodynamic Cycles
• +۲ مورد دیگر

Dr. Francisco Ruiz is Associate Professor of Mechanical and Aerospace Engineering at Illinois Institute of Technology's Armour College of Engineering, specializing in thermal sciences and energy systems. Education: Ph.D. in Mechanical Engineering from Carnegie Mellon University (1987) M.S. in Mechanical Engineering from Carnegie Mellon University (1985) B.S. in Aerospace Engineering from Universidad Politécnica de Madrid (1983) His research investigates fundamental mechanisms of fuel atomization, cavitation effects in injection systems, and advanced internal combustion concepts including regenerative cycles and natural gas engines. He pioneers computational modeling approaches for combustion optimization and has developed innovative curriculum frameworks for engineering innovation. Publications demonstrate enduring focus on adaptive thermodynamic cycles, combustion stability, and fluid atomization physics. Recent work examines novel engine architectures and combustion modeling techniques for efficiency improvement. Awards recognize research and educational innovation: Tanasawa Award for atomization research (1988) SAE Teetor Award (1990) Chicago Tribune All-Professor Team (1993) He leads the Invention Center pedagogy initiative transforming engineering education through design thinking methodologies. Professional memberships include SAE, Tau Beta Pi, and Pi Tau Sigma.

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.

Ryan T. Whiteمشاهده پروفایل

دانشیار

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.

• Analogue and Mixed Signal IC Design
• Digital System Design (FPGA)
• CMOS Lab-on-Chip Platforms
• +۴ مورد دیگر

Junming Zeng is a Researcher at the Department of Electrical and Electronic Engineering, Faculty of Engineering at Imperial College London. His work focuses on advanced CMOS technologies for biomedical applications, including lab-on-chip platforms and ion imaging systems. He holds a PhD from Imperial College London (2022), following a Master's in Analogue and Digital Integrated Circuit Design (2017) and a Bachelor's in Electronic Engineering (2016), both from UK institutions. His research interests span analogue/mixed-signal IC design, FPGA-based digital systems, and ultra-high-speed ion sensing solutions. He has pioneered CMOS lab-on-chip platforms for real-time chemical monitoring and developed compressed sensing techniques for optimizing sensor array performance. His work integrates deep learning for applications like diabetes glucose prediction and drift compensation in ISFET sensors. Zeng has received the Best Student Paper Award (1st Prize) at ISCAS 2018 and Imperial's Department PhD Scholarship. His research bridges electrical engineering and biomedical engineering, with a focus on scalable, energy-efficient systems for healthcare and diagnostics. He leads projects involving edge computing, temporal fusion transformers, and microfluidic integration, demonstrating expertise in both hardware innovation and algorithmic development. His lab develops cutting-edge systems such as 1000fps ISFET SoCs with programmable gain and high-throughput digital readout architectures. Recent work includes live demonstrations of real-time pH monitoring in 3D-printed microfluidic systems and spatio-temporal ion membrane characterization platforms.

• 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.

• Power Amplifiers
• Signal Processing
• Radar Systems
• +۷ مورد دیگر

Souheil Ben Smida is an Associate Professor at the School of Engineering & Physical Sciences at Heriot-Watt University, affiliated with the Institute of Sensors, Signals & Systems. His research focuses on power amplifiers, signal processing, radar systems, and biomedical applications, with a particular emphasis on integrating neural networks and wireless communication technologies. His work spans areas such as nonlinear systems, 5G technology, and healthcare innovation, leveraging radar and machine learning for non-invasive monitoring. Recent contributions include dual-band radar systems for vital signs detection, low-complexity neural network-based channel estimation, and predistortion techniques for power amplifiers. Key research trends include biomedical applications using radar and neural networks, optimization of communication systems for 5G, and hardware implementations on FPGA platforms. His publications reflect a balance between theoretical advancements and practical applications in electronics, healthcare, and telecommunications. Dr. Ben Smida's expertise contributes to the UN Sustainable Development Goals, particularly in advancing health technologies and sustainable industrial innovation.

Werner Dietlمشاهده پروفایل

دانشیار

• Programming Languages
• Static Analysis
• Software Security
• +۴ مورد دیگر

Werner Dietl is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His work focuses on programming languages, static analysis, software security, and formal verification techniques. He has contributed to type system design, low-power computing, and approximate data types through projects like EnerJ. His research also addresses challenges in compiler design, cryptographic protocol validation, and runtime enforcement mechanisms. Key research areas include: Type systems for imperative and domain-specific languages Static analysis of implicit control flow (e.g., Java reflection, Android intents) Approximate computing and energy-efficient computation Formal verification of security properties Publications span topics from unit measurement type inference to ownership-based security models, reflecting a focus on practical formal methods. His work emphasizes scalability and precision in type systems while addressing real-world software engineering challenges.

• Software Engineering
• Self-adaptive Software Systems
• Software Reuse
• +۳ مورد دیگر

Dr. Jesper Andersson is a Professor of Computer Science and Dean of the Faculty of Technology at Linnaeus University. He holds a PhD from Linköping University (2007) and has extensive leadership experience, serving as Department Chair from 2013–2020. His research focuses on self-adaptive software systems, software reuse, and cyber-physical systems. He has published widely in top venues like ACM Transactions on Autonomous and Adaptive Systems and Computing , and actively contributes to organizing international conferences. Education: Responsible for advanced courses in software design and development processes. Engaged with industry through technical advising for global companies. Completed major projects include developing a master’s program in computer science and the PROSSES project on self-protecting systems. Current research projects include Digital Twin of Organizations (DTO), DIACCESS for sustainable cities, and Aladino for adaptable architectures. His work emphasizes resilience frameworks, decentralized control, and industrial adaptation practices. Key collaborations include leading the AdaptWise research group and co-chairing SEAMS 2023. His articles span self-adaptive patterns, trust-aware systems, and IoT applications, reflecting a strong focus on both theoretical and applied software engineering challenges.

• Software Engineering
• Probabilistic Programming
• Security and Penetration Testing
• +۵ مورد دیگر

Jürgen Cito is an Associate Professor in the Department of Software Engineering at the Faculty of Informatics, TU Wien, where he leads research in probabilistic programming, security, and configuration management. His work is supported by major grants from the Austrian Science Fund (FWF), European Commission, and Meta Platforms, Inc., with active projects spanning 2022-2027. His research focuses on the intersection of software engineering and machine learning, particularly in static analysis of probabilistic programs, AI-driven penetration testing, and infrastructure security. Key contributions include identifying secret exposure in configuration files, grammar inference for ad hoc parsers, and performance prediction from source code, often combining empirical studies with tool development. Analysis of his 15 most recent publications (2020-2024) reveals three dominant trends: (1) Security vulnerabilities in configuration management systems, especially secret leakage in dotfiles; (2) Application of large language models to offensive security testing; and (3) Machine learning techniques for performance prediction and AutoML optimization in software contexts. Cito has supervised 22 Master's students on cutting-edge topics including AI security, infrastructure as code, and program analysis. His current research portfolio includes: Types4Strings (FWF, 2024-2027): Type systems for string processing Cloud Open Source Research Mobility Network (EU, 2023-2026): Open-source cloud infrastructure Software Assistants for Probabilistic Programming (Meta, 2022-2026): AI tools for probabilistic code He is embedded in TU Wien's Institute of Software Technology and Interactive Systems (E194), collaborating on cross-institutional projects focused on software security and developer tooling, with particular emphasis on empirical validation of security practices and configuration management systems.

• Cyber-Physical Systems
• Multi-Agent Systems
• Autonomous Systems
• +۳ مورد دیگر

Andreas Brandstätter is a PostDoc Researcher in the Cyber-Physical Systems department at TU Wien. His work focuses on multi-agent systems, autonomous robotics, and control theory with applications to drone flocking and autonomous racing. He is affiliated with the Scuderia Segfault team, TU Wien’s autonomous F1TENTH racing group. His research emphasizes distributed control mechanisms and sensor-based coordination in robotic systems. Education: Completed a Diploma Thesis (2019) on local positioning systems for quadcopters at TU Wien. Research Interests: Developing algorithms for multi-agent coordination, predictive control in autonomous vehicles, and leveraging sensor networks for real-time system adaptation. His work intersects robotics, machine learning, and formal methods in control systems. Publications (Selected): 5 peer-reviewed articles in top robotics venues (ICRA, ISoLA) since 2019, focusing on flock formation, predictive control, and autonomous systems. Advising: Supervised a 2024 diploma thesis on coordinated ground-aerial vehicle control. Labs/Teams: Active contributor to Scuderia Segfault, advancing autonomous racing and real-world robotics applications.

• Power Line Communications
• Signal Processing
• Electrical Engineering
• +۳ مورد دیگر

Cornelis Jan Kikkert is an academic affiliated with James Cook University, specializing in areas such as Power Line Communications (PLC), Signal Processing, and Electrical Engineering. His research focuses on improving communication systems within power grids, including impedance measurement techniques, transformer modeling, and smart grid infrastructure. He has contributed to numerous peer-reviewed journals and conferences, including IEEE Smart Grid Communications and Power Line Communications events. His work spans topics like FPGA-based phasor measurement units, inductive shunt analyzers, and broadband PLC applications. Notable contributions include modeling powerline communication channels, developing low-cost coupling networks for smart grids, and advancing adaptive digital predistortion techniques for high-crest-factor signals. His research often bridges theoretical analysis with practical implementations, such as embedded software for real-time impedance analysis and hardware design for satellite beacon receivers. Kikkert has collaborated extensively with industry and academic partners, presenting at global conferences like ISPLC and ICICS. His publications reflect a deep engagement with both foundational and applied aspects of electrical engineering, emphasizing innovation in power systems and communication technologies.

• 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.