جست‌وجوی استاد و پژوهشگر

• Robotics
• Sensors Technology
• Biomedical Engineering
• +۵ مورد دیگر

Oliver Ozioko is a Lecturer in Electrical and Electronic Engineering at the College of Science and Engineering. His research focuses on advanced sensor technologies, wearable systems, and robotic applications. He specializes in developing soft, flexible sensors for robotics, prosthetics, and biomedical devices. Key areas of interest include tactile sensing, electronic skin (e-skin), additive manufacturing for sensors, and assistive technologies for the deafblind community. His work integrates interdisciplinary approaches combining material science, nanotechnology, and robotics. Notable contributions include capacitive sensors using recycled carbon fibers, 3D-printed robotic end-effectors, and multifunctional e-skin systems with temperature and pressure sensing capabilities. He has also pioneered tactile communication interfaces and haptic feedback devices for assistive applications. Publications span journals like IEEE Sensors Letters and Sensors, with conference contributions at IEEE International Conferences on Flexible Sensors and Systems. His research emphasizes sustainability (e.g., recycled materials) and real-world applications in healthcare and industrial automation. Awards and grants information is not explicitly detailed in the provided texts, though his prolific publication record indicates sustained research activity. He collaborates extensively with industry partners and academic institutions globally, though specific lab affiliations are not mentioned.

Amlan Chatterjeeمشاهده پروفایل

دانشیار

• High-Performance Computing
• Big Data
• Graph Compression
• +۵ مورد دیگر

Dr. Amlan Chatterjee is an Associate Professor in the Department of Computer Science at California State University, Dominguez Hills. His work focuses on high-performance computing, big data analytics, and GPU-based graph compression techniques. He has held academic roles since 2015, including Assistant Professor (2015-2021) and currently serves as Associate Professor. His research explores efficient computation on large datasets using multi-core architectures and GPUs, alongside cloud computing optimization and IoT applications in aviation and health monitoring. Education: Ph.D., Computer Science, University of Oklahoma, 2014 M.S., Computer Science, State University of New York, 2009 B.Tech., Computer Science & Engineering, West Bengal University of Technology, 2007 Research Interests: Dr. Chatterjee's research spans graph compression, social network analysis, cloud resource optimization, and IoT-driven solutions for aviation safety and health monitoring. He has mentored students in projects like GPU-based big data processing and cloud computing efficiency. Awards: Graduate Computer Science Scholarship (University of Oklahoma, 2012-13) Computer Science Advisory Board Scholarship (2012) Phillips Petroleum Scholarship (2010-11) Top Undergraduate Rank (1st/68 students) Academic Contributions: He has served on numerous committees, including the Research Chair for Untenured Faculty (2016-17), IEEE conference session chairs, and accreditation boards. His teaching includes courses on data structures, operating systems, and introductory computer science.

Faegheh Moazeniمشاهده پروفایل

استادیار

• Mathematical Optimization
• Design Optimization of Critical Infrastructure
• Cybersecurity of Smart Systems
• +۵ مورد دیگر

Faegheh Moazeni is an Assistant Professor in the Department of Civil & Environmental Engineering at Lehigh University. Her research focuses on mathematical optimization, cybersecurity of critical infrastructure systems, and smart city technologies. She leads projects in water-energy nexus systems, resilient microgrids, and data-driven predictive control. Her work integrates machine learning, nonlinear model predictive control (MPC), and cyber-physical security frameworks to enhance infrastructure resilience. Recent efforts include hardware-in-the-loop validation of control systems and stochastic modeling for offshore renewable energy. Key research areas include securing smart water systems against cyberattacks, optimizing energy dispatch in islanded microgrids, and developing adaptive algorithms for real-time operational challenges. Her interdisciplinary approach addresses challenges at the intersection of environmental engineering, control theory, and cybersecurity. Notable contributions include frameworks for detecting cyberattacks in water networks, economic dispatch models for water-energy systems, and stability-guaranteed control architectures for naval and civilian infrastructure.

Mason Porterمشاهده پروفایل

استاد

• Network Science
• Social Dynamics
• Nonlinear Dynamics
• +۴ مورد دیگر

Mason Porter is a Professor in the Department of Mathematics at the University of California, Los Angeles (UCLA). His research focuses on network science, nonlinear dynamics, and mathematical modeling of complex social systems. He explores topics such as opinion dynamics, temporal networks, multilayer networks, and the interplay between network structure and dynamical processes. Porter’s work spans theoretical and applied domains, including the analysis of social networks, epidemic spread, and infrastructure resilience. He has contributed to methods for detecting community structures, analyzing hypergraphs, and modeling collective behavior in systems ranging from online social media to biological networks. His recent studies emphasize bounded-confidence models, quantum walks on networks, and the application of topological data analysis to spatial systems. His research also intersects with interdisciplinary projects, such as modeling disease mitigation strategies, customer mobility in supermarkets, and the coevolution of disease spread and opinions. He has collaborated on initiatives like the NSF-funded project to predict microbiome assembly via multilayer networks. Porter’s publications reflect a deep engagement with both foundational theory and real-world applications, often leveraging computational and analytical techniques to uncover principles governing complex systems. His work bridges mathematics, physics, and social sciences, addressing challenges in data ethics, information diffusion, and network-driven phenomena.

Max Kellyمشاهده پروفایل

پژوهشگر ارشد

• Plastic Pollution
• Microplastic Analysis
• Textile Recycling
• +۲ مورد دیگر

Dr. Max Kelly is a Postdoctoral Research Fellow in the International Marine Litter Research Unit at University of Plymouth's School of Biological and Marine Sciences, leading projects on global plastic pollution mitigation. Primary research initiatives: PISCES: Systems analysis of plastic waste in Indonesian societies Future Fibres Network+: Embedding environmental science in fashion/textile industry Biotic clothing prototypes with Procter & Gamble Publications focus on microplastic sources (textiles, paint), pollution pathways, and treaty development. Recent work emphasizes quantification methods for environmental plastic waste and enzymatic textile treatments.

Richard Paigeمشاهده پروفایل

استاد

• Software Engineering
• Model-Driven Engineering
• Safety-Critical Systems
• +۱۰ مورد دیگر

Richard Paige is a Joseph Ip Distinguished Engineering Professor at McMaster University's Department of Computing and Software, Faculty of Engineering. He also holds a part-time position as Professor of Enterprise Systems at the University of York, UK. His primary research focuses on software engineering, particularly model-driven engineering, safety-critical systems, and low-code development. He leads the McMaster Centre for Software Certification (McSCert) and has extensive industry collaborations, including with Rolls-Royce, Leonardo, and NASA. Paige holds a PhD in Computer Science from the University of Toronto (1997), an MSc from the same institution (1994), and a BSc from McMaster University (1992). He has supervised over 22 PhD and 72 Master’s students, many of whom pursue careers in academia and industry. His funding exceeds $24M CAD from NSERC, Ontario Research Fund, and industry grants. His research emphasizes model management, assurance cases, and safety-critical systems. Key contributions include the Epsilon framework and advancements in model-driven tools. Paige chairs the STAF conferences and serves on editorial boards for journals like Software and Systems Modeling . He actively promotes open-source tooling and has won multiple best paper awards, including ACM Distinguished Paper at MoDELS (2010, 2017) and the Ten-Year Most Influential Paper Award (2016). Paige’s teaching focuses on problem-based learning and flipped classrooms, with awards for pedagogical innovation. He also leads interdisciplinary projects, such as SECT-AIR (reducing aerospace software costs) and CROSSMINER (developer-centric knowledge mining). His work bridges academia and industry, addressing challenges in automotive, healthcare, and aerospace sectors. Current projects include safety assurance for autonomous vehicles and model-driven sustainability evaluation.

• 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.