جستجوی استاد راهنمای دانشگاهی

• Network Optimization
• Infrastructure Resilience
• Supply Chain Risk
• +۳ مورد دیگر

Nafiseh Ghorbani-Renani is a Teaching Professor in the Department of Systems and Enterprises at Stevens Institute of Technology. Her research develops mathematical frameworks for managing risk in interconnected systems such as critical infrastructure, supply chains, and energy networks. Education includes: PhD in Industrial and Systems Engineering (University of Oklahoma) MS in Industrial Engineering (University Technology Malaysia) BS in Industrial Engineering (Yazd University) Research integrates optimization techniques with machine learning for resilience planning in interdependent networks. Current projects examine microgrid energy trading, supply chain disruption management, and infrastructure restoration scheduling. Recent publications focus on tri-level optimization models for critical infrastructure protection, energy equity in community microgrids, and machine learning applications for network resilience. Articles demonstrate consistent advancement in modeling complex interdependencies across infrastructure systems. As Faculty Advisor for the Software Division, she guides student projects bridging theoretical models with practical implementation. Course instruction spans software engineering fundamentals, agile methodologies, and systems development lifecycle management.

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +۸ مورد دیگر

Paolo Buono is Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. He holds a PhD in Computer Science with specialization in Visual Data Analysis. His research focuses on Information Visualization, Visual Analytics, Human-Computer Interaction, and Mobile Applications. Co-founder and CEO of LARE (2010), a university spinoff providing real-time surgical support through audio-video telestration Member (since 2002) and computer science coordinator at METEA Research Center for environmental protection Visiting scientist at AVIZ (France), University of Maryland (USA), and Fraunhofer IPSI (Germany) His work spans multiple application domains including: Cultural Heritage through interactive exploration systems Healthcare with smart therapeutic devices Environmental Monitoring via CET system IoT-based Smart Interactive Experiences He has contributed to: Dynamic hypergraph visualization techniques End-User Development frameworks (EUDroid) Usability evaluation methodologies Mobile health applications As project leader, he has coordinated: EU-funded VisMaster Coordination Action (2008-2010) Italian Ministry-funded LOGIN project (2014-2015) Apulia Region environmental projects His professional engagements include: Co-chair roles at INTERACT, AVI, IS-EUD conferences Program committee participation in VIS series and HCI conferences Member of ACM, IEEE, and SIGCHI Italy

Chengzhou Tangمشاهده پروفایل

استادیار

• 3D Computer Vision
• Deep Learning
• Robotics
• +۳ مورد دیگر

Chengzhou Tang is an Assistant Professor in the Department of Computer Science at the University of Manitoba. His research focuses on 3D computer vision, deep learning, robotics, and embodied artificial intelligence. He leads the Spatial Intelligent System (SPINS) Lab, exploring topics such as 3D object reconstruction, camera localization, and video processing techniques. His work spans applications in robotics, virtual reality, and computational imaging. Dr. Tang's research interests include developing novel algorithms for multi-view diffusion models, deep recurrent optimizers, and subspace methods. He has contributed to advancements in video depth estimation, point cloud processing, and stabilization of 360-degree videos. His recent work emphasizes generative models and embodied AI, addressing challenges in sparse-view 3D reconstruction and scene-agnostic camera localization. His publications reflect a trend toward integrating deep learning with traditional computer vision problems, such as structure-from-motion and SLAM (Simultaneous Localization and Mapping). The SPINS Lab collaborates on projects involving global bundle adjustment networks and initialization-robust monocular SLAM systems. While no awards or grants are explicitly listed, his active publication record indicates sustained research engagement in these areas. Dr. Tang’s academic profile highlights his contributions to foundational techniques in computer vision and their practical applications in robotics and AI systems. His work bridges theoretical advancements with real-world challenges in imaging and spatial intelligence.

• Multimodal Signal Processing
• Biomedical Signal Processing
• Image and Speech Processing
• +۳ مورد دیگر

Richard Dansereau is a Professor and Associate Dean (Graduate Studies) in the Department of Systems and Computer Engineering at Carleton University, part of the Faculty of Engineering and Design. He holds a Ph.D. from the University of Manitoba and is a Professional Engineer (P.Eng.) and Senior Member of IEEE. His research focuses on signal processing, including biomedical applications, compressive sensing, medical imaging, and fractal complexity analysis. He has led the Signal Processing and Machine Learning Lab and advised numerous PhD students in areas like PET image reconstruction and drone detection through Riemannian geometry. His academic roles include serving as Clerk of Senate and Academic Editor for IET Signal Processing. Key research contributions span deep learning for compressive sensing (e.g., DEQ-based networks), cervical cell segmentation, and biomedical signal processing. Over 150 publications highlight his work on topics like PET-MRI fusion, audio-visual speech enhancement, and radar systems. Collaborations include projects on cardiac PET imaging and drone detection algorithms. Awards include the IEEE Senior Member designation. His teaching spans courses such as Digital Signal Processing, Wavelets, and Biomedical Systems across Carleton and the Georgia Institute of Technology.

Muhammad M. Sherifمشاهده پروفایل

استادیار

• Smart Materials
• Structural Systems
• Machine Learning Applications
• +۴ مورد دیگر

Dr. Muhammad M. Sherif is an Assistant Professor in the Department of Civil, Construction, and Environmental Engineering at the University of Alabama at Birmingham (UAB), part of the School of Engineering. He joined UAB in Fall 2019 after completing his Ph.D. at the University of Virginia and M.S. at Carnegie Mellon University, both in structural engineering. His research focuses on smart materials, structural systems, and machine learning applications in civil infrastructure. He is particularly interested in additive manufacturing for construction and the development of innovative materials like engineered cementitious composites. Education: B.S., United Arab Emirates University M.S., Carnegie Mellon University Ph.D., University of Virginia Research Interests: Dr. Sherif’s work spans material characterization, machine learning models for structural analysis, and the integration of advanced materials into infrastructure systems. He explores topics like crack detection using UAVs, superelastic shape memory alloys, and multi-objective optimization in welding processes. His Advanced Materials and Smart Infrastructure Systems (AMSIS) lab emphasizes interdisciplinary approaches to solving civil engineering challenges. Publications: His recent work includes studies on UAV-based pavement crack detection, machine learning for concrete strength prediction, and optimization of tube-to-tubesheet joints. These reflect trends toward AI-driven solutions and sustainable material innovations. Advising: He actively mentors students in multidisciplinary research projects, emphasizing self-motivation and innovation. His lab collaborates on topics like composite materials, structural health monitoring, and infrastructure resilience. Labs/Teams: His AMSIS lab at UAB focuses on advancing smart materials and infrastructure systems through cutting-edge research and collaboration.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Dr. Masoud Makrehchi is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, part of the Faculty of Engineering and Applied Science. His research focuses on Natural Language Processing, Artificial Intelligence, Machine Learning, and Social Computing, with a strong emphasis on applications in network science and moral AI. He holds a PhD from the University of Waterloo (2007), along with earlier degrees from Shiraz University and Iran University of Science and Technology. Prior to academia, he worked as a Senior Research Scientist at Thomson Reuters (2008–2012) and completed a postdoctoral fellowship at the University of Waterloo (2007–2008). His research expertise includes text mining, social network analysis, and network science, addressing challenges such as signed social network analysis, document classification, and crime trend prediction using social media data. He has received several awards, including the NSERC Postgraduate Scholarship and the SBP Challenge Award (2012). His work spans over 50 peer-reviewed publications in journals like Expert Systems with Applications, Social Network Analysis and Mining, and Web Intelligence, with contributions to conferences such as GECCO and IEEE/ACM. Dr. Makrehchi’s publications highlight innovations in feature selection, text segmentation, and AI-driven frameworks for requirements elicitation. His recent work explores coherence graphs for text segmentation, generative AI in education, and bias detection in medical datasets. He has also contributed to patent applications related to sentiment analysis and text extraction systems.

• Structural Engineering
• Energy Efficiency in Buildings
• Sustainable Architecture
• +۳ مورد دیگر

Juliana Felkner is an academic affiliated with ETH Zurich, primarily associated with the Department of Architecture within the School of Architecture, Civil and Environmental Engineering. Her research focuses on integrating structural efficiency with energy performance in tall buildings, emphasizing multi-objective optimization and sustainable design principles. Key contributions include studies on embodied vs. operational energy in buildings, structural design frameworks balancing sustainability and efficiency, and interactive optimization techniques using NURBS curves and Particle Swarm Optimization (PSO). Her doctoral thesis (2016) explored multi-objective design strategies addressing structural, environmental, and architectural considerations. Felkner has published extensively on topics such as energy lifecycle analysis, natural ventilation potentials, and computational design methods. Her work highlights the importance of reducing operational energy in existing building stocks to mitigate greenhouse gas emissions. Awards and grants are not explicitly mentioned in the provided texts, but her peer-reviewed publications indicate active engagement in academic research and collaboration across disciplines.

Min Sunمشاهده پروفایل

استاد

• Educational Policy
• Teacher Education & Research
• AI/ML in Education
• +۸ مورد دیگر

Dr. Min Sun is a Professor in the Department of Educational Policy, Organization and Leadership at the University of Washington's College of Education. Her research focuses on teacher learning, AI/ML integration in education, and policy-driven educational reforms. She leads interdisciplinary teams developing AI tools like the NSF-funded Colleague lesson planning platform and the IES-funded AmplifyGAIN Center. Her work addresses inequities in education through policy analysis and partnerships with K-12 schools and EdTech industries. Dr. Sun holds a Ph.D. in Educational Policy and Measurement from Michigan State University. She teaches courses such as EDLPS 302: Intro to Educational Policy and EDLPS 564: Economics of Education. Her research spans four key areas: AI/ML method development, AI-powered educational tools, data science training programs, and policy research with multi-sector collaborations. Notable grants include a $10 million IES grant for the AmplifyGAIN Center and a $1.5 million NSF grant for AI-driven math lesson planning. Her policy work emphasizes equitable education access and data-driven solutions. She directs the Education Policy Analytics Lab (EPAL) and collaborates with stakeholders to translate research into actionable strategies.

Micael Derelövمشاهده پروفایل

دانشیار

• Industrial Engineering
• Product Development
• Reliability Engineering
• +۵ مورد دیگر

Micael Derelöv is an Associate Professor at Linköping University's Department of Management and Engineering (IEI), specializing in Product Realisation (PROD). His work focuses on optimizing safety, reliability, and efficiency in industrial and aerospace systems. He contributes to sustainable product development through advanced methodologies in design optimization and failure analysis. His research integrates robotics, manufacturing systems, and systems engineering to address challenges in collaborative assembly, aircraft design, and risk management. Dr. Derelöv’s research interests include multi-objective optimization for balancing safety and weight in aircraft systems, industrial safety demonstrators, and reliability-centric design processes. He has developed frameworks for evaluating design concepts and identifying potential failures in early-stage engineering projects. His work also explores the application of genetic algorithms in concept synthesis and the use of qualitative modeling for risk assessment. His publications highlight trends in industrial safety, systems reliability, and aerospace engineering. Recent work includes advancements in safe collaborative robotics on assembly lines and methodologies for industrial safety demonstrators. Earlier contributions address cost optimization in reliability-focused design and bio-mechatronic product development. While no specific scientific awards are listed, his active research and academic role reflect a commitment to advancing engineering practices. He collaborates on student projects, such as a recent initiative designing a pressure-resistant device for space exploration, demonstrating engagement in applied and interdisciplinary research. Micael Derelöv’s affiliation with the Department of Management and Engineering positions him at the intersection of academic research and industrial innovation, particularly within the Product Realisation group. His work emphasizes sustainable, integrated approaches to product development, blending theoretical insights with practical applications in manufacturing and aerospace sectors.

• Ecohydrology
• Water Quality Management
• Environmental Impact Assessment
• +۶ مورد دیگر

Dr. Pouyan Nejadhashemi is an MSU Research Foundation Professor in the Departments of Plant, Soil and Microbial Sciences and Biosystems & Agricultural Engineering at Michigan State University. He serves as Director of the MSU Institute of Water Research and the Center for Intelligent Water Resources Engineering. His expertise spans ecohydrology, climate change adaptation, environmental impact assessment, and decision support systems for ecosystem sustainability. He holds a Ph.D. in Biological Resources Engineering from the University of Maryland. Dr. Nejadhashemi's research focuses on water quality modeling, non-point source pollution prevention, and integrating machine learning with hydrological systems. Key areas include irrigation optimization, PFAS contamination monitoring, and watershed management. He teaches courses on water resources systems analysis and ecohydrology. Awards: MSU Research Foundation Professor Title Grants: FAA grant for PFAS remediation, grants for developing agricultural innovations in Senegal and Bangladesh Labs/Teams: MSU Institute of Water Research, Center for Intelligent Water Resources Engineering His work bridges computational methods with environmental challenges, addressing global water security, food systems, and sustainable resource management. Recent projects include modeling water quality in the Chesapeake Bay and developing AI tools for agricultural extension platforms.

Ning Wangمشاهده پروفایل

استاد

• 5G and beyond networking
• Future Internet design
• Mobile content delivery technologies
• +۵ مورد دیگر

Professor Ning Wang is a leading academic in communication systems at the University of Surrey's Institute for Communication Systems (ICS), School of Computer Science and Electronic Engineering. He holds a PhD from the University of Surrey (2004) and has expertise in 5G/6G networks, edge computing, and space-terrestrial integration. As a coordinator for the EuroMaster Programme and Communication Networks and Software (CNS) pathway, he leads research in network management, mobile video delivery, and IoT applications. His work has been featured in IEEE ComSoc Technology News three times since 2012. Current leadership roles include 5GIC Work Area 1 leader for content and network context. Research collaborations span global institutions like UCL, ETH Zurich, and industry partners like BT and InterDigital. Notable contributions include SDN-based space-terrestrial network integration (VDPA scheme) and O-RAN automation via federated DRL. Over 130 publications and active participation in standards bodies (IETF, 3GPP) reflect his impact on future network architectures. Educations: BEng in Computing (Changchun University of Science and Technology, 1996) MEng in Electronic Engineering (Nanyang Technological University, 2000) PhD in Electronic Engineering (University of Surrey, 2004) Research Focus: Future Internet design, network intelligence, content-centric networking, and satellite integration. Key projects include EU Horizon Europe SPIRIT (immersive telepresence), ESA TINA (satellite 5G functions), and EPSRC NG-CDI (converged digital infrastructures). His research emphasizes practical solutions like edge-AI for VNF splitting and holographic frame synchronisation. Grants & Projects: Over £20M in grants from EPSRC, EU Horizon, InnovateUK, and Royal Society. Active in EU-funded SAT5G (satellite-terrestrial 5G) and C-DAX (smart grid cybersecurity).

Ozgur Ozdemirمشاهده پروفایل

استاد پژوهش

• Unmanned Aerial Systems (UAS)
• mmWave Technology
• Wireless Communication
• +۷ مورد دیگر

Ozgur Ozdemir is an Associate Research Professor in the Department of Electrical and Computer Engineering at North Carolina State University's College of Engineering. He is affiliated with the university's primary faculty and works closely with platforms like AERPAW and AFAR Challenge. His research focuses on advanced wireless systems, including UAV networks, mmWave technology, spectrum analysis, and radar-communication integration. His work spans experimental testbed development, digital twin applications for AI research, and methodologies for improving coverage in rural and urban environments. Ozdemir has contributed to standards like 5G NR and non-standalone 5G systems, emphasizing practical implementation and environmental impact analysis. He leads efforts in signal processing for autonomous systems and has expertise in software-defined radio (SDR) technologies. Research interests include aerial threat detection, RF signal analysis, and robust localization techniques. He explores challenges in NLOS bias correction, antenna radiation patterns, and passive reflector-based coverage enhancement. His work often combines theoretical models with experimental validation using platforms like AERPAW. His articles highlight advancements in drone detection systems, channel modeling for mmWave, and the integration of radar and communication systems. Key trends show a focus on outdoor/indoor spectrum measurements, UAV navigation, and resilient network design. Ozdemir's contributions are characterized by empirical studies and cross-disciplinary approaches to wireless challenges.

• Wearable Computing
• Human-Computer Interaction
• Machine Learning
• +۳ مورد دیگر

Silvia Santini is an Associate Professor at the Faculty of Informatics of USI since 2016, leading the People-Centered Computing Lab. Previously, she held roles at TU Dresden (2014–2016) and TU Darmstadt (2011–2014), focusing on embedded systems and wireless sensor networks. She completed her PhD at ETH Zurich in 2009 and earned a Telecommunication Engineering degree from Sapienza University of Rome (2004). Her research centers on wearable computing, human-computer interaction, and sensor networks applied to health monitoring, activity recognition, and physiological signal analysis. Notable projects include BiHeartS (bilateral heart rate monitoring) and studies on sleep quality using wearable devices. Key achievements include being ranked among the World’s top 2% of Scientists (2022) and securing grants for projects like XAI-FinCrime. She advises on PhD and postdoc positions, such as the 2025 Innosuisse-funded project in explainable AI for financial crime detection. Labs/Teams: People-Centered Computing Group (USI), collaborating on cross-disciplinary projects in pervasive computing and healthcare technology.

Kaiyu Hangمشاهده پروفایل

استادیار

• Robotics
• Robotic Manipulation
• Physical Interactions
• +۵ مورد دیگر

Kaiyu Hang is an Assistant Professor of Computer Science at Rice University, directing the Robotics and Physical Interactions Lab (RobotΠ Lab). He holds a PhD and MSc from KTH Royal Institute of Technology and a B.Eng. from Xi’an Jiaotong University. His postdoctoral research was conducted at Yale University. His research focuses on robotic systems capable of physically interacting with the environment and humans, emphasizing algorithms in optimization, learning, and control. Key areas include manipulation systems (small-scale grasping to large-scale multi-robot manipulation), robust control, and energy-efficient UAV perching mechanisms inspired by nature. His work has been featured in MIT Technology Review, Science Robotics, and NPR. Hang has received notable awards such as the NSF CAREER Award (2023) and ASME Rising Star (2024). He serves on editorial boards for IEEE Robotics and Automation Letters (2019–present), ICRA (2021–present), IROS (2020–present), and Humanoids (2019). He also organizes the 10th Robotic Grasping and Manipulation Competition (RGMC) at ICRA 2024. As a faculty advisor for the Rice Robotics Club and on the CS Graduate Admission Committee, Hang actively mentors students and promotes inclusivity in robotics through initiatives like Inclusion@RSS. His lab’s projects aim to enhance manipulation robustness, develop novel UAV landing gear, and advance nonprehensile manipulation via motion planning and control.

Pietro Valdastriمشاهده پروفایل

استاد

• Surgical Robotics
• Robotic Endoscopy
• Medical Robotics
• +۵ مورد دیگر

Professor Pietro Valdastri is a Full Professor and Chair in Robotics & Autonomous Systems at the University of Leeds. He directs the Science and Technologies Of Robotics in Medicine (STORM) Lab, the Institute of Robotics, Autonomous Systems and Sensing (IRASS), and the Robotics at Leeds network. His expertise spans surgical robotics, robotic endoscopy, and magnetic manipulation, with a focus on developing soft magnetic surgical robots (SMSRs) for minimally invasive medical procedures. Valdastri holds a Laurea in Electronic Engineering (University of Pisa, 2001) and a PhD in Biomedical Engineering (Scuola Superiore Sant’Anna, 2006). He previously served as Assistant Professor at Scuola Superiore Sant’Anna (2006–2011) and Vanderbilt University (2011–2016) before joining Leeds in 2016. His research aims to leverage magnetic actuation for surgical navigation, with applications in cancer therapy and endoscopy. Key awards include the NSF CAREER Award (2015), ERC Consolidator Grant (2019), and KUKA Innovation Award (2019). He is a Royal Society Wolfson Research Fellow, IEEE Fellow, and editor for IEEE Robotics and Automation Letters. His work has been featured in major media outlets like the BBC, New Scientist, and WIRED. Valdastri has secured over £25M in grants from NSF, NIH, ERC, EU-H2020, and industry partnerships. He co-founded WinMedical srl (acquired in 2017) and Atlas Endoscopy Limited, advancing robotic colonoscopy platforms. Current research emphasizes autonomous robotic systems, magnetic tentacles for lung therapy, and patient-specific surgical tools.