جست‌وجوی استاد و پژوهشگر

Jon Spangenbergمشاهده پروفایل

استاد

• Additive Manufacturing
• 3D Printing Technologies
• Computational Fluid Dynamics
• +۴ مورد دیگر

Jon Spangenberg is a Professor and Head of the Digital Building Technologies section at the Department of Civil and Mechanical Engineering , Technical University of Denmark (DTU). His work focuses on additive manufacturing, 3D printing technologies, and their applications in construction and materials science. He leads research on composite materials, computational fluid dynamics (CFD), and structural engineering innovations. Key research areas include: Additive manufacturing processes for construction materials (e.g., geopolymers, concrete) 3D-printed sensors and conductive materials Numerical modeling of material extrusion, pultrusion, and resin infusion Optimization of thermoset formworks and structural components Notable achievements include the Best Paper Award at Digital Concrete 2020 and recognition at the Solid Freeform Fabrication Symposium 2021 . He supervises multiple PhD projects on topics like topology optimization for AM, digital twins for sustainable 3D printing, and tomographic volumetric additive manufacturing. His work addresses UN Sustainable Development Goals related to affordable and clean energy and sustainable cities through advanced materials and construction techniques. Collaborations span academia and industry, focusing on process modeling, material characterization, and scalable manufacturing solutions.

Sara Kohtzمشاهده پروفایل

استادیار

• Data Science
• Physics-Informed Machine Learning
• Reliability Engineering
• +۳ مورد دیگر

Sara Kohtz is an Assistant Professor in the School of Systems Science and Industrial Engineering at Binghamton University. She holds a BS (2016) and MS (2017) in Industrial Engineering from Binghamton University and a PhD in Industrial Engineering from the University of Illinois at Urbana-Champaign (2024). Her research focuses on machine learning theory and applications in high-impact engineered systems, particularly physics-informed machine learning for energy systems, reliability engineering, and fault diagnosis in electrified systems. Education Background: PhD: Industrial Engineering – University of Illinois at Urbana-Champaign MS: Industrial Engineering – Binghamton University BS: Industrial Engineering – Binghamton University Her work bridges data science and engineering, addressing challenges in battery management, sensor placement, and optimal system design. Recent publications emphasize machine learning methodologies for prognostics, energy systems optimization, and physics-driven models. Professional memberships include ASME, IISE, and the Society of Women Engineers (SWE). No awards or grants are explicitly listed in the provided information.

• Autonomous Robot Systems
• Applied Ocean Science
• Quantum Engineering
• +۴ مورد دیگر

Mohammad Javad Khojasteh is a Gleason Endowed Assistant Professor in the Department of Electrical and Microelectronic Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). He directs the Khojasteh Autonomous Systems Laboratory (KAS Lab) and is affiliated with RIT's Global Cybersecurity Institute (GCI) and Center for Human-aware AI (CHAI). Before joining RIT, he held postdoctoral positions at Scripps Institution of Oceanography (MPL), MIT’s Department of Mechanical Engineering and LIDS, and Caltech’s CAST, collaborating with NASA JPL’s Team CoSTAR. Education: B.Sc. in Electrical Engineering and Mathematics, Sharif University of Technology (2015) M.Sc. in Electrical and Computer Engineering, UC San Diego (2017) Ph.D. in Electrical and Computer Engineering (Intelligent Systems, Robotics, and Control), UC San Diego (2019) Research Interests: Dr. Khojasteh focuses on autonomous systems, applying machine learning and control theory to enhance robotics, marine autonomous systems, and quantum engineering. His work spans cybersecurity in cyber-physical systems, data-driven optimization, and statistical signal processing. He emphasizes practical applications in ocean science and quantum technologies. Key Contributions: His research has been recognized with awards such as the Tammy L. Blair Student Paper Award. He explores topics like particle flow filters, event-triggered control, and secure autonomous navigation. His lab’s projects include developing robust algorithms for underwater robotics and quantum sensor networks. Advising & Grants: While specific grants are not detailed, his work indicates a focus on multi-agent systems, safety-critical control, and adversarial learning. He advises students in robotics and control systems, though no named advisees are listed. Labs & Teams: Director of KAS Lab, collaborating with GCI and CHAI on human-AI interaction and cybersecurity challenges in autonomous systems.

Paris Mastorocostasمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Computational Intelligence
• +۷ مورد دیگر

Paris Mastorocostas is a Professor at the University of West Attica, specializing in computational intelligence, signal processing, and algorithmic data mining. His research focuses on neuro-fuzzy systems, deep learning applications, and their integration into domains like energy systems, transportation networks, and industrial automation. He has pioneered methodologies for short-term load forecasting, telecommunications fraud detection, and adaptive noise cancellation in medical signals. Key research themes include: Neuro-fuzzy modeling for dynamic systems Machine learning in energy and transportation Data warehouse development using Python/MySQL UAV-based inventory quantification Graph neural networks for urban metro flow His work demonstrates a strong interdisciplinary approach, combining algorithm design with practical industrial applications. Notable contributions include the ReNFuzz-LF model for electricity load forecasting and TMD-BERT for transportation mode detection. His publications span over 25 years, showing sustained innovation in computational intelligence techniques and their real-world implementation.

• Biomedical Engineering
• Photonics Engineering
• Health Technology
• +۳ مورد دیگر

Sijung Hu is a Reader in Biomedical Engineering and Research Group Lead for Photonics Engineering & Health Technology at Loughborough University, UK. He holds senior positions including Visiting Professor at Shanghai Jiao Tong University and Chief Scientific Officer at Carelight Limited, a spin-out company commercializing his opto-physiological monitoring (OPM) technology. His career spans academic, industrial, and international collaboration roles, focusing on biomedical photonics, non-invasive physiological sensing, and health technology innovation. Education: PhD in Applied Science, Loughborough University (1996–1998) MSc in Environmental Management, University of Surrey (1993–1995) Research Interests: Dr. Hu specializes in opto-physiological monitoring systems, wearable sensors, and biomedical photonics. Key areas include: Non-invasive vital sign monitoring (heart rate, oxygen saturation) Development of OPM and DA 2 SD technologies Health technology commercialization via spin-outs like Carelight Respiratory pattern analysis for AAC (augmentative and alternative communication) Articles Trends: Recent work emphasizes real-time opto-physiological systems for motion-tolerant monitoring, adaptive signal processing algorithms, and integration of AI (e.g., GANs for PPG denoising). Applications span clinical diagnostics, sports science, and assistive technologies for speech-impaired individuals. Awards: 2014: Translation Research Award Nomination (SPIE) 2007: UK Health Technology Innovation Award 2011: Top 10 Article in BioMedLib Advising & Grants: Supervised numerous PhD students and coordinated multidisciplinary projects with academia/industry. Key collaborations include ACE Centre for AAC and Shanghai Jiao Tong University. Active in EPSRC grant reviews and international tech transfer initiatives. Labs & Teams: Leads the Photonics Engineering & Health Technology Research Group at Loughborough, and oversees Carelight’s R&D. Collaborates with global partners through initiatives like the UK-China Science Network and Shanghai International Medical Zone.

Qiuji Yiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Nondestructive Testing
• +۴ مورد دیگر

Dr. Qiuji Yi is an **Assistant Professor** in the **Computer and Information Sciences Department** at **Northumbria University**, focusing on applying artificial intelligence (AI) and machine learning (ML) to nondestructive testing (NDT) and structural health monitoring (SHM). His research develops AI-driven tools for NDT instruments like eddy current testing, thermography, and ultrasound, addressing challenges in composite materials such as carbon fiber composites. Yi holds a **PhD in Electrical and Electronic Engineering** (Awarded: 6 Apr 2021). **Research Interests**: Physics-informed ML for material characterization Defect detection in composites using clustering, matrix factorization, and deep learning Integration of AI with engineering needs in manufacturing and maintenance **Collaborations & Funding**: Collaborates with industry partners (Rolls-Royce, GKN Aerospace, National Composite Centre) and academic institutions (TU Delft, Newcastle University). Secured notable grants including the EU ITN project NDTonAIR (€3.8m), EPSRC grants (e.g., Certest: £6.9m), and the UK-Italy Trustworthy AI Visiting Researcher Award (2023). **Key Contributions**: Published 19 papers, including high-impact journals like NDT&E International and IEEE Transactions on Industrial Informatics . Notable work includes an automatic delamination detection framework using Kernel Principal Component Analysis. **Advising & Grants**: Accepting PhD students and actively pursuing funding for interdisciplinary projects combining AI and sustainable engineering solutions.

Deniz Erdogmusمشاهده پروفایل

استاد

• Neural Interfaces
• Machine Learning
• Biomedical Signal Processing
• +۲ مورد دیگر

Deniz Erdogmus is COE Distinguished Professor at Northeastern University with joint appointments in Electrical & Computer Engineering and Bioengineering. He directs the Cognitive Systems Laboratory and serves as CTO of Kostas Research Institute. His research spans statistical machine learning, neural interfaces, and biomedical data analytics. Research integrates information-theoretic learning with applications in brain-computer interfaces, assistive technologies, and robotic systems. Recent work focuses on EEG-based intent recognition, sensorimotor neural processing, and adaptive human-robot collaboration. Honors include NSF CAREER Award, Distinguished Faculty Award, and Søren Buus Research Award. His 200+ publications demonstrate consistent innovation in machine learning for physiological systems and real-time signal processing.

Anna Kaasinenمشاهده پروفایل

مدرس

• Mathematical Modelling
• Inverse Problems
• Industrial Process Control
• +۳ مورد دیگر

Anna Kaasinen is a University Lecturer at the Department of Technical Physics, University of Eastern Finland. Her research focuses on mathematical modeling, inverse problems, and their applications in industrial process control. She also actively contributes to pedagogical innovation, particularly in flipped learning methodologies and university mathematics education. Her teaching responsibilities include mathematics courses and study guidance for undergraduates, alongside participation in curriculum development. She co-authored the flipped learning guidebook Flippausmanuaali and led studies comparing flipped classroom effectiveness in medical certificate education. Key research areas include microwave drying processes of polymer foams using tomographic measurements, as well as university students’ study habits. Kaasinen is affiliated with the Inverse Problems research group and Team Ameba, contributing to interdisciplinary projects combining control systems with process tomography.

• Power Systems Engineering
• State Estimation
• Dynamic System Modeling
• +۲ مورد دیگر

Marcos Netto is an Assistant Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology. His research focuses on power systems engineering, state estimation, dynamic system modeling, and control theory with specialized expertise in Koopman operator applications. Research interests center on power system dynamics, grid stability, and renewable energy integration. Current projects include ASCENT, developing digital twins for resilient power grids, and uncertainty quantification in dynamic systems. Publications demonstrate consistent focus on Koopman operator theory applications for power systems, including state estimation methods, measurement uncertainty analysis, and controller design. Recent work emphasizes grid resilience and renewable integration challenges.

Dongfang Liuمشاهده پروفایل

استادیار

• Artificial General Intelligence (AGI)
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Dr. Dongfang Liu is an Assistant Professor in the Department of Computer Engineering at the Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering. He holds a Ph.D. from Purdue University (2021) and specializes in artificial general intelligence (AGI), vision-language systems, and interdisciplinary AI applications such as autonomous robotics, healthcare, and energy systems. His research is supported by the NSF and industry partnerships. Education: Ph.D. in Computer Science/Engineering, Purdue University (2021) Research Interests: Dr. Liu focuses on developing AGI systems to address societal challenges. His work integrates computer vision, robotics, and machine learning, with recent projects including vision-anchored UAV automation using RGB-D cameras. He emphasizes ethical AI, robustness against adversarial attacks, and real-world deployment in domains like autonomous driving and medical diagnostics. Service & Leadership: Area Chair, CVPR (2023–present) Senior Program Committee Member, AAAI/IJCAI (2023–present) Associate Editor, IEEE TCSVT, Neurocomputing, MTAP, ACM JATS (2023–present) Key Projects: "Vision-Anchored UAV Automation": Full automation of bird-sized UAVs in cluttered indoor environments using vision-only sensors, funded by NSF Grant #2242243. Developing AGI tools for fusion energy simulation, medical diagnostics, and sustainable energy. Teaching: CMPE-679 Deep Learning CMPE-789 Special Topics IMGS-890/891 Research & Thesis Supervision Grants/Awards: NSF Grant #2242243 (2023–2025): Supports UAV automation research.

• Robotics
• Control Systems
• Industrial IT
• +۳ مورد دیگر

Geir Hovland is a Professor at the Department of Engineering Sciences, University of Agder. He holds an MSc in Engineering Cybernetics from NTNU (1993) and a PhD in Robotics from the Australian National University (1997). His research focuses on robotics, control systems, industrial IT, and modeling/identification of dynamic systems. He serves as Chief Editor of the MIC Journal . Research Interests: Robotics and automation, including flexible manipulators and sensor networks Control systems for offshore and industrial applications Parallel kinematic machines and mechatronic systems Blockchain stability analysis (nonlinear control) Publications: Over 100 peer-reviewed articles in journals like Robotics , IEEE Transactions , and MIC Journal , with a focus on advanced control strategies, sensor optimization, and robotics in harsh environments. Labs/Teams: Leads the Norwegian Motion-Laboratory and collaborates with industry partners on projects like autonomous mooring systems and offshore crane modeling.

• Surgical Robotics
• Minimally Invasive Surgery
• Virtual Reality in Medical Training
• +۳ مورد دیگر

Caroline Giai Ly Cao serves as Professor in Industrial and Enterprise Systems Engineering, Bioengineering, and Biomedical and Translational Sciences at the University of Illinois Urbana-Champaign's Grainger College of Engineering. She directs both Engineering Innovation and Medical Simulation and Applied Health Technology Initiatives, driving interdisciplinary medical technology development. Her research focuses on surgical robotics and medical simulation, specializing in laparoscopic training systems with haptic feedback integration. Key interests include virtual reality applications for clinical practice, operator fatigue modeling in robot-assisted surgery, and stress response analysis in virtual environments. The fingerprint analysis confirms dominant expertise in minimally invasive surgery (76% weight) and virtual reality applications (25% weight). Recent publications reveal strong trends in mixed-reality medical imaging integration and neural network applications for surgical instrumentation. Her team develops decision support systems for robotic procedures while advancing human factors engineering in healthcare settings through fatigue modeling and stress induction studies. Scientific recognition includes: IEA Fellow (2024) As director of two major initiatives, Dr. Cao leads teams developing medical simulation tools that bridge engineering innovation with clinical practice. Her Applied Health Technology Initiatives program specifically creates translational solutions for surgical training and patient care enhancement through advanced simulation technologies.

Ann Sychterzمشاهده پروفایل

استادیار

• Structural Engineering
• Sustainable and Resilient Infrastructure Systems
• Adaptive and Deployable Structures
• +۵ مورد دیگر

Ann Sychterz is an Assistant Professor in the Department of Civil and Environmental Engineering and a Faculty Affiliate in Mechanical Science and Engineering at the University of Illinois Urbana-Champaign (UIUC). She holds multiple affiliations, including Faculty Fellow at the National Center for Supercomputing Applications and the Beckman Institute for Advanced Science and Technology. Her research focuses on adaptive, deployable structures and resilient infrastructure systems, leveraging geometrically nonlinear systems like tensegrity and origami. She leads the SMARTI lab, which develops innovative solutions for civil infrastructure challenges. Education: Ph.D. (2018) from Swiss Federal Institute of Technology Lausanne (EPFL), M.A.Sc. (2014) and B.A.Sc. (2012) from the University of Waterloo. Postdoctoral research at the University of Michigan. Research interests include optimized sensor placement, structural dynamics, machine learning in civil engineering, damage mitigation, and adaptive structures. Her work emphasizes sustainability and resilience against climate change impacts. Awards include the 2024 SEAOI Outstanding Young Engineer Award, ASCE SEI Future Leaders Award, and the 2023 ASCE SEI Futures Fund Young Professionals Award. She is actively involved in editorial roles and professional committees, including ASCE Structural Control and Sensing Technical Committee. Her lab's projects include deployable anchors for coastal erosion prevention and VR-based engineering education tools. She collaborates internationally, including a 2024 visiting professorship at the University of Stuttgart.

Sasan Gooranمشاهده پروفایل

عضو هیئت علمی

• Digital Halftoning
• Image Reproduction
• Color Reproduction
• +۳ مورد دیگر

Sasan Gooran is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), specializing in Media and Information Technology (MIT). He holds a PhD in Media Technology from Linköping University (2001), following M.Sc. (1994) and Licentiate (1998) degrees in related fields. His research focuses on digital halftoning, image reproduction, and 3D printing technologies. He has supervised seven PhD students and over 50 M.Sc. theses, contributing significantly to graphic technology and appearance modeling. Teaching includes courses in Graphic Arts, Computer Graphics, Image Processing, and Python programming. His work integrates theory and application in visual information technology, with collaborations in interdisciplinary projects like material appearance digitization and BRDF modeling. He actively participates in research groups exploring 3D printing materials and visualization technologies. Recent research highlights include 3D printing material analysis via spectrophotometry, empirical BRDF models for reflective inks, and adaptive halftoning algorithms. His contributions span over 20 years, emphasizing quality enhancement in printing and digital imaging.

Krisjanis Steinsمشاهده پروفایل

مدرس

• Healthcare Logistics
• Emergency Response Systems
• Transportation Systems
• +۳ مورد دیگر

Krisjanis Steins is a Lecturer at Linköping University's Department of Science and Technology (ITN), affiliated with the Communications and Transport Systems (KTS) division. His research focuses on healthcare logistics, emergency response systems, and quantitative modeling of transportation and healthcare systems. He specializes in applying discrete-event simulation to optimize emergency department accessibility, disaster response logistics, and healthcare resource planning. His work addresses challenges such as bleeding control kit placement, ICU resource allocation, and hospital operational efficiency. Key research affiliations include the Center for Advanced Research in Emergency Response (CARER). His publications span topics like resilient transport systems, crisis management, and sensor technology integration in emergency logistics. He actively collaborates with institutions on projects involving simulation-based solutions for healthcare and transport infrastructure.