جستجوی استاد راهنمای دانشگاهی

Danijela Tadićمشاهده پروفایل

استاد

• Production Engineering
• Industrial Engineering
• Decision-Making Models
• +۵ مورد دیگر

Danijela Tadić is a Professor at the Department of Production Engineering, Faculty of Engineering, University of Kragujevac, Serbia. Her primary research focuses on decision-making models under uncertainty, manufacturing process optimization, failure mode analysis, and sustainable recycling technologies in the automotive and industrial sectors. She holds a leadership role in the Faculty of Engineering, contributing to academic governance and curriculum development. Her scientific work integrates fuzzy logic systems, multi-criteria decision-making (MCDM), and optimization algorithms to address complex engineering challenges. Notable areas include risk management in supply chains, quality improvement in manufacturing processes, and environmental impact assessment of recycling equipment. She has collaborated on projects evaluating organizational resilience, strategic failure management, and resource planning in SMEs. Prof. Tadić’s publications emphasize practical industrial applications, such as PFMEA integration with fuzzy decision tools, cloud platform selection frameworks, and electric vehicle evaluation models. Her work bridges theoretical advancements in fuzzy set theory with real-world manufacturing and environmental sustainability problems. She actively participates in academic and professional networks, contributing to conferences and journals in production engineering and industrial systems. Her expertise spans project lifecycle management, lean manufacturing, and the evaluation of recycling technologies for end-of-life vehicles (ELVs).

Jiling Fengمشاهده پروفایل

مدرس ارشد

• Fluid Mechanics in Cardiovascular Systems
• Biomedical Engineering
• Arterial Waveform Analysis
• +۳ مورد دیگر

Dr. Jiling Feng is a Senior Lecturer in Mechanical Engineering at Manchester Metropolitan University, specializing in fluid mechanics applied to cardiovascular systems and biomedical engineering. Her research focuses on arterial waveform analysis, stent design, and material science for vascular disease treatment, supported by over £1.1 million in grants from EPSRC, UKRI, and Royal Society. She holds a BEng, MSc, PhD, and is a Chartered Engineer (CEng) and Fellow of the Higher Education Academy (FHEA). Education: BEng, MSc, PhD Professional Memberships: IMechE, IEEE, British Atherosclerosis Society Research interests include computational modeling of cardiovascular mechanics, fluid-structure interaction in arteries, and biomaterials for medical devices. She has authored over 40 peer-reviewed articles in top journals like Biomechanics and Modelling in Mechanobiology and serves on editorial boards for Mathematics and Frontiers in Biophysics . Recent projects include a KTP collaboration with Krohne (£235,000) and a Royal Society grant with Beijing University of Technology (£11,600). She supervises PhD and MSc projects on plaque mechanics and arterial waveforms. Labs/Teams: Active in vascular mechanics research groups, collaborating with vascular surgeons and industry partners.

• Automation Engineering
• Robotics
• Materials Science
• +۳ مورد دیگر

Andrés Suárez García is an Assistant Professor at the University of Vigo's Department of Systems and Automation Engineering. His teaching includes courses on Systems and Control Engineering, Industrial Computing, Robotics, and Automation Fundamentals. He has consistently taught across multiple academic years from 2014/2015 to 2024/2025, covering disciplines like structural mechanics, fluid dynamics, and manufacturing quality control. His research focuses on interdisciplinary engineering applications, emphasizing automation, robotics, additive manufacturing, and energy systems. Notable projects include optimizing 3D printing parameters, analyzing lithium-ion battery health using machine learning, and developing IoT-based educational platforms. He also explores naval and military engineering challenges, such as energy storage for submarines and structural design for space exploration vehicles. Over 20+ supervised final-year projects highlight his mentorship in cutting-edge technologies like piezoelectric energy harvesting, supercapacitor integration in military vessels, and AI-driven anomaly detection in maritime routes. His work bridges theoretical engineering principles with practical applications in defense, environmental monitoring, and sustainable infrastructure.

Jiong Tangمشاهده پروفایل

استاد

• System Dynamics
• Control
• Sensing and Automation
• +۷ مورد دیگر

Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.

Kerry Gilmoreمشاهده پروفایل

استادیار

• Organic Chemistry
• Flow Chemistry
• Process Development
• +۵ مورد دیگر

Kerry Gilmore is an Assistant Professor at the University of Connecticut's Department of Chemistry. She holds the Lawrence E. Posner and Amy E. Newburger Fellowship and leads the Research Group at the Max-Planck Institute of Colloids and Interfaces (2014-2020). Her work focuses on flow chemistry to enhance reproducibility, efficiency, and control in organic synthesis, including new reaction development, multistep syntheses, and integration of computational chemistry, machine learning, and automation. Key projects involve API synthesis, singlet oxygen oxidations, and automated flow platforms. Education: Ph.D. in Organic Chemistry (Florida State University, 2012), B.S. in Biology and Chemistry (Roger Williams University, 2006). Postdoctoral training at Max-Planck Institute (2012-2014). Research emphasizes sustainable and efficient chemical processes, with breakthroughs in crystallization optimization and green synthesis of antimalarial drugs like artemisinin. She has pioneered machine learning applications for predicting glycosylation stereoselectivity and developed modular flow chemistry devices. Awards include the 2021 ACS Award for Affordable Green Chemistry and the 2022 Outstanding Early Career Paper Award. Her lab actively recruits graduate students and postdocs, emphasizing innovation in flow chemistry and automation. Labs/Teams: Gilmore Lab at UConn, collaborating with Max-Planck Institute on flow chemistry and automation projects. Current initiatives include drug synthesis optimization and AI-driven reaction design.

Johann Kastnerمشاهده پروفایل

استاد

• Computed Tomography
• Materials Science
• Non-Destructive Testing
• +۳ مورد دیگر

Johann Kastner is a Professor at Upper Austria University of Applied Sciences, leading the Research Center Wels Computed Tomography R&D-Headquarters. He is affiliated with Centers of Excellence in Automotive/Mobility, Energy, and Smart Production. His research focuses on advanced materials characterization using X-ray computed tomography (X-CT), with applications in non-destructive testing, composite materials, and biomedical engineering. Key research areas include porosity analysis in carbon fiber reinforced polymers, phase contrast imaging, and additive manufacturing. He has led over 10 projects, including the EU-funded xCTing initiative (2021–2025) and the X-PRO project (2020–2024), emphasizing industrial CT applications and cross-virtuality data analysis. His work spans 335+ publications, with notable contributions to XCT-based defect detection, material microstructure analysis, and AI-driven image segmentation. Collaborations include COMET K Projects and FTI-Structurförderung grants. He has advised 2 PhD students and actively participates in international conferences and workshops. Laboratory facilities include state-of-the-art XCT systems for 3D microstructural analysis, Talbot-Lau grating interferometry, and augmented reality visualization tools for industrial applications.

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.

Martin Kuentzمشاهده پروفایل

استاد

• Pharmaceutical Technology
• Drug Delivery Systems
• Oral Formulations
• +۳ مورد دیگر

Prof. Martin Kuentz is a Professor of Pharmaceutical Technology at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), leading the research group on Quality by Design of oral dosage forms. His primary affiliation is with the School of Life Sciences and the Institute for Pharma Technology and Biotechnology . He holds a Ph.D. in Pharmaceutical Technology from the University of Basel (1999) and a Pharmacist degree (1995). Prior to academia, he worked at F. Hoffmann-La Roche Ltd. as a Senior Scientist and Coordinator of Galenical Development Projects. Research Focus: His work centers on lipid-based formulations, amorphous solid dispersions, and computational tools for drug delivery. Key techniques include Raman spectroscopy, UV imaging, and molecular dynamics simulations. He has pioneered methods to enhance solubility of poorly water-soluble drugs using mesoporous silica, hydrophobic deep eutectic solvents (HDES), and machine learning-driven approaches. Publications & Awards: Prof. Kuentz has authored over 90 peer-reviewed articles and received awards such as the IPEC Foundation Best Manuscript Award (2014) and multiple Best Poster Awards at Swiss Pharma Science Days (2012–2015). He is a member of the American Association of Pharmaceutical Scientists (AAPS), Arbeitsgemeinschaft der Pharmazeutischen Verfahrenstechnik (APV), and the Swiss Society of Industrial Pharmacists (GSIA). Teaching: He teaches courses on industrial processes in life sciences, pharmaceutical chemistry, and quality management at both bachelor and master levels. He is certified in university didactics from the University of Basel (2008). Lab/Team: Leads the Oral Formulations for Chemical Drugs research team at FHNW. Grants/Advising: Supervises multiple Early Stage Researchers (ESRs) in the PEARRL network, including main supervision of ESR 2 and ESR 5, and co-supervision of ESR 1, 3, 4, and 6.

• Machine Learning
• Computer Science
• Medical Informatics
• +۷ مورد دیگر

Yan Li is a researcher with extensive contributions across interdisciplinary domains including Machine Learning , Signal Processing , and Environmental Science . Affiliated with institutions such as the University of Southern Queensland , Hebei University , and Shandong University , Li's work spans applications in Medical Informatics , Remote Sensing , and Operations Research . Recent publications highlight expertise in Deep Learning (e.g., hyperspectral classification, image fusion), Stochastic Modeling (e.g., chemotaxis models), and Federated Learning (e.g., vertical federated fuzzy clustering). Collaborative projects include 3D Reconstruction , Smart Grid Security , and Landslide Monitoring using satellite data. Li's 2025 work demonstrates a focus on Medical Imaging (segmentation algorithms with dual-frequency decoupling), AI in Education (ChatGPT adoption), and Industrial IoT (GPU-accelerated vessel trajectory visualization). While no explicit academic rank is provided, their prolific publication record suggests a Researcher role.

• Electrical Engineering
• Automotive Engineering
• Machine Learning
• +۳ مورد دیگر

Shengdun Zhao is an active researcher in the fields of Electrical Engineering, Automotive Engineering, and Machine Learning, contributing extensively to optimization techniques and control systems for electric vehicles and motors. His work spans journals like IEEE Transactions on Vehicular Technology and Journal of Intelligent & Fuzzy Systems , focusing on practical applications of deep reinforcement learning, meta-learning, and multi-objective optimization. Key research areas: Electric motor control, energy management systems, and clustering algorithms. Collaborates with researchers such as Yiming Zhang, Wei Du, Chee-Kong Chui, and Chin-Boon Chng. His publications from 2007–2025 address technical challenges in mechatronics, sustainable transportation, and data-driven engineering solutions. Research Trends Recent articles highlight Zhao's emphasis on deep reinforcement learning for motor control, meta-learning in energy systems, and evolutionary algorithms for multi-objective optimization. He integrates machine learning with automotive engineering to improve electric vehicle efficiency and motor performance.

• Cryogenics
• Heat Transfer
• Thermal Management
• +۴ مورد دیگر

Dr. S. Vanapalli is an Associate Professor in the Energy Materials Systems department at the University of Twente, specializing in cryogenics, heat transfer, and microcooling technologies. With over 920 Scopus citations and a 16 h-index, their research focuses on cryogenic systems, phase change materials, and thermal management solutions for biomedical and industrial applications. Research Highlights: Developed cryogen-free snap-freezing devices for tissue preservation Innovated gas-gap heat switches for thermal control systems Investigated Leidenfrost dynamics in liquid nitrogen and dry ice systems Optimized microchannel cooling architectures with isotropically etched pillars Explored additive manufacturing applications in cryogenic components Scientific Impact: Vanapalli's recent work spans cryogenic cold chain logistics, direct-contact freeze concentration, and sublimation temperature modeling. Their research bridges fundamental thermodynamic studies with practical applications in pharmaceuticals and precision oncology, with recent emphasis on eliminating sacrificial cryogens in biomedical freezing protocols.

Ji Maمشاهده پروفایل

استادیار

• Additive Manufacturing
• Multifunctional Materials
• Tailored Materials
• +۷ مورد دیگر

Ji Ma is an Assistant Professor in the Department of Materials Science and Engineering at the University of Virginia. His research focuses on additive manufacturing of metallic alloys, microstructure control, and multifunctional materials design. He explores novel material properties through 3D printing techniques, including spatially tailored properties, 3D concrete printing, and medical implant applications. His work addresses challenges in porosity, residual stress, and corrosion resistance in additively manufactured materials. Education: Ph.D. Mechanical Engineering, Texas A&M University (2012) B.S. Civil Engineering, Texas A&M University (2008) Research Interests include: Additive Manufacturing Multifunctional Materials Tailored Materials Orthopaedic Implants His projects span metallic alloys, multi-material printing, and bio-inspired materials for healthcare and construction. Grants and Collaborations: Virginia Innovation Partnership grant for commercializing 3D-printed wrist replacement technology Lead of $4.3M DARPA project to develop corrosion-resistant materials for naval systems Cross-disciplinary work with architecture and environmental sciences on sustainable 3D-printed soil structures Labs/Teams: Directs the Advanced Additive Manufacturing and Materials Group, focusing on innovation in material design, process optimization, and application-driven solutions for industry and healthcare.

• Service-Oriented Architectures
• Cloud Computing
• Data Management
• +۷ مورد دیگر

Professor Kyriazis Dimosthenis holds a faculty position at the Department of Digital Systems, University of Piraeus. He earned his diploma in Electrical & Computer Engineering from the National Technical University of Athens (2001) and a cross-disciplinary MSc in Techno-Economic Systems (2004). His academic rank is Professor specializing in service-oriented architectures with a focus on quality of service and workflow management. He has led European projects like BigDataStack, CrowdHEALTH, and CYBELE, addressing challenges in cloud computing, edge computing, and AI-driven solutions for healthcare, finance, and industrial sectors. His research emphasizes resilient service-oriented systems, AI explainability, and human-centric digital transformation. Notable contributions include frameworks for dynamic resource allocation in hybrid cloud/edge environments, AI applications for maritime safety, and data governance solutions for cross-sector integration. He coordinates initiatives such as the Future Internet Architecture Board and Cloud QoS&SLAs, driving advancements in federated data marketplaces and sustainable computing practices. His recent work explores Large Language Model (LLM) applications in financial decision-making, conversational AI for MLOps, and neurosymbolic systems for defect detection. He also investigates XAI methodologies, including VirtualXAI, which leverages GPT-generated personas for explainability assessment. His projects often bridge technical innovation with societal impact, such as the iHELP platform for holistic health records and SmartCHANGE for behavioral change strategies in youth health. Key themes in his publications include bias mitigation in machine learning, dynamic deployment prediction in hybrid cloud settings, and energy-efficient data spaces for mobility. He has contributed to standards like the H2020-funded IRMOS and 5GTANGO, emphasizing interoperability and fault-tolerant architectures. His work frequently intersects with EU policy frameworks, particularly in data governance and ethical AI implementation.

Laila Cureمشاهده پروفایل

دانشیار

• Healthcare Systems Engineering
• Operations Research
• Mathematical Modeling
• +۳ مورد دیگر

Dr. Laila Cure is an Associate Professor in the Department of Industrial, Systems and Manufacturing Engineering at Wichita State University's College of Engineering. She holds a Ph.D. from the University of South Florida. Her research focuses on applying analytics and mathematical modeling to healthcare systems, particularly in understanding human-technology interactions in uncertain environments. Key areas include healthcare operations optimization, quality improvement, and risk management. Dr. Cure teaches courses such as Probability and Statistics, Work Systems, and Data Mining and Analytics. She leads the Health Systems Engineering (HSE) Lab, which specializes in healthcare operations analysis, optimization, and data-driven decision-making. The lab employs tools like statistical analysis, simulation, and mathematical programming to improve healthcare delivery processes. Her work emphasizes practical applications, such as optimizing ambulance crew workflows, reducing hospital readmissions, and enhancing infection control through strategic facility design. She has advised students like Dr. Parisa Eimanzadeh, who pursued a tenure-track faculty position after her defense. Dr. Cure's research spans trauma care quality, clinical workflow interruptions, and healthcare technology integration, contributing to both academic and applied advancements in the field. Contact: Laila.Cure@wichita.edu , Industrial and Manufacturing Engineering Department, Wichita State University.

Josep Casanovasمشاهده پروفایل

استاد

• Modelling and Simulation
• Internet and Information Systems
• Urban Mobility
• +۴ مورد دیگر

Josep Casanovas is a Full Professor at the Statistics and Operations Research Department of the Technical University of Catalonia (UPC), affiliated with the Barcelona School of Informatics. He previously served as head of inLab FIB (2012-2020) and as dean (1998-2004) and vice-rector (2006-2011) of UPC, leading strategic initiatives in university governance and ICT policies. His research focuses on Modelling and Simulation , Internet and Information Systems , and Urban Mobility . He has led projects for the European Union, including C-ROADS Spain, REMEDiAL, and ECHORD++, addressing intelligent transport, software automation, and robotic innovation. Recent publications highlight his work on agent-based simulation for urban health, deep learning applications in traffic and energy savings, and wildfire management tools . He co-directs LogiSim and coordinates the Severo Ochoa Research Excellence Program at the Barcelona Supercomputing Center (BSC-CNS).