جست‌وجوی استاد و پژوهشگر

• Functional Safety
• Functional Testing
• Microprocessor Testing
• +۹ مورد دیگر

Riccardo Cantoro is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is a member of the College of Computer, Film and Mechatronics Engineering and the College of Mechanical, Aerospace and Automotive Engineering. He is affiliated with the CAD - Electronic CAD & Reliability Group and the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His work bridges academic research and industrial applications through multiple commercially funded projects. Scientific Disciplinary Sector: IINF-05/A - Information Processing Systems ERC Sectors: PE7_4, PE6_2, PE6_11, PE6_12 His research focuses on functional safety, functional testing, and microprocessor testing, with a strong emphasis on embedded systems and reliability. He applies machine learning and formal methods to enhance test efficiency and system robustness, particularly in automotive and safety-critical domains. His work integrates computer-aided design, fault modeling, and resilience assessment in both hardware and AI systems. The recent publications highlight a trend toward data-efficient and intelligent testing methodologies, combining machine learning (e.g., TabPFN, active learning) with traditional electronic design automation. Topics include microcontroller performance screening, CNN resiliency, FeFET device testing, and system-level test optimization, reflecting a cohesive research agenda in trustworthy computing and hardware reliability. Scientific Awards: No awards explicitly mentioned in the provided texts. Advising and Grants: Dr. Cantoro supervises numerous PhD students in Computer and Systems Engineering, focusing on functional safety, test methodologies, and AI for CAD. He leads multiple industry-funded research projects, including collaborations with Infineon Technologies and Dana-TM4 Italia, on topics such as ATPG tools, speed monitor modeling, and power module reliability. His role as Scientific Manager/Head underscores his leadership in applied research and technology transfer. Labs and Teams: He is a core member of the CAD - Electronic CAD & Reliability Group (DAUIN) and contributes to the CARS@PoliTO center, fostering interdisciplinary research in automotive systems and sustainable mobility.

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks
• +۱۹ مورد دیگر

Professor Stephan Chalup is a leading academic in Artificial Intelligence and Machine Learning at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Data Science and Statistics department. He leads the Interdisciplinary Machine Learning Research Group (IMLRG) and the Newcastle Robotics Lab , where his team has achieved global recognition, including two RoboCup world championships. PhD in Computing Science , Queensland University of Technology (2002) Diplom in Mathematics with Neuroscience , University of Heidelberg His research focuses on artificial neural networks , deep learning , and high-dimensional data analysis , with applications in robotics, computer vision, medical imaging, and architectural analysis. He investigates how biological neural systems inspire robust AI models, particularly in topological data analysis and 4D vision . The recent publications highlight a strong trend in topological and geometric machine learning , with a focus on 4D data analysis , multi-agent reinforcement learning , and robot perception . His work bridges theoretical AI with practical industry solutions in transport, healthcare, and robotics. RoboCup World Champion (2008, 2006) Leadership Excellence, CESE (2024) Supervision Research Excellence Award (2015) Multiple Best Student Paper Awards (2019, 2018, 2011) Chalup has supervised numerous students and led significant research projects, including the ARC Discovery Project on estimating topology of low-dimensional data. His lab fosters interdisciplinary collaboration and innovation, with alumni working in top global tech roles. He is an active keynote speaker and program committee member in major AI conferences. His labs, including the Newcastle Robotics Lab , are equipped with state-of-the-art robots and computing systems, supporting cutting-edge research in humanoid robotics, autonomous navigation, and AI-driven data analysis.

Maryam Kamgarpourمشاهده پروفایل

استادیار

• Control Theory
• Game Theory
• Mechanism Design
• +۶ مورد دیگر

Maryam Kamgarpour is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Engineering. She previously held faculty positions at the University of British Columbia and ETH Zürich. Her work bridges stochastic control , multiagent learning , and game theory , focusing on safety-critical systems. Education: PhD in Engineering from UC Berkeley, BSc in Applied Science from University of Waterloo. Research Interests: Control under uncertainty, game theory, mechanism design, mixed-integer optimization, and applications to transportation, robotics, power grids, and healthcare. Her recent publications emphasize safe reinforcement learning , multirobot coordination , and stochastic trajectory planning , with applications to aircraft navigation and energy systems. She has received the European Union ERC Starting Grant, NASA High Potential Individual Award, and IEEE Transactions on Control of Network Systems Outstanding Paper Award. Scientific Awards: ERC Starting Grant (2016-2021) NASA High Potential Individual Award (2010) NASA Excellence in Publication Award IEEE Outstanding Paper Award (2022) PhD Students: Jordan Philip Christopher Maddux Anna Maria Ni Tingting Ren Kai Salizzoni Giulio Schlaginhaufen Andreas Vaishampayan Saurabh Dilip Vallat Gabriel Rémi Former EPFL student: Guo Baiwei

• Aerospace control systems
• Autonomous and assisted vehicles
• Biomedical engineering
• +۸ مورد دیگر

Carlo Novara is a Full Professor at Politecnico di Torino, affiliated with the Department of Electronics and Telecommunications (DET) and the Interdepartmental Center CARS@PoliTO - Center for Automotive Research and Sustainable Mobility. He serves as a member of the College of Computer, Film and Mechatronics Engineering and the College of Mechanical, Aerospace and Automotive Engineering. His research interests span aerospace control systems, autonomous and assisted vehicles, biomedical engineering, design of experiments, energy system control and optimization, model predictive control, modeling and simulation, set membership estimation, statistical analysis, system identification, machine learning, complex networks and systems, prediction and estimation, and quantum optimization. His work focuses on nonlinear and LPV system identification, filtering/estimation, time series prediction, nonlinear control, predictive control, data-driven methods, set membership methods, sparse methods, and nonlinear optimization with applications in automotive, aerospace, biomedical, and energy domains. His recent publications demonstrate strong trends in nonlinear model predictive control, physics-based system identification, and space applications. Many papers focus on computational efficiency for real-time control, with increasing emphasis on quantum optimization techniques and space mission applications like the LISA mission. His work bridges theoretical control methods with practical applications in automotive, aerospace, and energy systems. Professional Recognition: Member of IEEE TC on System Identification and Adaptive Control Member of IFAC TC on Modelling, Identification and Signal Processing Founding member of IEEE-CSS TC on Medical and Healthcare Systems Professor Novara has supervised numerous PhD students including Lucrezia Lovaglio, Giovanni Marinello, Francesco Cerrito, Sabrina Savino, Cesare Donati, and Mattia Boggio. His research has been supported by significant grants including the CNMS - Spoke 2 Sustainable Mobility Center (2022-2025), PRYSTINE ECSEL project (2018-2021), and multiple commercial research contracts with ESA and other aerospace organizations. He leads research in the Automatica research group at DET, focusing on advanced control algorithms for aerospace applications, data-driven control of autonomous vehicles and biomedical systems, quantum optimization for complex system design, and modeling and optimization of networked systems.

• Mechanical Design
• Machine Construction
• Bearings
• +۸ مورد دیگر

Chiara Gastaldi is an Associate Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin, specializing in mechanical design and machine construction. Her academic career spans multiple teaching roles across bachelor's, master's, and doctoral programs, with particular focus on sustainable design, mechanical engineering, and computational methods. She serves on the College of Mechanical, Aerospace, and Automotive Engineering and the College of Biomedical Engineering, contributing to curriculum development and academic governance. Dr. Gastaldi's research centers on bearings, friction, multiphysics modeling, and numerical modeling, with emphasis on sustainable and circular design approaches. Her work bridges traditional mechanical engineering with modern computational techniques, focusing on practical applications in aerospace engineering, computational engineering, fluid mechanics, and sustainable design. She leads the ISED (Industrial Systems Engineering and Design) research group, driving innovation in model-based systems engineering applied to sustainable product development. Her recent publications reveal a strong trend toward integrating sustainability principles into mechanical design, particularly through life cycle assessment methodologies applied to human-powered vehicles and circular design strategies. The research demonstrates growing interest in lattice metamaterials, friction modeling, and computational approaches to mechanical design optimization, with applications ranging from turbine blades to hydrogen storage systems. ASME Yetep Award (2016) ASME Yetep Award (2019) Dr. Gastaldi actively supervises multiple PhD students working on sustainable mechanical design, lattice metamaterials, and circular economy approaches. Her research portfolio includes significant projects funded by competitive calls and commercial contracts, such as PRIME for predictive maintenance, mechanical design of metal scrap crushing machines, CO2 footprint assessment of vehicle aftermarkets, and dynamic design of turbine blades with friction contacts for renewable energy applications. She also serves as an Associate Editor for the PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS. PART C, JOURNAL OF MECHANICAL ENGINEERING SCIENCE and participates in scientific committees including the ASME Technical Committee on Sound and Vibration and the International Committee on Joint Mechanics. Through the ISED research group, Dr. Gastaldi leads collaborative efforts in industrial systems engineering and design, with particular emphasis on sustainable and circular approaches to mechanical product development. Her team works closely with industry partners on practical applications of advanced mechanical design principles, while also mentoring the next generation of engineers through student teams like Policumbent, which focuses on human-powered vehicle design.

• Mechatronics
• Automotive Engineering
• Machine Learning
• +۲ مورد دیگر

Shailesh Sudhakara Hegde is a Fixed-term Assistant Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at Polytechnic University of Turin. His research focuses on the intersection of mechatronic systems, automotive engineering, and machine learning applications for intelligent transportation systems. Academic Role: Teaching collaborator for courses in Dynamic Design of Machines and E-Powertrain Components since 2022 Research Affiliation: Mechatronics Lab and rotating machines design group at DIMEAS Scientific Branch: IIND-03/A - Mechanical Design and Machine Construction His recent work investigates AI-based automotive systems reliability , soft error impacts on autonomous perception, and machine learning applications for energy-efficient driving assistance. Hegde contributes to both academic research and industry-oriented patents, including a hybrid powertrain control method (US20240157930A1) with practical applications in electric vehicle technology. Email Contact: shailesh.hegde@polito.it Curriculum: Available through university directory

Claus Danielsonمشاهده پروفایل

استادیار

• Control Theory
• Optimization
• Model Predictive Control
• +۵ مورد دیگر

Claus Danielson serves as Assistant Professor in the Department of Mechanical Engineering at the University of New Mexico, specializing in constrained planning, control, and optimization for safety-critical systems. His work bridges theoretical control theory with practical applications across robotics, autonomous vehicles, and energy infrastructure. His academic credentials include: PhD in Mechanical Engineering from University of California, Berkeley MS in Mechanical Engineering from Rensselaer Polytechnic Institute BS in Mechanical Engineering from University of Washington Danielson's research centers on Model Predictive Control (MPC), motion planning, and reference governors. He develops algorithms exploiting structural properties in large-scale systems to ensure safety and robustness. His lab creates provably safe motion planning solutions using invariant set theory and non-convex optimization, with applications spanning autonomous vehicles, spacecraft, HVAC, and energy storage networks. Current projects emphasize data-driven approaches for complex constrained systems. Analysis of his recent publications reveals dominant trends in data-driven invariant set methods (2023-2025), robust adaptive MPC for aerospace applications, and safety-guaranteed motion planning. His work increasingly integrates machine learning with control theory, particularly in randomized path planning and extremum seeking control for energy systems like concentrated solar power and HVAC networks. No scientific awards are documented in the provided materials. Danielson teaches Robot Engineering (ME-482/582) and Introduction to Feedback Control (ME-380/ECE-345), mentoring students in non-convex optimization, dynamic system modeling, and control algorithm design. His research collaborations include the UNM-AFRL Agile Manufacturing Laboratory and New Mexico EPSCoR Smart Grid Center, though specific grant details remain undisclosed. He directs the Model Predictive Control Lab, which focuses on developing motion planning algorithms for robotics and autonomous vehicles. The lab also advances control solutions for photovoltaics, concentrated solar, and smart grid technologies, emphasizing scalable algorithms for large-scale energy systems with multi-timescale dynamics.

Svetlana V. Porosevaمشاهده پروفایل

استاد

• Turbulence Modeling
• Uncertainty Quantification
• Computational Fluid Dynamics
• +۴ مورد دیگر

Dr. Svetlana V. Poroseva is a Professor at the Department of Mechanical Engineering, University of New Mexico (UNM), with a distinguished career in turbulence physics, uncertainty quantification, and bio-inspired design. Her work bridges computational fluid dynamics (CFD) with applications in renewable energy, power systems survivability, and aerospace engineering. Her educational background includes a Ph.D. in Fluid and Plasma Mechanics (1996) and an M.S. in Physics (1989) from Novosibirsk State University, Russia. She has held faculty positions at UNM since 2010, progressing from Assistant to Professor (2023). Her research focuses on high-order turbulence closures, data-driven modeling, and evidence theory for multi-model predictions, with a strong emphasis on quantifying uncertainty in simulations. Dr. Poroseva’s recent publications highlight advancements in bio-inspired rotor design, DNS analysis of mixing layers, and solar energy accessibility studies. She has developed software tools like FIESTA and Power System Graph Converter, both under open licenses. Her work spans turbulent flow validation, power grid topology optimization, and aerosol transmission modeling, particularly during the pandemic. Scientific Awards UNM School of Engineering Dean’s Excellence Lectureship Award (2022) Harrison Faculty Excellence Award (2021-2022) Rainforest Innovation Award (2022) AIAA Associate Fellow (2013) Fellowships at Stanford, CNRS, and CISM She advises numerous graduate and undergraduate students, leads the UNM AIAA Student Chapter, and collaborates internationally on projects involving NASA, Sandia, and European institutions.

Ali Badieiمشاهده پروفایل

مدرس ارشد

• Building Energy Research
• Renewable Energy Technologies for Buildings
• Low-Carbon Building Design
• +۵ مورد دیگر

Dr. Ali Badiei serves as Senior Lecturer in Building Services and Renewable Energy at Brunel University's College of Engineering, Design and Physical Sciences, within the Mechanical and Aerospace Engineering department. His expertise bridges academic research and practical engineering solutions for sustainable building systems. His educational background includes: PhD in Building Energy Research, Loughborough University (2018) MSc with Distinction in Low Carbon Building Design and Modelling, Loughborough University (2013) BSc (Hons) in Mechanical Engineering, Middle East Technical University (2012) Badiei's research centers on decarbonizing the built environment through innovative energy systems. Key focus areas include solar-assisted heat pumps, building-integrated renewable technologies, and low-carbon cooling solutions. His work combines dynamic energy modeling with experimental validation to address real-world challenges in energy efficiency and climate resilience, particularly emphasizing practical implementation in residential and commercial buildings. Current investigations explore AI-enhanced forecasting for dew point coolers and thermal storage optimization using nanomaterials. His publication portfolio demonstrates consistent advancement in building energy systems, with recent articles emphasizing data-driven approaches to thermal management, hybrid renewable systems, and climate-responsive design. Notable trends include increasing integration of machine learning for performance prediction and a strategic shift toward circular economy principles in energy system design. Professional recognitions include: Fellow of Chartered Institution of Building Services Engineers (FCIBSE) Fellow of Higher Education Academy (FHEA) Chartered Engineer (CEng) status As Course Director for MSc Renewable Energy Engineering, Badiei supervises PhD research in domestic decarbonization, Passivhaus analysis, and smart energy technologies. His funded projects span multiple continents with support from BEIS, InnovateUK, EU, and EPSRC, including patented solar-thermal innovations and large-scale field trials of heat pump systems. Current initiatives focus on retrofit solutions for social housing and modular data center cooling. His leadership extends to the Centre for Sustainable Energy Technologies, where he directs experimental validation of novel HVAC systems and collaborates with industry partners on technology commercialization through B2B channels.

Ioannis Lambadarisمشاهده پروفایل

استاد

• Network Traffic Modeling
• Quality of Service (QoS) Control
• Cloud Data Center Resource Allocation
• +۸ مورد دیگر

Ioannis Lambadaris is a Full Professor and Chancellor’s Professor at Carleton University's Department of Systems and Computer Engineering, Faculty of Engineering and Design. Holding a Ph.D. from the University of Maryland, he has contributed extensively to network performance analysis over 25+ years. Specializes in stochastic processes, cloud computing, and wireless edge systems Led Ericsson 5G Chair initiatives Supervised over 70 graduate students His research spans QoS control , VNF placement optimization , and IoT indoor localization , with over 170 publications. Recent work focuses on reinforcement learning and deep learning in network resource allocation. Scientific Recognition: Chancellor’s Professor Ericsson 5G Chair Contact: ioannis@sce.carleton.ca | Office: Mackenzie 4448, Ottawa, ON

Frank Puppeمشاهده پروفایل

استاد

• Artificial Intelligence
• Medical Image Analysis
• Document Analysis
• +۵ مورد دیگر

Frank Puppe is a Full Professor of Computer Science at the University of Würzburg, Germany, where he holds the Chair of Computer Science VI (Artificial Intelligence and Applied Computer Science) within the Faculty of Mathematics and Computer Science. He is also affiliated with the Center for Artificial Intelligence and Data Science (CAIDAS) and leads research in artificial intelligence, knowledge systems, and applied computer science. His educational background includes a Diploma in Computer Science from Bonn University (1983), a dissertation on Diagnostic Problem Solving from Kaiserslautern University (1986), and a habilitation on Problem Solving with Expert Systems from Karlsruhe University (1991). Professor Puppe's research spans multiple domains of artificial intelligence and its applications. His primary focus areas include Medical Image Analysis , where he develops AI systems for endoscopic disease detection and medical information extraction; Document Analysis and OCR , with significant contributions to processing historical documents and musical manuscripts; and Information Extraction from diverse domains including medical, legal, and literary texts. His work in E-Learning and E-Assessment has led to innovative systems for automatically evaluating programming assignments and argumentation structures. His recent publications demonstrate a strong interdisciplinary approach, bridging computer science with medicine, digital humanities, and law. A notable trend is the application of deep learning techniques to historical document analysis and medical imaging, while maintaining a strong foundation in knowledge-based systems. His research consistently focuses on practical applications of AI that solve real-world problems across multiple domains. 2015-2017: Senator at University of Würzburg 2011-2013: Dean at University of Würzburg 2008-2011: Dean of Students at University of Würzburg Professor Puppe leads multiple significant research projects including KINERGY (optimization of heating systems), DZ-PTM (order entry optimization in radiology), Corpus Monodicum (edition of medieval Latin music), and projects related to adenoma detection in colonoscopy. His laboratory develops tools such as OCR4all for historical document processing and it4all for programming assessment.

Francesco Leofanteمشاهده پروفایل

پژوهشگر ارشد

• Explainable AI
• Counterfactual Explanations
• Robustness
• +۳ مورد دیگر

Francesco Leofante is a Research Fellow at Imperial College London , affiliated with the Centre for Explainable AI . His work focuses on Explainable AI (XAI) , particularly counterfactual explanations with formal robustness guarantees against perturbations. Imperial College Research Fellowship DAAD AINet Fellowship (Safety and Security in AI) Imperial PFDC Supporting Research Staff and Students Award 2023 Research Interests center on Explainable AI , emphasizing counterfactual explanations , robustness , model multiplicity , and formal verification in critical systems like energy and aviation. His work bridges AI , formal methods , and human-AI collaboration . Publications include studies on robust counterfactual explanations , parametric ReLUs for verification, and multi-agent systems with formal guarantees. These appear in top venues like AAAI , KR , IJCAI , and AAMAS , often addressing AI safety and trustworthy systems . Scientific Awards include the Imperial PFDC Supporting Research Staff and Students Award 2023 , DAAD AINet Fellowship , and Imperial College Research Fellowship . He also contributes to workshops and program committees at conferences like AAAI, KR, and IJCAI. Future Work includes expanding robust XAI into critical infrastructure systems (energy, aviation) and developing tools like OMTPlan for AI planning and verification .

• Ergonomics and Human Factors
• Risk Assessment and Management
• Multi-Criteria Decision Making
• +۶ مورد دیگر

Prof. Dr. Gülin Feryal Ural is a Professor of Industrial Engineering at Baskent University's Faculty of Engineering. With a PhD in Industrial Engineering from Kocaeli University (2013), she has established herself as a prominent researcher in ergonomics, risk assessment, and multi-criteria decision making. Her academic journey includes a Master's in Business Administration (2006) and BSc in Industrial Engineering (2004), both from Kocaeli University. Dr. Ural's educational background is deeply rooted in Industrial Engineering, with a PhD from Kocaeli University in 2013, followed by a Master's degree in Business Administration in 2006 and her undergraduate degree in Industrial Engineering in 2004, all from the same institution. This strong foundation has enabled her to bridge theoretical knowledge with practical applications in her research and teaching. Her research interests span multiple domains within Industrial Engineering, with a strong focus on Ergonomics and Human Factors Management , Risk Assessment and Management , and Multi-Criteria Decision Making methodologies. Dr. Ural has pioneered approaches integrating various decision-making techniques like KEMIRA-M, DEMATEL, and Entropy methods to address complex problems in occupational safety, usability evaluation, and production systems. Her work often bridges theoretical frameworks with practical industrial applications, particularly in healthcare, manufacturing, and educational settings. Analysis of Dr. Ural's extensive publication record reveals a clear trajectory toward increasingly sophisticated risk assessment methodologies and ergonomics applications. She has consistently advanced the integration of multiple decision-making techniques to create holistic assessment frameworks. Her research shows particular strength in adapting these methods to specific industry contexts, from healthcare to manufacturing, and increasingly incorporating machine learning approaches for risk prediction. The interdisciplinary nature of her work is evident in publications spanning engineering, healthcare management, and educational research domains. Dr. Ural has actively contributed to academic service through editorial roles, including membership on the editorial board of the International Journal of Intelligent Computing and Cybernetics and serving as an editor for the International Journal of Lean Six Sigma since 2023. Her teaching portfolio is extensive and reflects her research expertise, covering courses such as Ergonomics and Human Factors Management, Work Study, Lean Production, Occupational Health and Safety, Risk Analysis and Risk Management, and Special Topics in Industrial Engineering. This comprehensive teaching approach ensures her students receive both theoretical foundations and practical applications of Industrial Engineering principles.

• Chaos Control
• Nonlinear Dynamics
• Power Electronics
• +۳ مورد دیگر

Cristina MOREL is a Permanent lecturer-researcher at ESTACA since 2020, specializing in electrical engineering and control systems. Previously, she held academic positions at ESEO (2016-2020) and the IUT of Angers (2000-2020), and began her career at the Technical University of Cluj-Napoca (1992-1999). Her research focuses on nonlinear dynamics, chaos control, and applications in power electronics and renewable energy systems. Dr. MOREL holds a Doctorate in Automatics and Applied Computer Science from the University of Angers (2005) and a Habilitation to Direct Research (2016). She earned her Graduate Engineer degree in Automation and Computer Science from the Technical University of Cluj-Napoca (1992), where she was Vice-major of her promotion. 2016: Habilitation to Direct Research in Electronics, University of Angers 2005: Doctorate in Automatics and Applied Computer Science, University of Angers 1992: Graduate Engineer in Automation and Computer Science, Technical University of Cluj-Napoca Her research spans chaos control , nonlinear dynamics , and their applications in power electronics and renewable energy systems . She pioneered anticontrol of chaos to reduce electromagnetic interference in power converters and developed novel maximum power point tracking (MPPT) algorithms for photovoltaic systems under partial shading. Current work focuses on fault diagnosis for electric vehicle powertrains using entropy analysis and neural networks. Recent publications (2022-2025) show strong focus on chaos applications for MOSFET thermal management, entropy-based fault diagnosis in inverters, and energy storage optimization for electric vehicles and drones. There is clear evolution toward data-driven approaches (neural networks) for fault detection and real-world applications in e-mobility. Dr. MOREL has received several academic honors early in her career: Winner of the National University Assistant Competition (Romania, 1996) Winner of the National University Preparation Competition (Romania, 1993) Winner of the national entrance exam to the Polytechnic School of Cluj-Napoca (1987, 15th out of 700 candidates) She has supervised five PhD theses and three Master's research projects on topics including fault diagnosis in polyphase machines, energy management for battery-ultracapacitor vehicles, and MPPT for photovoltaic systems. Her research is supported by collaborations with University of Angers, ESEO, and ESTACA laboratories. Dr. MOREL led the Automation and Electrical Engineering research group at ESEO (2016-2018) and the Energy and Environment option (2016-2020), conducting experimental work in power electronics and control systems laboratories focused on converter design and EV powertrain validation.

Hilary Bart-Smithمشاهده پروفایل

استاد

• Biotechnology and Biomolecular Engineering
• Biomolecular Design
• Cellular and Molecular Bioengineering
• +۶ مورد دیگر

Hilary Bart-Smith is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia. She founded the Multifunctional Materials and Structures Laboratory and the Bio-inspired Engineering Research Laboratory, emphasizing biomimetic designs and advanced mechanical systems. B.S., Mechanical Engineering, University of Glasgow Ph.D., Engineering Sciences, Harvard University Research Interests: Bio-inspired systems, Biotechnology and Biomolecular Engineering, Biomolecular Design, and Cellular and Molecular Bioengineering. Her work spans underwater robotics, deployable aerospace structures, and electro-active polymers with applications in artificial muscles and sensing technology. Publications (2025–2021): Focus on bio-inspired underwater propulsion, tuna and manta ray robotics, flexible foils, hydrodynamic efficiency, and deployable aerospace structures. Keywords include Bio-inspired Robotics, Computational Fluid Dynamics, and Electro-active Polymers. Labs: Multifunctional Materials and Structures Laboratory and Bio-inspired Engineering Research Laboratory at UVA.