Find Academic Supervisors

• Actuators
• Digital Twin
• Flight Control Systems
• +9 more

Massimo Sorli is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Polytechnic University of Turin. He serves as a Scientific Advisor for the partnership agreement with Leonardo SpA and is actively involved in the Power Electronics Innovation Center (PEIC) . His academic leadership includes membership in the Board/Steering Committee of the Piedmont Aerospace District (2019-present) and the Scientific Committee of Custer Tecnologico Nazionale Aerospazio CTNA (2013-present). Research Interests : Actuators and Servo Systems Digital Twin and PHM (Prognostics and Health Management) Flight Control Systems in Aerospace and Automotive Mechatronic Design and Analysis Transmission Systems and Harmonic Drives Recent Research Trends focus on condition-based maintenance for aerospace systems, digital twin integration in actuation systems, and AI/ML applications for aircraft diagnostics. His work spans computational modeling of harmonic drives, vibration mitigation in helicopters, and ergonomic industrial design. Teaching includes courses on Servosystems and System Health Management at the doctoral and master's levels since 2019. He has supervised numerous PhD students in mechanical engineering cycles, with recent theses on Compact Electromechanical Actuators , Inflatable Robots for On-Orbit Servicing , and Model-Based Robot Health-Management . Patents cover aerospace actuation systems, harmonic drive diagnostics, and coffee machine flow regulators. His industry collaborations with Collins Aerospace, Lufthansa Technik, and Leonardo SpA focus on hydraulic diagnostics, flight control actuator testing, and asbestos risk reduction.

• Aerospace Engineering
• Control Systems
• Deep Learning
• +5 more

Dr Will Jacobs is a Research Fellow at the University of Sheffield's School of Electrical and Electronic Engineering. His work focuses on advanced control systems, nonlinear system identification, and deep learning applications in aerospace and energy systems. He is affiliated with the Amy Johnson Building in Sheffield, S1 3JD, and can be contacted at w.jacobs@sheffield.ac.uk or +44 114 222 5671. Research interests include distributed digital twins for fleet management, fault detection in civil nuclear plants, and interpretable machine learning for complex systems. His recent work emphasizes resource-constrained system monitoring, economic control strategies for aerospace fleets, and frequency-domain analysis for nonlinear systems. Jacobs' interdisciplinary approach bridges electrical engineering, control theory, and applied mathematics. Publications span 2014–2024, with a focus on gas turbine health monitoring, Bayesian system identification, and convolutional neural networks for in-flight anomaly detection. His research contributes to both theoretical advancements and practical industrial applications in energy systems and aerospace engineering. No scientific awards are explicitly listed in the provided information. His advising record is not publicly detailed here, though his involvement in doctoral training and research collaborations is implied through project participation.

Wenxian YangView profile

Professor

• Renewable Energy Engineering
• Offshore Wind Energy Systems
• Thermoelectric Materials
• +3 more

Wenxian Yang is a Professor of Renewable Energy Engineering at the Department of Engineering, School of Computing and Engineering, University of Huddersfield. His research focuses on renewable energy systems, including offshore wind turbines, thermoelectric materials, battery technology, and condition monitoring. He has contributed significantly to the UN Sustainable Development Goals related to affordable and clean energy. His work spans mechanical systems, hydrodynamics, and machine learning applications for fault diagnosis and energy harvesting. Key research areas include optimizing floating offshore wind turbine stability, improving lithium-ion battery performance and diagnostics, and developing advanced algorithms for structural health monitoring. Yang has published extensively, with over 130 peer-reviewed articles and an h-index of 42. He is actively involved in collaborations on tidal energy systems, biomimetic blade designs, and smart monitoring solutions for renewable energy infrastructure. His recent studies address challenges in offshore foundation scour mitigation, thermal management of batteries, and adaptive neural network models for predictive maintenance. Despite his prolific output, no specific awards or grants are explicitly listed in the provided text. Yang is currently accepting PhD students in renewable energy engineering and related interdisciplinary fields.

Faris ElashaView profile

Senior Lecturer

• Rotating Machines Diagnosis and Prognosis
• Condition Monitoring
• Asset Integrity and Management
• +3 more

Faris Elasha is a Senior Lecturer in Dynamics at Coventry University's CEES School of Engineering since September 2015. Previously, he served as a Research Fellow at Cranfield University and held roles in the Power Generation Industry, leading maintenance teams for power stations. He holds a First Class BSc in Mechanical Engineering from Sudan University of Science and Technology, an MSc (Distinction) in Mechanical Engineering Design from The University of Manchester, and a PhD in Mechanical Engineering from Cranfield University. His research focuses on condition monitoring, diagnostics, and prognosis of rotating machinery, particularly in gearbox dynamics, asset integrity management, and vibration-based analysis. He has contributed to developing the first condition monitoring system for tidal turbines. His work aligns with UN Sustainable Development Goals, emphasizing reliability improvement and cost reduction in mechanical systems. Key research trends in his publications include advanced signal processing techniques (e.g., wavelet transforms), machine learning applications for prognostics, and vibration analysis for fault detection in gears and bearings. His articles span aerospace (helicopter gearboxes), renewable energy (wind and tidal turbines), and general mechanical systems. Elasha has supervised one student and engaged in academic activities such as PhD examinations and collaborative research with institutions like Cranfield University. His expertise bridges industrial practice and academic research, addressing challenges in rotating equipment health management.

• Condition Monitoring
• Predictive Maintenance
• Robotics and Control
• +3 more

Dr. Xihui (Larry) Liang is an Associate Professor in the Department of Mechanical Engineering at the University of Manitoba, Canada. He holds a PhD in Mechanical Engineering from the University of Alberta (2016) and completed a postdoctoral fellowship there. His research focuses on condition monitoring, predictive maintenance, robotics, and reliability engineering, with applications in machinery and infrastructure systems. He is a recipient of the Terry G. Falconer Memorial Award for his work in developing intelligent monitoring systems. Education: PhD in Mechanical Engineering, University of Alberta, 2016 Research Interests: Dr. Liang's work spans advanced condition monitoring techniques for mechanical systems, robotics and control for manufacturing, and reliability modeling. Key areas include: Embedded systems and smart sensors for structural health monitoring Machine learning for fault diagnosis and RUL prediction Maintenance optimization strategies for wind turbines and gas engines Smart bearing design with self-sensing capabilities Recent Work Trends: His publications emphasize innovative sensor technologies (e.g., FBG and PVDF transducers) and machine learning approaches for predictive analytics. Recent studies address challenges in non-stationary signal processing and cross-domain fault detection. Awards: Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award (2024) Advising & Labs: Dr. Liang mentors graduate students in embedded systems, robotics, and reliability engineering. His lab develops advanced monitoring tools for industrial applications, including smart bearings and additive manufacturing quality control.

Chris BinghamView profile

Professor

• Energy Conversion
• Aircraft Dynamics
• Power Electronics
• +5 more

Chris Bingham is a Professor of Energy Conversion and Director of Research at the School of Engineering and Physical Sciences, University of Lincoln. He holds a B.Eng. from Sheffield City Polytechnic (1989), M.Sc. from University of Sheffield (1990), and a Ph.D. from Cranfield University (1994). His research focuses on energy conversion systems, control engineering, and sustainable urban development. He has held academic roles at the University of Sheffield from 1994-2010 and currently serves on the editorial board of Energies . Research interests include aircraft dynamics, power electronics, sensor validation, and the impact of climate change on urban energy systems. His recent work emphasizes real-time prognostics, low-cost drive systems, and digital twin applications in gas turbines. He collaborates with researchers like Amira Elnokaly and Xiaowen Liao on magnetic bearings and energy efficiency. Contributions span 142+ co-authored publications and editorial leadership. Grants and advising details are not explicitly listed here. His work bridges engineering innovation with sustainable urban and energy solutions.

Janis TerpennyView profile

Professor

• Smart Manufacturing Systems
• Design Education
• Digital Twin Technologies
• +3 more

Janis Terpenny is a Professor at George Mason University with joint appointments in the Department of Systems Engineering and Operations Research and the Department of Mechanical Engineering. She brings extensive leadership experience from previous roles including Dean of Engineering at the University of Tennessee, Department Head at Penn State and Iowa State, and Director of the NSF Center for e-Design. Her research focuses on smart integrated systems for manufacturing and design education innovations. Education: PhD and MS in Industrial and Systems Engineering from Virginia Tech, BS in Applied Mathematics from Virginia Commonwealth University Her research spans smart manufacturing systems, design engineering, digital twins, and engineering pedagogy. Recent work emphasizes predictive maintenance algorithms and democratizing additive manufacturing through cloud platforms. She has received recognition as Fellow of ASME and IISE. Key scientific contributions include developing data fusion frameworks for manufacturing quality control, creating cloud-enabled predictive maintenance systems, and advancing ontology-based design tools for product lifecycle management. Her editorial roles include Area Editor for Engineering Economist and board membership at Computers in Industry . NSF Center for e-Design Director Digital Manufacturing Commons™ Initiative Leader Industry experience with General Electric Professional affiliations include AAAS, Alpha Pi Mu, ASEE, SME, and Tau Beta Pi. Current research explores AI-driven manufacturing diagnostics, inclusive engineering education models, and cloud-based design optimization.

Gautam BiswasView profile

Professor

• Intelligent Systems
• Cyber-Physical Systems
• Reinforcement Learning
• +13 more

Gautam Biswas is the Cornelius Vanderbilt Professor of Engineering and a Professor of Computer Science and Computer Engineering at Vanderbilt University. He is affiliated with the Department of Computer Science in the School of Engineering and is a Senior Research Scientist at the Institute for Software Integrated Systems (ISIS). His research spans two major domains: intelligent systems for cyber-physical systems and intelligent learning environments for STEM and computer science education. In cyber-physical systems, his work focuses on Deep Reinforcement Learning, Unsupervised and Semi-supervised Anomaly Detection, and Online Risk and Safety Analysis applied to air and marine vehicles and smart buildings. In education, he develops open-ended learning environments, learning analytics, and data mining techniques to study student behavior, metacognition, and teamwork in augmented and mixed reality settings. His recent publications reflect a strong trend in applying AI and machine learning to both engineering systems and educational technologies, particularly in modeling complex system behaviors and understanding learner engagement through multi-modal data analysis. Notable scientific awards include: NASA 2011 Aeronautics Research Mission Directorate Technology and Innovation Group Award EDM 2023 Test of Time Award IEEE Fellow (2014) PHM Society Fellow (2016) APSCE Fellow (2020) Life Fellow IEEE (2020) Prof. Biswas has been a co-PI on major research grants from the NSF AI Institute for Engaged Learning (ENGAGE AI) and the IES GenAI for STEM Education Center (GENIUS). He has extensive experience mentoring students and leading interdisciplinary research teams. His research has been supported by the Army, Navy, NASA, NSF, DARPA, and the U.S. Department of Education. He leads two research labs: the Open Ended Learning Environments (OELE) Lab and the Modeling and Analysis of Complex Systems (MACS) Lab, where he directs projects integrating AI, systems engineering, and educational innovation.

Mohammad Ali AyoubiView profile

Associate Professor

• Multibody Dynamics
• Rotordynamics
• Spaceflight Dynamics
• +4 more

Dr. Mohammad Ali Ayoubi is an Associate Professor in the Department of Mechanical Engineering at Santa Clara University's School of Engineering. He holds a Ph.D. in Aeronautics and Astronautics from Purdue University (2007), an M.S. in Aerospace Engineering from Sharif University of Technology (1998), and a B.Sc. in Mechanical Engineering from Amirkabir University of Technology (1991). His research focuses on dynamics and control systems for aerospace applications, including: Multibody and spacecraft dynamics Vibration control of rotating machinery Intelligent control algorithms (fuzzy logic, neural networks) Guidance systems for reentry vehicles and spacecraft attitude control Recent publications (2016-2024) demonstrate strong emphasis on spacecraft control, vibration suppression, and optimal guidance methods, frequently employing advanced techniques like Takagi-Sugeno fuzzy models and data-driven approaches. Awards & Honors: AIAA Leadership Award (2019) ASME Fellow (2016) NASA Faculty Fellow (2013-2014) Researcher of the Year, Santa Clara University (2013) Teaching excellence awards from Purdue University (2004-2006)

• Battery Management Systems
• Lithium-ion Batteries
• State Estimation
• +5 more

Xiaolei Bian is a Researcher in the Automatic Control unit at Chalmers University of Technology, specializing in advanced battery management systems. He joined Chalmers in 2022 after completing his PhD at KTH Royal Institute of Technology, bringing expertise at the intersection of control engineering and electrochemistry. Dr. Bian earned his PhD in chemical engineering from KTH Royal Institute of Technology, Sweden, in 2021. His educational background combines deep knowledge of electrochemical processes with engineering principles, positioning him uniquely to address complex challenges in battery technology. His primary research focuses on advanced battery management systems, with particular emphasis on smart sensor integration, fast charging technologies, battery aging mechanisms, and electrochemical modeling. Dr. Bian's work addresses critical challenges in electric vehicle technology and energy storage systems, aiming to improve battery performance, longevity, and safety through innovative monitoring and control strategies. His research bridges theoretical modeling with practical implementation, developing methods that can operate effectively with limited data in real-world applications. Analysis of Dr. Bian's publication record reveals a strong trend toward developing practical battery state estimation techniques that work with partial or random charging data. His work increasingly integrates machine learning with traditional electrochemical models, creating hybrid approaches that balance accuracy with computational efficiency. A significant portion of his research targets real-time implementation constraints, making his methods particularly valuable for automotive applications where computational resources are limited. Serves on the advisory panel for the journal Batteries Organized the 2024 IEEE International Conference on Prognostics and Health Management (ICPHM) in Stockholm Contributed to the 49th Annual Conference of the IEEE Industrial Electronics Society in Singapore Helped organize the inaugural IET Energy Storage Conference in Glasgow Active reviewer for over 15 top-tier journals and multiple international conferences As a project co-PI or PI, Dr. Bian has secured significant research funding from the Swedish Energy Agency, the European Commission, and Chalmers Areas of Advance. His current projects focus on monitoring microscale inhomogeneity in lithium-ion batteries, developing physics-informed learning approaches for predictive maintenance of e-powertrains, and creating faster charging solutions with extended battery lifetime through advanced degradation monitoring using low-cost sensors. These projects involve collaboration with researchers across multiple disciplines including materials science, electrical engineering, and industrial automation. Dr. Bian works within Chalmers' Automatic Control research environment, collaborating closely with researchers in Materials Physics and other departments. His research group focuses on translating theoretical advances in battery state estimation into practical implementations for electric vehicles and energy storage applications, with strong industry connections that ensure relevance to real-world challenges.

• Modal analysis
• Nonlinear system identification
• Damping
• +7 more

Luigi Garibaldi is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin, Italy. He has been actively involved in teaching and research for several decades, with a focus on mechanical systems dynamics, vibration analysis, and machine diagnostics. His work spans multiple research projects funded by competitive tenders, commercial contracts, and regional/national grants. Professor Garibaldi's educational background is reflected in his extensive teaching portfolio, which includes courses such as "Mechanics Applied to Machines," "Vehicle Noise and Vibration," and "Dynamics of Mechanical Systems" at both undergraduate and graduate levels. He has served on doctoral college committees for Mechanical Engineering since the 19th cycle (2003/2004) through the current 40th cycle (2024/2025). His research interests center on modal analysis and identification of linear and nonlinear dynamic systems, characterization and use of viscoelastic materials, and monitoring, diagnostics, and prognostics of rotating machinery and mechanical systems. His work aligns with several Sustainable Development Goals, particularly those related to industry, innovation, infrastructure, clean energy, and health and well-being. His research spans multiple domains including aerospace engineering, computational engineering, industrial design, machine learning applications in signal processing, and mechanical manufacturing engineering. Analysis of Professor Garibaldi's recent publications reveals a strong focus on vibration-based diagnostics, particularly for rotating machinery, structural health monitoring, and pyroshock testing applications. His work demonstrates expertise in signal processing techniques, nonlinear dynamics, and the application of machine learning to predictive maintenance problems. He has contributed significantly to the field through his editorial work as Associate Editor for Mechanics & Industry (since 2014) and Mechanical Systems and Signal Processing (since 2004). His leadership extends to roles such as Scientific Committee co-chair for the Surveillance 9 conference and Directorate of Research and Valorisation for INSA - Euro Mediterranée. Professor Garibaldi has supervised PhD students, including Riccardo Barbera (40th cycle, 2024-present), and has collaborated extensively with industry partners on commercial research projects related to machinery diagnostics, vibration analysis, and structural health monitoring. His research group, Dynamics of Mechanical Systems and Identification (DIMEAS), has undertaken numerous projects for companies including RFI S.p.A., focusing on railway catenary dynamics and interaction with pantographs.

Fan ZhangView profile

Assistant Professor

• Cybersecurity
• Nuclear Engineering
• Data Analytics
• +9 more

Fan Zhang is an Assistant Professor at the Woodruff School of Mechanical Engineering at the Georgia Institute of Technology , specializing in cybersecurity of nuclear facilities , data analytics for fault detection , and nuclear systems modeling . She leads the iFAN Lab and collaborates internationally with organizations like the IAEA and DOE Office of International Nuclear Security . Education : Ph.D. in Nuclear Engineering (University of Tennessee, Knoxville, 2019), M.S. in Statistics (University of Tennessee, Knoxville, 2019), M.S. in Nuclear Engineering (North China Electric Power University, 2016), B.S. in Nuclear Engineering (North China Electric Power University, 2013). Her research focuses on cybersecurity , instrumentation & control , autonomous systems , and AI-driven nuclear diagnostics . She develops testbeds combining simulators and physical components for cybersecurity and process health management studies. Recent publications highlight her work on robot-assisted nuclear monitoring , radiation localization , and digital twin security , reflecting cross-disciplinary expertise in cyber-physical systems , ML robustness , and industrial threat mitigation . Scientific Awards : Ted Quinn Early Career Award (2021), Smoky Mountain Data Challenge Best Paper (2019), ANS Graduate Scholarships (2018-2020), UT Extraordinary Professional Promise Award (2018), Alpha Nu Sigma Honor Society (2017). Contact: fan.zhang@me.gatech.edu | Office: Boggs 371, Georgia Tech

Kshitij JerathView profile

Associate Professor

• Self-organized dynamics
• Multi-agent systems
• Robotic swarms
• +5 more

Kshitij Jerath serves as Associate Professor in the Department of Mechanical and Industrial Engineering, Robotics at the Francis College of Engineering, University of Massachusetts Lowell. His research focuses on self-organized dynamics in complex systems, multi-agent control, and robotic swarms, with significant contributions to traffic flow theory and sensor characterization. He directs the Emergent Dynamics, Control and Analytics Labs (EXALABS), advancing bottom-up control algorithms for minimal-intervention system guidance. Dr. Jerath's academic background includes: Ph.D. in Mechanical Engineering from Pennsylvania State University (2014), dissertation: 'Influential subspaces in self-organizing multi-agent systems' M.S. in Electrical Engineering from Pennsylvania State University (2011), thesis: 'Sensor noise modeling, characterization and simulation: An Allan variance tutorial' M.S. in Mechanical Engineering from Pennsylvania State University (2010), thesis: 'Impact of adaptive cruise control on the formation of self-organized traffic jams on highways' Bachelor's equivalent in Mechanical and Automation Engineering from Amity School of Engineering and Technology, India His research spans self-organized dynamics , multi-agent systems , and robotic swarm control , applying statistical mechanics principles to model emergent behavior in transportation networks and complex systems. Current work focuses on influencing macro-scale dynamics through minimal intervention by small agent subsets, with extensions to social ensembles and neural systems. His methodologies integrate control theory, network science, and machine learning for real-world applications in autonomous vehicles and system reliability. Recent publications (2023-2025) reveal strong trends in relational network applications for multi-agent learning, adaptive data granulation techniques, and human-swarm interaction frameworks. Key developments include database-inspired algorithms for sensor characterization, renormalization group approaches to traffic modeling, and fault-tolerant recovery mechanisms for robotic teams. These works demonstrate increasing convergence of control theory, database systems, and reinforcement learning in addressing complex system challenges. Dr. Jerath has received notable recognition including: Two Best Presentation awards at American Control Conference (2014, 2012) Kulakowski Travel Award from Penn State (2014) National Merit-cum-Means Scholarship from Indian Government (2013) 2nd place in ITS America Student Essay Competition (2012) His research is supported by grants including the CPS: Medium project 'Automated Discovery of Data Validity for Safety-Critical Feedback Control in Connected Vehicles' (2019) and a Graduate Teaching Fellowship from Penn State (2013). EXALABS maintains active collaborations with transportation agencies and robotics researchers to translate theoretical advances into practical applications. The Emergent Dynamics, Control and Analytics Labs (EXALABS) develops frameworks for modeling, quantifying, and influencing collective behavior across scales. Current projects include human-guided swarm control in virtual reality, traffic flow optimization using connected vehicle networks, and adaptive granulation techniques for large-scale sensor data. The lab employs interdisciplinary approaches combining control theory, statistical mechanics, and machine learning to solve problems in robotics, transportation, and system reliability.

Pingfeng WangView profile

Professor

• Reliability Engineering
• Systems Design
• Resilience Engineering
• +5 more

Pingfeng Wang is a Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds positions as Donald Biggar Willett Faculty Scholar and Jerry S. Dobrovolny Faculty Scholar. He earned his Ph.D. in Mechanical Engineering from the University of Maryland (2010), M.S. in Applied Mathematics from Tsinghua University (2006), and B.E. in Mechanical Engineering from the University of Science & Technology Beijing (2001). His research focuses on reliability analysis, system resilience, design under uncertainty, and battery technologies. Key areas include failure prognosis, reliability-based design optimization, and sustainable engineering systems. He leads the Reliability Analysis & Safety Assurance (RASA) Lab, collaborating on projects funded by NSF, DOE, and industry partners. Wang has authored/co-authored over 200 publications, including the book Engineering Design under Uncertainty and Health Prognostics . He has received numerous awards, such as the NSF CAREER Award (2014) and ASME Design Automation Young Investigator Award (2016). He teaches courses on Reliability Engineering, Decision Analysis, and Data-Driven Design. Professional activities include editorial roles in journals like Journal of Mechanical Design and leadership in organizations like IISE and ASME.

• Subsea Engineering
• Controls Engineering
• Systems Engineering
• +4 more

Rafik Borji serves as Lecturer of Subsea Controls and Systems Engineering within the Department of Mechanical and Aerospace Engineering at the University of Houston's College of Engineering. He functions as the Subsea Engineering Academic Advisor, with his office located in Engineering Building 2, Room W228 on the Houston campus. His academic contact email is subsea-advisor@me.uh.edu. His research spans Subsea Engineering and Biomedical Engineering, focusing on model-based approaches for transient two-phase flow in pipelines and tissue characterization using optical coherence tomography/ultrasound techniques. Key areas include cardiovascular diagnostics (arterial plaque analysis, VAD prognostics) and subsea flow assurance, demonstrating interdisciplinary integration of mechanical systems modeling with biomedical applications. Publications from 2009-2016 reveal dual research trajectories: subsea systems work emphasizes low-dimensional modeling for pipeline flow dynamics, while biomedical research develops quantitative methods for tissue property assessment and cardiovascular device monitoring. Both domains utilize advanced modeling techniques for system diagnostics and prognostics. No scientific awards or honors are documented in the available information. As Subsea Engineering Academic Advisor, Borji provides program guidance though specific student advisees and research grants are not detailed in the source material. Documentation does not reference laboratories, research teams, or specialized facilities associated with his work.