Find Academic Supervisors

• Sustainable Development
• Disruptive Innovation
• Entrepreneurship Education
• +3 more

Dr. Carla Riverola is a Senior Lecturer in the Department of Management at Griffith University's Griffith Business School. Her research focuses on disruptive innovation for sustainable development, particularly in plant-based foods and AI-driven educational strategies. She holds a Ph.D. in Information Technologies and Management from Ramon Llull University, Barcelona. Professional roles include Deputy Director of the Griffith Business School (2023-2024) and International Lead (2022-2023). She is affiliated with the Centre for Work, Organisation and Wellbeing and Social Marketing@Griffith. Education: Ph.D., Information Technologies and Management, Ramon Llull University M.Sc., IT Management, Ramon Llull University B.Eng., Computer Science, Ramon Llull University Affiliations: Centre for Work, Organisation and Wellbeing Social Marketing@Griffith Climate Action Network International Educators Her research bridges entrepreneurship, sustainable innovation, and AI applications. Recent work examines plant-based food adoption and AI's role in organizational decision-making. She has received the Group Award for Excellence in Teaching (2024) and the Knut Holt Best Paper Award (2017). Her articles span innovation diffusion, food sustainability, and technology networks. Dr. Riverola advises on policy and industry partnerships, contributing to initiatives like the Catapult Program Roadmap (Logan City Council). She actively collaborates with international stakeholders to advance sustainable development goals, particularly Climate Action (SDG 13) and Responsible Consumption (SDG 12).

• Instrumentation Electronics
• Memristor Modelling
• Signal Integrity
• +6 more

Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.

Zia SaadatniaView profile

Assistant Professor

• Smart Structures and Materials
• Nonlinear Vibration and Structural Dynamics
• Energy Harvesting
• +5 more

Dr. Zia Saadatnia is an Assistant Professor at Ontario Tech University's Department of Mechanical and Manufacturing Engineering, part of the Faculty of Engineering and Applied Science. He holds affiliations with the KITE Research Institute (University Health Network) as an Affiliate Scientist and the University of Toronto's Department of Mechanical and Industrial Engineering as an Assistant Professor (Status-only). His research focuses on advanced materials, energy harvesting, and biomedical devices, with notable contributions to aerogel composites, triboelectric nanogenerators, and functional electrical stimulation technologies. Education: Ph.D., Mechanical Engineering, University of Toronto (2019) M.Sc., Mechanical Engineering, Ontario Tech University (2015) B.Sc. (Hons), University of Science and Technology, Iran (2007) Research Interests: Smart structures and materials Nonlinear vibration dynamics Energy harvesting systems Sensors and actuators Biomedical devices Polymer composites and aerogel fabrication Awards and Honors: Mitacs Accelerate Fellowship (2021-2024) William Dunbar Memorial Scholarship (2019) Pierre Rivard Hydrogenics Graduate Fellowship (2018) Ranked First in Undergraduate Class (2011) Advising and Grants: Dr. Saadatnia has led research projects supported by grants from Mitacs and the Government of Canada. His work integrates interdisciplinary approaches to address challenges in energy systems, biomedical engineering, and material innovation. Labs and Teams: Collaborates with KITE Research Institute and University of Toronto teams on advanced materials and biomedical applications. His lab focuses on experimental and computational studies of energy harvesting and smart materials.

Yongjie Jessica ZhangView profile

Professor

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +5 more

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

Carl TropperView profile

Professor

• Parallel Simulation
• Distributed Systems
• VLSI Circuit Simulation
• +2 more

Carl Tropper is Professor Emeritus of Computer Science at McGill University, specializing in parallel discrete-event simulation. His research develops synchronization algorithms for large-scale systems including VLSI circuits, gravitational N-body simulations, and neuronal reaction-diffusion models. Education: BSc, McGill University PhD, Polytechnic Institute of New York Professional Experience: Former positions at Boeing, MITRE, and Caltech's Jet Propulsion Laboratory

• Global Navigation Satellite Systems (GNSS)
• Autonomous Systems
• Sensor Fusion
• +4 more

Patrick Henkel is a Professor at the Technical University of Munich (TUM) affiliated with the TUM School of Engineering and Design and the Chair of Communication and Navigation. He holds a professorship in Satellite Geodesy under Prof. Hugentobler. His research focuses on advanced positioning technologies, including Global Navigation Satellite Systems (GNSS), autonomous systems, and sensor fusion. He develops algorithms for precise positioning in challenging environments such as urban areas, alpine regions, and indoor spaces. His work also extends to environmental applications, such as snow hydrology and climate monitoring using GNSS signals. Henkel’s contributions include innovations in real-time kinematic (RTK) positioning, UAV navigation, and multi-sensor integration for robotics and autonomous vehicles. His research is supported by collaborations with industry and academic partners, addressing both theoretical and applied challenges in geodesy and navigation. Henkel leads projects on GNSS signal processing, satellite-based environmental monitoring, and autonomous driving technologies. He has contributed to the Galileo HAS service and developed methodologies for snow water equivalent estimation using multi-frequency GNSS signals. His expertise spans hardware-software co-design for navigation systems and algorithm optimization for high-precision positioning in dynamic environments. He actively publishes in top-tier journals and conferences, with a focus on advancing the reliability and accuracy of navigation systems across various domains. His advising and grants include funding for projects on sensor fusion, UAV-based measurements, and satellite receiver development. He collaborates with teams at TUM’s Navigation Lab and the Professur für Satellitengeodäsie, contributing to both academic and industrial applications. His work on low-bandwidth RTK dissemination and laser-tracker verified UAV positioning highlights his commitment to bridging theoretical advancements with real-world implementation.

Matthias IhmeView profile

Professor

• Combustion Science
• Fluid Dynamics
• Computational Fluid Dynamics
• +5 more

Matthias Ihme is a Professor in the Department of Mechanical Engineering and Photon Science Directorate at Stanford University. His research focuses on large-eddy simulation (LES) of turbulent reacting flows, aeroacoustics, combustion-generated noise, numerical methods, and high-order schemes. He holds a Ph.D. from Stanford University (2008), an M.Sc. in Computational Engineering from the University of Erlangen (Germany, 2002), and a Dipl.-Ing. in Mechanical Engineering from Munich University of Applied Sciences (Germany, 2000). His work bridges computational fluid dynamics, combustion science, and photon science, with notable contributions to supercritical fluid dynamics, machine learning integration in fluid simulations, and high-fidelity atmospheric transport modeling. Recent research emphasizes ultrafast cluster dynamics, shock-induced interface behavior, and stochastic ignition mechanisms in advanced fuel systems. Publications highlight interdisciplinary advancements, including physics-informed ML frameworks for reacting flows and experimental studies using X-ray photon correlation spectroscopy. His projects often involve high-performance computing and collaboration with national labs like SLAC.

Brian Y. ChenView profile

Associate Professor

• Bioinformatics
• Structural Biology
• Computational Biology
• +5 more

Dr. Brian Y. Chen is an Associate Professor and Doctoral Program Director in the Department of Computer Science & Engineering at Lehigh University. His research focuses on bioinformatics, structural biology, and machine learning applications in computational biology. He holds a Ph.D. in Computer Science from Rice University and B.A. degrees in Mathematics and Computer Science from Rutgers University. Dr. Chen's work emphasizes developing algorithms to analyze protein structures, protein-protein interactions, and ligand binding mechanisms. He has contributed to tools like DeepVASP-S and MechPPI, which explain molecular interactions and predict binding specificity. His recent projects include Alzheimer’s disease diagnosis using multimodal data and containerization frameworks for bioinformatics software. He previously served as a postdoctoral researcher in Barry Honig's Lab at Columbia University, where he contributed to the Center for Computational Biology and Bioinformatics. His research spans structural bioinformatics, computational methods for protein function prediction, and interdisciplinary applications in medicine and materials science. Key achievements include a nomination for Outstanding Mentorship (2017) and collaborative projects funded by the Army Research Lab and Lehigh University. His lab explores cutting-edge AI techniques for biomedical problems, including interpretable machine learning models and scalable bioinformatics pipelines.

Arian MalekiView profile

Associate Professor

• Compressed Sensing
• Signal Processing
• Statistical Inference
• +6 more

Arian Maleki is an Associate Professor in the Department of Statistics at Columbia University, affiliated with the Faculty of Arts and Sciences and the Foundations of Data Science Center. He holds a PhD from Stanford University (2010) and previously served as a postdoctoral scholar at Rice University. His research focuses on statistical inference, signal processing, and machine learning, with particular emphasis on compressed sensing, high-dimensional statistics, and algorithm design. Key areas include noise mitigation, image reconstruction, and the theoretical analysis of algorithms for inverse problems. Recent work highlights include studies on speckle noise challenges, phase transitions in compressed sensing, and certified data removal techniques. His contributions bridge theory and application, often addressing practical computational and statistical challenges in imaging and signal processing. No scientific awards are explicitly listed. Research outputs emphasize foundational advancements in statistical methodologies and algorithmic frameworks for high-dimensional data analysis. Labs/teams: Active involvement in the Foundations of Data Science Center at Columbia University.

Shahriar AfkhamiView profile

Researcher

• Materials Science
• Welding Engineering
• Additive Manufacturing
• +3 more

Shahriar Afkhami is a Researcher in the Department of Mechanical Engineering at LUT School of Energy Systems, LUT University. His research focuses on advanced materials science, additive manufacturing processes, and mechanical properties of high-strength steels and dissimilar joints. He specializes in fatigue analysis, welding technologies, and the optimization of structural components for industrial applications. His work integrates experimental methods with computational modeling to address challenges in material behavior under extreme conditions. Key research areas include: Welding of ultra-high strength steels and dissimilar materials Mechanical performance of additively manufactured components Fatigue life assessment of welded joints and cut edges Thermomechanical behavior of heat-affected zones Material characterization of laser powder bed fusion (LPBF) steels Publications highlight trends in additive manufacturing for industrial applications, particularly in optimizing 3D-printed metal structures and analyzing their mechanical integrity. His work on notch-load interactions and fatigue strength has advanced methodologies for predicting component failure under complex loading conditions. Notable contributions include the VERKOTA project exploring 3D printing networks for enhanced industrial adoption. No scientific awards are listed in the provided information. Afkhami's research has been supported by collaborative projects such as the VERKOTA initiative, though specific grants are not detailed here. He maintains an active presence on professional networks including LinkedIn and Google Scholar.

Mark McQuillingView profile

Associate Professor

• Experimental Fluid Mechanics
• Low Reynolds Number Flows
• Laminar-to-Turbulent Transition
• +4 more

Mark McQuilling is an Associate Professor of Aerospace and Mechanical Engineering at Saint Louis University's School of Science and Engineering. He holds a Ph.D. in Engineering from Wright State University, alongside M.S. and B.S. degrees in Mechanical Engineering from the University of Kentucky. His research focuses on experimental fluid mechanics, low Reynolds number flows, laminar-to-turbulent transition, airfoil design, unsteady aerodynamics (turbomachinery and airdrop systems), bio-fluid flows, and flow control. His work integrates advanced fluid dynamics techniques with practical applications in aerospace and biomedical engineering. Dr. McQuilling oversees the Fluid Systems Laboratory, which includes subsonic and supersonic wind tunnels, a water tunnel, and thermal system facilities. These labs support undergraduate and graduate research, with capabilities such as Laser Doppler Velocimetry, DPIV systems, and strain gauge balances. His thermal research spans micro-scale fluid phenomena to planetary atmospheric modeling, including studies on Uranus/Neptune vortex dynamics and airdrop parachute aerodynamics. Highlighted research areas include low-pressure turbine blade aerodynamics, parachute drag prediction, and bio-fluid studies like pharyngeal airflow analysis in sleep apnea patients. His peer-reviewed publications (over 20 entries) address topics ranging from micro-air vehicle wing design to thermal management in turbine blades. McQuilling is active in professional organizations like AIAA, ASME, and ASEE, and previously worked at the Air Force Research Laboratory. His email is mark.mcquilling@slu.edu.

Karsten UrbanView profile

Professor

• Numerical Mathematics
• Scientific Computing
• Partial Differential Equations
• +9 more

Prof. Karsten Urban is a Full Professor of Numerical Mathematics at the University of Ulm, leading the Institute for Numerical Mathematics. He holds roles such as Dean of Studies in Computational Science and Engineering (CSE) and Deputy Spokesman for the Research Association for Scientific Computing in Baden-Württemberg. He is an active member of prestigious societies including the Deutsche Mathematikervereinigung (DMV) and SIAM. His academic journey includes a PhD from RWTH Aachen (1995), Habilitation (2001), and a full professorship at Ulm since 2005. Research focuses on numerical methods for PDEs, reduced basis techniques, multiscale simulations in fluid mechanics, biomechanics, quantum sciences, and financial mathematics. He has pioneered wavelet-based methods and collaborated with industries on ship propulsion and energy trading models. His work integrates mathematical rigor with real-world applications, emphasizing model reduction and computational efficiency. Editorial Roles: Managing Editor of Advances in Computational Mathematics , Editor of SN Partial Differential Equations and Applications . Awards: Teaching award of Baden-Württemberg (2005), Science-Economy Cooperation Awards (2004, 2008). Administrative Roles: Member of the University Council and ASIIN expert committee. Supervises doctoral students in numerical analysis, quantum simulations, and biomechanics. Active in interdisciplinary projects, including quantum systems (IQST) and fracture healing modeling in collaboration with biomechanics experts. His contributions bridge academia and industry, driving innovation in computational methods.

Dan M. IonelView profile

Professor

• Alternative and Renewable Energy Technologies
• Electric Machines and Power Electronic Drives
• Electromagnetic Devices
• +3 more

Dan M. Ionel, Professor and inaugural L. Stanley Pigman Chair in Power at the University of Kentucky (UK), directs the Power and Energy Institute Kentucky (PEIK). He is affiliated with the SPARK Laboratory in the Department of Electrical and Computer Engineering, focusing on renewable energy, electric machines, and smart grids. Alternative and Renewable Energy Technologies Electric Machines and Power Electronic Drives Electromagnetic Devices Electric Power Systems Energy Storage Smart Grids and Buildings His research integrates machine learning with electromagnetic design for applications like electric vehicles and aircraft propulsion. Recent work includes axial flux permanent magnet machines with Halbach arrays and cryogenic thermal management systems. Scientific awards include IEEE Fellowship. Research is sponsored by NSF, DOE, NASA, and industry partners like ANSYS, EPRI, and Regal Rexnord. Collaborations span national labs (NREL, ORNL), utilities (LG&E, TVA), and aerospace entities.

Fahim KhanView profile

Assistant Professor

• Computer Graphics
• Data Visualization
• Computer Vision
• +5 more

Fahim Khan is an Assistant Professor in the Department of Computer Science and Software Engineering at California Polytechnic State University’s College of Engineering. He specializes in computer graphics, data visualization, computer vision, and machine learning, with a focus on applying these technologies to education, environmental monitoring, and public safety. His research emphasizes making complex data accessible through advanced tools, bridging the gap between raw data and actionable insights. He is deeply committed to inclusive education and fostering interdisciplinary collaboration. His work often integrates citizen science initiatives, empowering communities through mobile applications and machine learning. Notable projects include real-time rip current detection systems and platforms for high school students to engage in research. Khan advocates for equity in technology, designing inclusive learning environments and promoting diversity in STEM. He actively supports the university’s Learn by Doing philosophy, blending practical education with theoretical rigor. Professionally, he contributes to coastal observation networks and autonomous vehicle datasets while maintaining a balance through outdoor activities like exploring Pismo Beach. His research trends reflect a strong focus on mobile computing, environmental applications, and education technology, with recent efforts emphasizing citizen science and data-driven solutions. While no formal grants or advising records are detailed, his projects implicitly involve collaborative efforts. He is affiliated with labs focused on environmental monitoring and mobile technology development, though specific lab names are not mentioned.

• Computational Fluid Dynamics
• Turbomachinery
• Aerodynamics
• +4 more

Dr. Kidambi Sreenivas is an Associate Professor in Mechanical Engineering at the University of Tennessee at Chattanooga (UTC), affiliated with the College of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and specializes in computational fluid dynamics (CFD), with a focus on unstructured multi-physics flow solvers and applications in aerospace, environmental systems, and biomedical engineering. His research bridges academia and industry, collaborating with NASA, the U.S. Navy, Department of Energy, and private companies. Dr. Sreenivas' research interests include rotating machinery simulations, pre-conditioners for non-ideal fluids, and real-world applications such as submarine hydrodynamics, wind farm optimization, aerodynamic efficiency of vehicles, and contaminant dispersal modeling. He has pioneered methods for simulating complex geometries and physics, including high-fidelity simulations of hypersonic vehicles, weapons bay cavities, and shock-wave interactions. Recent work emphasizes advanced CFD methodologies for high-speed flows, thermal effects on turbulence, and aerothermal characteristics of hypersonic test articles. His collaborations have led to practical solutions for drag reduction on Class 8 trucks and improved accuracy in wind turbine modeling. Dr. Sreenivas also contributes to educational initiatives, such as developing PIV systems for undergraduate fluid mechanics labs. His advising and grants reflect partnerships with federal agencies and private sectors, focusing on projects like microplastic sampling devices for stormwater management. These projects highlight his interdisciplinary approach to solving real-world engineering challenges through cutting-edge computational methods.